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Advancing Diversity, Equity, and Inclusion in Hospital Medicine
Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)1 throughout nearly all aspects of academic medicine, including rank,2-4 tenure,5 authorship,6,7 funding opportunities,8,9 awards,10 speakership,11 leadership,12,13 and salaries.2,14,15
In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.
METHODS
Our Division’s Framework to DEI—“It Takes a Village”
Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues.
Needs Assessment
As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.
Interventions
TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.
Our institutional structures
Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to
Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks33 for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.
A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.
Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.32 Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.
Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.
Our People
The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.
Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.
Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.
Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.
Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations.
Measures
Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.
Analysis
Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.
RESULTS
Strategic Plan Development and Tracking
From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.
Compensation
One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.
Recruitment and Advancement
Toolkits32 and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.
Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.
We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.
Environment
We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.
Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.
We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.
DISCUSSION
The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.
We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.
By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.34 By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.
Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs35) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking33 as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.
Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.
We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:
- Instituting implicit bias training for all of our faculty
- Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
- Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
- Completion of a diversity dashboard to track our progress in all of these efforts over time
- Development of a more robust pipeline to promotion and leadership for our URM faculty
This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.
CONCLUSION
Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.
1. Underrepresented racial and ethnic groups. National Institutes of Health website. Accessed December 26, 2020. https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups
2. Ash AS, Carr PL, Goldstein R, Friedman RH. Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141(3):205-212. https://doi.org/10.7326/0003-4819-141-3-200408030-00009
3. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680
4. Fang D, Moy E, Colburn L, Hurley J. Racial and ethnic disparities in faculty promotion in academic medicine. JAMA. 2000;284(9):1085-1092. https://doi.org/10.1001/jama.284.9.1085
5. Baptiste D, Fecher AM, Dolejs SC, et al. Gender differences in academic surgery, work-life balance, and satisfaction. J Surg Res. 2017;218:99-107. https://doi.org/10.1016/j.jss.2017.05.075
6. Hart KL, Perlis RH. Trends in proportion of women as authors of medical journal articles, 2008-2018. JAMA Intern Med. 2019;179:1285-1287. https://doi.org/10.1001/jamainternmed.2019.0907
7. Thomas EG, Jayabalasingham B, Collins T, Geertzen J, Bui C, Dominici F. Gender disparities in invited commentary authorship in 2459 medical journals. JAMA Netw Open. 2019;2(10):e1913682. https://doi.org/10.1001/jamanetworkopen.2019.13682
8. Hechtman LA, Moore NP, Schulkey CE, et al. NIH funding longevity by gender. Proc Natl Acad Sci U S A. 2018;115(31):7943-7948. https://doi.org/10.1073/pnas.1800615115
9. Sege R, Nykiel-Bub L, Selk S. Sex differences in institutional support for junior biomedical researchers. JAMA. 2015;314(11):1175-1177. https://doi.org/10.1001/jama.2015.8517
10. Silver JK, Slocum CS, Bank AM, et al. Where are the women? The underrepresentation of women physicians among recognition award recipients from medical specialty societies. PM R. 2017;9(8):804-815. https://doi.org/10.1016/j.pmrj.2017.06.001
11. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the proportion of female speakers at medical conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
12. Carr PL, Raj A, Kaplan SE, Terrin N, Breeze JL, Freund KM. Gender differences in academic medicine: retention, rank, and leadership comparisons from the National Faculty Survey. Acad Med. 2018;93(11):1694-1699. https://doi.org/10.1097/ACM.0000000000002146
13. Carr PL, Gunn C, Raj A, Kaplan S, Freund KM. Recruitment, promotion, and retention of women in academic medicine: how institutions are addressing gender disparities. Womens Health Issues. 2017;27(3):374-381. https://doi.org/10.1016/j.whi.2016.11.003
14. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284
15. Lo Sasso AT, Richards MR, Chou CF, Gerber SE. The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30(2):193-201. https://doi.org/10.1377/hlthaff.2010.0597
16. Wachter RM, Goldman L. The emerging role of “hospitalists” in the American health care system. N Engl J Med. 1996;335(7):514-517. https://doi.org/10.1056/NEJM199608153350713
17. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400
18. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340
19. Northcutt N, Papp S, Keniston A, et al, Society of Hospital Medicine Diversity, Equity and Inclusion Special Interest Group. SPEAKers at the National Society of Hospital Medicine Meeting: a follow-up study of gender equity for conference speakers from 2015 to 2019. The SPEAK UP Study. J Hosp Med. 2020;15(4):228-231. https://doi.org/10.12788/jhm.3401
20. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247
21. Shah SS, Shaughnessy EE, Spector ND. Promoting gender equity at the Journal of Hospital Medicine [editorial]. J Hosp Med. 2020;15(9):517. https://doi.org/10.12788/jhm.3522
22. Sheehy AM, Kolehmainen C, Carnes M. We specialize in change leadership: a call for hospitalists to lead the quest for workforce gender equity [editorial]. J Hosp Med. 2015;10(8):551-552. https://doi.org/10.1002/jhm.2399
23. Evans MK, Rosenbaum L, Malina D, Morrissey S, Rubin EJ. Diagnosing and treating systemic racism [editorial]. N Engl J Med. 2020;383(3):274-276. https://doi.org/10.1056/NEJMe2021693
24. Rock D, Grant H. Why diverse teams are smarter. Harvard Business Review. Published November 4, 2016. Accessed July 24, 2019. https://hbr.org/2016/11/why-diverse-teams-are-smarter
25. Johnson RL, Saha S, Arbelaez JJ, Beach MC, Cooper LA. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med. 2004;19(2):101-110. https://doi.org/10.1111/j.1525-1497.2004.30262.x
26. Betancourt JR, Green AR, Carrillo JE, Park ER. Cultural competence and health care disparities: key perspectives and trends. Health Aff (Millwood). 2005;24(2):499-505. https://doi.org/10.1377/hlthaff.24.2.499
27. Acosta D, Ackerman-Barger K. Breaking the silence: time to talk about race and racism [comment]. Acad Med. 2017;92(3):285-288. https://doi.org/10.1097/ACM.0000000000001416
28. Cohen JJ, Gabriel BA, Terrell C. The case for diversity in the health care workforce. Health Aff (Millwood). 2002;21(5):90-102. https://doi.org/10.1377/hlthaff.21.5.90
29. Chang E, Simon M, Dong X. Integrating cultural humility into health care professional education and training. Adv Health Sci Educ Theory Pract. 2012;17(2):269-278. https://doi.org/10.1007/s10459-010-9264-1
30. Foronda C, Baptiste DL, Reinholdt MM, Ousman K. Cultural humility: a concept analysis. J Transcult Nurs. 2016;27(3):210-217. https://doi.org/10.1177/1043659615592677
31. Butkus R, Serchen J, Moyer DV, et al; Health and Public Policy Committee of the American College of Physicians. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168(10):721-723. https://doi.org/10.7326/M17-3438
32. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. GWIMS Equity Recruitment Toolkit. Accessed July 27, 2019. https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf
33. AAMC Faculty Salary Report. AAMC website. Accessed September 6, 2020. https://www.aamc.org/data-reports/workforce/report/aamc-faculty-salary-report
34. Promotion process. University of Colorado Anschutz Medical Campus website. Accessed September 7, 2020. https://medschool.cuanschutz.edu/faculty-affairs/for-faculty/promotion-and-tenure/promotion-process
35. Pierce RG, Diaz M, Kneeland P. Optimizing well-being, practice culture, and professional thriving in an era of turbulence. J Hosp Med. 2019;14(2):126-128. https://doi.org/10.12788/jhm.3101
Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)1 throughout nearly all aspects of academic medicine, including rank,2-4 tenure,5 authorship,6,7 funding opportunities,8,9 awards,10 speakership,11 leadership,12,13 and salaries.2,14,15
In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.
METHODS
Our Division’s Framework to DEI—“It Takes a Village”
Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues.
Needs Assessment
As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.
Interventions
TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.
Our institutional structures
Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to
Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks33 for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.
A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.
Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.32 Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.
Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.
Our People
The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.
Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.
Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.
Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.
Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations.
Measures
Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.
Analysis
Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.
RESULTS
Strategic Plan Development and Tracking
From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.
Compensation
One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.
Recruitment and Advancement
Toolkits32 and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.
Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.
We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.
Environment
We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.
Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.
We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.
DISCUSSION
The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.
We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.
By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.34 By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.
Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs35) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking33 as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.
Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.
We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:
- Instituting implicit bias training for all of our faculty
- Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
- Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
- Completion of a diversity dashboard to track our progress in all of these efforts over time
- Development of a more robust pipeline to promotion and leadership for our URM faculty
This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.
CONCLUSION
Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.
Studies continue to demonstrate persistent gaps in equity for women and underrepresented minorities (URMs)1 throughout nearly all aspects of academic medicine, including rank,2-4 tenure,5 authorship,6,7 funding opportunities,8,9 awards,10 speakership,11 leadership,12,13 and salaries.2,14,15
In this article, we report DEI efforts within our division, focusing on the development of our strategic plan and specific outcomes related to compensation, recruitment, and policies.
METHODS
Our Division’s Framework to DEI—“It Takes a Village”
Our Division of Hospital Medicine (DHM), previously within the Division of General Internal Medicine, was founded in October 2017. The DHM at the University of Colorado Hospital (UCH) is composed of 100 faculty members (70 physicians and 30 advanced-practice providers; 58% women and 42% men). In 2018, we implemented a stepwise approach to critically assess DEI within our group and to build a strategic plan to address the issues.
Needs Assessment
As a new division, we sought stakeholder feedback from division members. All faculty within the division were invited to attend a meeting in which issues related to DEI were discussed. A literature review that spanned both medical and nonmedical fields was also completed. Search terms included salary equity, gender equity, diverse teams, diversity recruitment and retention, diversifying leadership, and diverse speakers. Salaries, internally funded time, and other processes, such as recruitment, promotion, and hiring for leadership positions, were evaluated during the first year we became a division.
Interventions
TThrough this work, and with stakeholder engagement, we developed a divisional strategic plan to address DEI globally. Our strategic plan included developing a DEI director role to assist with overseeing DEI efforts. We have highlighted the various methods utilized for each component (Figure 1). This work occurred from October 2017 to December 2018.
Our institutional structures
Using best practices from both medical and nonmedical fields, we developed evidence-based approaches to
Compensation: transparent and consistent approaches based upon benchmarking with a framework of equal pay for equal work and similar advanced training/academic rank. In conjunction with efforts within the School of Medicine (SOM), Department of Medicine (DOM), and the UCH, our division sought to study salaries across DHM faculty members. We had an open call for faculty to participate in a newly developed DHM Compensation Committee, with the intent of rigorously examining our compensation practices and goals. Through faculty feedback and committee work, salary equity was defined as equal pay (ie, base salary for one clinical full-time equivalent [FTE]) for equal work based on academic rank and/or years of practice/advanced training. We also compared DHM salaries to regional academic hospital medicine groups and concluded that DHM salaries were lower than local and national benchmarks. This information was used to create a two-phase approach to increasing salaries for all individuals below the American Association of Medical Colleges (AAMC) benchmarks33 for academic hospitalists. We also developed a stipend system for external roles that came with additional compensation and roles within our own division that came with additional pay (ie, nocturnist). Phase 1 focused on those whose salaries were furthest away from and below benchmark, and phase 2 targeted all remaining individuals below benchmark.
A similar review of FTEs (based on required number of shifts for a full-time hospitalist) tied to our internal DHM leadership positions was completed by the division head and director of DEI. Specifically, the mission for each of our internally funded roles, job descriptions, and responsibilities was reviewed to ensure equity in funding.
Recruitment and advancement: processes to ensure equity and diversity in recruitment, tracking, and reporting, working to eliminate/mitigate bias. In collaboration with members of the AAMC Group on Women in Medicine and Science (GWIMS) and coauthors from various institutions, we developed toolkits and checklists aimed at achieving equity and diversity within candidate pools and on major committees, including, but not limited to, search and promotion committees.32 Additionally, a checklist was developed to help recruit more diverse speakers, including women and URMs, for local, regional, and national conferences.
Policies: evidence-based approaches, tracking and reporting, standardized approaches to eliminate/mitigate bias, embracing nontraditional paths. In partnership with our departmental efforts, members of our team led data collection and reporting for salary benchmarking, leadership roles, and committee membership. This included developing surveys and reporting templates that can be used to identify disparities and inform future efforts. We worked to ensure that we have faculty representing our field at the department and SOM levels. Specifically, we made sure to nominate division members during open calls for departmental and schoolwide committees, including the promotions committee.
Our People
The faculty and staff within our division have been instrumental in moving efforts forward in the following important areas.
Leadership: develop the position of director of DEI as well as leadership structures to support and increase DEI. One of the first steps in our strategic plan was creating a director of DEI leadership role (Appendix Figure 2). The director is responsible for researching, applying, and promoting a broad scope of DEI initiatives and best practices within the DHM, DOM, and SOM (in collaboration with their leaders), including recruitment, retention, and promotion of medical students, residents, and faculty; educational program development; health disparities research; and community-engaged scholarship.
Support: develop family leave policies/develop flexible work policies. Several members of our division worked on departmental committees and served in leadership roles on staff and faculty council. Estimated costs were assessed. Through collective efforts of department leadership and division head support, the department approved parental leave to employees following the birth of an employee’s child or the placement of a child with an employee in connection with adoption or permanent foster care.
Mentorship/sponsorship: enhance faculty advancement programs/develop pipeline and trainings/collaborate with student groups and organizations/invest in all of our people. Faculty across our divisional sites have held important roles in developing pipeline programs for undergraduate students bound for health professions, as well as programs developed specifically for medical students and internal medicine residents. This includes two programs, the CU Hospitalist Scholars Program (CUHSP) and Leadership Education for Aspiring Doctors (LEAD), in which undergraduate students have the opportunity to round with hospital medicine teams, work on quality-improvement projects, and receive extensive mentorship and advising from a diverse faculty team. Additionally, our faculty advancement team within the DHM has grown and been restructured to include more defined goals and to ensure each faculty member has at least one mentor in their area of interest.
Supportive: lactation space and support/diverse space options/inclusive and diverse environments. We worked closely with hospital leadership to advocate for adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations.
Measures
Our measures focused on (1) development and implementation of our DEI strategic plan, including new policies, processes, and practices related to key components of the DEI program; and (2) assessment of specific DEI programs, including pre-post salary data disparities based on rank and pre-post disparities for protected time for similar roles.
Analysis
Through rapid PDSA cycles, we evaluated salary equity, equity in leadership allotment, and committee membership. We have developed a tracking board to track progress of the multiple projects in the strategic plan.
RESULTS
Strategic Plan Development and Tracking
From October 2017 to December 2018, we developed a robust strategic plan and stepwise approach to DEI (Figure 1 and Figure 2). The director of DEI position was developed (see Appendix Figure 2 for job description) to help oversee these efforts. Figure 3 highlights the specific efforts and the progress made on implementation (ie, high-level dashboard or “tracking board”). While outcomes are still pending in the areas of recruitment and advancement and environment, we have made measurable improvements in compensation, as outlined in the following section.
Compensation
One year after the salary-equity interventions, all of our physician faculty’s salaries were at the goal benchmark (Table), and differences in salary for those in similar years of rank were nearly eliminated. Similarly, after implementing an internally consistent approach to assigning FTE for new and established positions within the division (ie, those that fall within the purview of the division), all faculty in similar types of roles had similar amounts of protected time.
Recruitment and Advancement
Toolkits32 and committee recommendations have been incorporated into division goals, though some aspects are still in implementation phases, as division-wide implicit bias training was delayed secondary to the COVID-19 pandemic. Key goals include: (1) implicit bias training for all members of major committees; (2) aiming for a goal of at least 40% representation of women and 40% URMs on committees; (3) having a diversity expert serve on each committee in order to identify and discuss any potential bias in the search and candidate-selection processes; and (4) careful tracking of diversity metrics in regard to diversity of candidates at each step of the interview and selection process.
Surveys and reporting templates for equity on committees and leadership positions have been developed and deployed. Data dashboards for our division have been developed as well (for compensation, leadership, and committee membership). A divisional dashboard to report recruitment efforts is in progress.
We have successfully nominated several faculty members to the SOM promotions committee and departmental committees during open calls for these positions. At the division level, we have also adapted internal policies to ensure promotion occurs on time and offers alternative pathways for faculty that may primarily focus on clinical pathways. All faculty who have gone up for promotion thus far have been successfully promoted in their desired pathway.
Environment
We successfully advocated and achieved adequately equipped lactation spaces, including equipment such as pumps, refrigerators, and computer workstations. This achievement was possible because of our hospital partners. Our efforts helped us acquire sufficient space and facilities such that nursing mothers can pump and still be able to answer phones, enter orders, and document visits.
Our team members conducted environmental scans and raised concerns when the environment was not inclusive, such as conference rooms with portraits of leadership that do not show diversity. The all-male pictures were removed from one frequently used departmental conference room, which will eventually house a diverse group of pictures and achievements.
We aim to eliminate bias by offering implicit bias training for our faculty. While this is presently required for those who serve on committees, in leadership positions, or those involved in recruitment and interviewing for the DOM, our goal is to eventually provide this training to all faculty and staff in the division. We have also incorporated DEI topics into our educational conferences for faculty, including sessions on recognizing bias in medicine, how to be an upstander/ally, and the impact of race and racism on medicine.
DISCUSSION
The important findings of this work are: (1) that successes in DEI can be achieved with strategic planning and stakeholder engagement; (2) through simple modification of processes, we can improve equity in compensation and FTE allotted to leadership; (3) though it takes time, diversity recruitment can be improved using sound, sustainable, evidence-based processes; (4) this work is time-intensive and challenging, requiring ongoing efforts to improve, modify, and enhance current efforts and future successes.
We have certainly made some progress with DEI initiatives within our division and have also learned a great deal from this experience. First, change is difficult for all parties involved, including those leading change and those affected by the changes. We purposely made an effort to facilitate discussions with all of the DHM faculty and staff to ensure that everyone felt included in this work and that everyone’s voice was heard. This was exemplified by inviting all faculty members to a feedback session in which we discussed DEI within our division and areas that we wanted to improve on. Early on, we were able to define what diversity, equity, and inclusion meant to us as a division and then use these definitions to develop tangible goals for all the areas of highest importance to the group.
By increasing faculty presence on key committees, such as the promotions committee, we now have faculty members who are well versed in promotions processes. We are fortunate to have a promotions process that supports faculty advancement for faculty with diverse interests that spans from supporting highly clinical faculty, clinician educators, as well as more traditional researchers.34 By having hospitalists serve in these roles, we help to add to the diverse perspectives on these committees, including emphasizing the scholarship that is associated with quality improvement, as well as DEI efforts which can often be viewed as service as opposed to scholarship.
Clear communication and transparency were key to all of our DEI initiatives. We had monthly updates on our DEI efforts during business meetings and also held impromptu meetings (also known as flash mobs35) to answer questions and discuss concerns in real time. As with all DEI work, it is important to know where you are starting (having accurate data and a clear understanding of the data) and be able to communicate that data to the group. For example, using AAMC salary benchmarking33 as well as other benchmarks allowed us to accurately calculate variance among salaries and identify the appropriate goal salary for each of our faculty members. Likewise, by completing an in-depth inventory on the work being done by all of our faculty in leadership roles, we were able to standardize the compensation/FTE for each of these roles. Tracking these changes over time, via the use of dashboards in our case, allows for real-time measurements and accountability for all of those involved. Our end goal will be to have all of these initiatives feed into one large dashboard.
Collaborating with leadership and stakeholders in the DOM, SOM, and hospital helped to make our DEI initiatives successful. Much too often, we work in silos when it comes to DEI work. However, we tend to have similar goals and can achieve much more if we work together. Collaboration with multiple stakeholders allowed for wider dissemination and resulted in a larger impact to the campus and community at large. This has been exemplified by the committee composition guidance that has been utilized by the DOM, as well as implementation of campus-wide policies, specifically the parental leave policy, which our faculty members played an important role in creating. Likewise, it is important to look outside of our institutions and work with other hospital medicine groups around the country who are interested in promoting DEI.
We still have much work ahead of us. We are continuing to measure outcomes status postimplementation of the toolkit and checklists being used for diversity recruitment and committee composition. Additionally, we are actively working on several initiatives, including:
- Instituting implicit bias training for all of our faculty
- Partnering with national leaders and our hospital systems to develop zero-tolerance policies regarding abusive behaviors (verbal, physical, and other), racism, and sexism in the hospital and other work settings
- Development of specific recruitment strategies as a means of diversifying our healthcare workforce (of note, based on a 2020 survey of our faculty, in which there was a 70% response rate, 8.5% of our faculty identified as URMs)
- Completion of a diversity dashboard to track our progress in all of these efforts over time
- Development of a more robust pipeline to promotion and leadership for our URM faculty
This study has several strengths. Many of the plans and strategies described here can be used to guide others interested in implementing this work. Figure 2 provides a stepwise
approach to addressing DEI in hospital medicine groups and divisions. We conducted this work at a large academic medical center, and while it may not be generalizable, it does offer some ideas for others to consider in their own work to advance DEI at their institutions. There are also several limitations to this work. Eliminating salary inequities with our approach did take resources. We took advantage of already lower salaries and the need to increase salaries closer to benchmark and paired this effort with our DEI efforts to achieve salary equity. This required partnerships with the department and hospital. Efforts to advance DEI also take a lot of time and effort, and thus commitment from the division, department, and institution as a whole is key. While we have outcomes for our efforts related to salary equity, recruitment efforts should be realized over time, as currently it is too early to tell. We have highlighted the efforts that have been put in place at this time.
CONCLUSION
Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is still ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.
1. Underrepresented racial and ethnic groups. National Institutes of Health website. Accessed December 26, 2020. https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups
2. Ash AS, Carr PL, Goldstein R, Friedman RH. Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141(3):205-212. https://doi.org/10.7326/0003-4819-141-3-200408030-00009
3. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680
4. Fang D, Moy E, Colburn L, Hurley J. Racial and ethnic disparities in faculty promotion in academic medicine. JAMA. 2000;284(9):1085-1092. https://doi.org/10.1001/jama.284.9.1085
5. Baptiste D, Fecher AM, Dolejs SC, et al. Gender differences in academic surgery, work-life balance, and satisfaction. J Surg Res. 2017;218:99-107. https://doi.org/10.1016/j.jss.2017.05.075
6. Hart KL, Perlis RH. Trends in proportion of women as authors of medical journal articles, 2008-2018. JAMA Intern Med. 2019;179:1285-1287. https://doi.org/10.1001/jamainternmed.2019.0907
7. Thomas EG, Jayabalasingham B, Collins T, Geertzen J, Bui C, Dominici F. Gender disparities in invited commentary authorship in 2459 medical journals. JAMA Netw Open. 2019;2(10):e1913682. https://doi.org/10.1001/jamanetworkopen.2019.13682
8. Hechtman LA, Moore NP, Schulkey CE, et al. NIH funding longevity by gender. Proc Natl Acad Sci U S A. 2018;115(31):7943-7948. https://doi.org/10.1073/pnas.1800615115
9. Sege R, Nykiel-Bub L, Selk S. Sex differences in institutional support for junior biomedical researchers. JAMA. 2015;314(11):1175-1177. https://doi.org/10.1001/jama.2015.8517
10. Silver JK, Slocum CS, Bank AM, et al. Where are the women? The underrepresentation of women physicians among recognition award recipients from medical specialty societies. PM R. 2017;9(8):804-815. https://doi.org/10.1016/j.pmrj.2017.06.001
11. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the proportion of female speakers at medical conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
12. Carr PL, Raj A, Kaplan SE, Terrin N, Breeze JL, Freund KM. Gender differences in academic medicine: retention, rank, and leadership comparisons from the National Faculty Survey. Acad Med. 2018;93(11):1694-1699. https://doi.org/10.1097/ACM.0000000000002146
13. Carr PL, Gunn C, Raj A, Kaplan S, Freund KM. Recruitment, promotion, and retention of women in academic medicine: how institutions are addressing gender disparities. Womens Health Issues. 2017;27(3):374-381. https://doi.org/10.1016/j.whi.2016.11.003
14. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284
15. Lo Sasso AT, Richards MR, Chou CF, Gerber SE. The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30(2):193-201. https://doi.org/10.1377/hlthaff.2010.0597
16. Wachter RM, Goldman L. The emerging role of “hospitalists” in the American health care system. N Engl J Med. 1996;335(7):514-517. https://doi.org/10.1056/NEJM199608153350713
17. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400
18. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340
19. Northcutt N, Papp S, Keniston A, et al, Society of Hospital Medicine Diversity, Equity and Inclusion Special Interest Group. SPEAKers at the National Society of Hospital Medicine Meeting: a follow-up study of gender equity for conference speakers from 2015 to 2019. The SPEAK UP Study. J Hosp Med. 2020;15(4):228-231. https://doi.org/10.12788/jhm.3401
20. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247
21. Shah SS, Shaughnessy EE, Spector ND. Promoting gender equity at the Journal of Hospital Medicine [editorial]. J Hosp Med. 2020;15(9):517. https://doi.org/10.12788/jhm.3522
22. Sheehy AM, Kolehmainen C, Carnes M. We specialize in change leadership: a call for hospitalists to lead the quest for workforce gender equity [editorial]. J Hosp Med. 2015;10(8):551-552. https://doi.org/10.1002/jhm.2399
23. Evans MK, Rosenbaum L, Malina D, Morrissey S, Rubin EJ. Diagnosing and treating systemic racism [editorial]. N Engl J Med. 2020;383(3):274-276. https://doi.org/10.1056/NEJMe2021693
24. Rock D, Grant H. Why diverse teams are smarter. Harvard Business Review. Published November 4, 2016. Accessed July 24, 2019. https://hbr.org/2016/11/why-diverse-teams-are-smarter
25. Johnson RL, Saha S, Arbelaez JJ, Beach MC, Cooper LA. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med. 2004;19(2):101-110. https://doi.org/10.1111/j.1525-1497.2004.30262.x
26. Betancourt JR, Green AR, Carrillo JE, Park ER. Cultural competence and health care disparities: key perspectives and trends. Health Aff (Millwood). 2005;24(2):499-505. https://doi.org/10.1377/hlthaff.24.2.499
27. Acosta D, Ackerman-Barger K. Breaking the silence: time to talk about race and racism [comment]. Acad Med. 2017;92(3):285-288. https://doi.org/10.1097/ACM.0000000000001416
28. Cohen JJ, Gabriel BA, Terrell C. The case for diversity in the health care workforce. Health Aff (Millwood). 2002;21(5):90-102. https://doi.org/10.1377/hlthaff.21.5.90
29. Chang E, Simon M, Dong X. Integrating cultural humility into health care professional education and training. Adv Health Sci Educ Theory Pract. 2012;17(2):269-278. https://doi.org/10.1007/s10459-010-9264-1
30. Foronda C, Baptiste DL, Reinholdt MM, Ousman K. Cultural humility: a concept analysis. J Transcult Nurs. 2016;27(3):210-217. https://doi.org/10.1177/1043659615592677
31. Butkus R, Serchen J, Moyer DV, et al; Health and Public Policy Committee of the American College of Physicians. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168(10):721-723. https://doi.org/10.7326/M17-3438
32. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. GWIMS Equity Recruitment Toolkit. Accessed July 27, 2019. https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf
33. AAMC Faculty Salary Report. AAMC website. Accessed September 6, 2020. https://www.aamc.org/data-reports/workforce/report/aamc-faculty-salary-report
34. Promotion process. University of Colorado Anschutz Medical Campus website. Accessed September 7, 2020. https://medschool.cuanschutz.edu/faculty-affairs/for-faculty/promotion-and-tenure/promotion-process
35. Pierce RG, Diaz M, Kneeland P. Optimizing well-being, practice culture, and professional thriving in an era of turbulence. J Hosp Med. 2019;14(2):126-128. https://doi.org/10.12788/jhm.3101
1. Underrepresented racial and ethnic groups. National Institutes of Health website. Accessed December 26, 2020. https://extramural-diversity.nih.gov/diversity-matters/underrepresented-groups
2. Ash AS, Carr PL, Goldstein R, Friedman RH. Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141(3):205-212. https://doi.org/10.7326/0003-4819-141-3-200408030-00009
3. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680
4. Fang D, Moy E, Colburn L, Hurley J. Racial and ethnic disparities in faculty promotion in academic medicine. JAMA. 2000;284(9):1085-1092. https://doi.org/10.1001/jama.284.9.1085
5. Baptiste D, Fecher AM, Dolejs SC, et al. Gender differences in academic surgery, work-life balance, and satisfaction. J Surg Res. 2017;218:99-107. https://doi.org/10.1016/j.jss.2017.05.075
6. Hart KL, Perlis RH. Trends in proportion of women as authors of medical journal articles, 2008-2018. JAMA Intern Med. 2019;179:1285-1287. https://doi.org/10.1001/jamainternmed.2019.0907
7. Thomas EG, Jayabalasingham B, Collins T, Geertzen J, Bui C, Dominici F. Gender disparities in invited commentary authorship in 2459 medical journals. JAMA Netw Open. 2019;2(10):e1913682. https://doi.org/10.1001/jamanetworkopen.2019.13682
8. Hechtman LA, Moore NP, Schulkey CE, et al. NIH funding longevity by gender. Proc Natl Acad Sci U S A. 2018;115(31):7943-7948. https://doi.org/10.1073/pnas.1800615115
9. Sege R, Nykiel-Bub L, Selk S. Sex differences in institutional support for junior biomedical researchers. JAMA. 2015;314(11):1175-1177. https://doi.org/10.1001/jama.2015.8517
10. Silver JK, Slocum CS, Bank AM, et al. Where are the women? The underrepresentation of women physicians among recognition award recipients from medical specialty societies. PM R. 2017;9(8):804-815. https://doi.org/10.1016/j.pmrj.2017.06.001
11. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the proportion of female speakers at medical conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
12. Carr PL, Raj A, Kaplan SE, Terrin N, Breeze JL, Freund KM. Gender differences in academic medicine: retention, rank, and leadership comparisons from the National Faculty Survey. Acad Med. 2018;93(11):1694-1699. https://doi.org/10.1097/ACM.0000000000002146
13. Carr PL, Gunn C, Raj A, Kaplan S, Freund KM. Recruitment, promotion, and retention of women in academic medicine: how institutions are addressing gender disparities. Womens Health Issues. 2017;27(3):374-381. https://doi.org/10.1016/j.whi.2016.11.003
14. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284
15. Lo Sasso AT, Richards MR, Chou CF, Gerber SE. The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30(2):193-201. https://doi.org/10.1377/hlthaff.2010.0597
16. Wachter RM, Goldman L. The emerging role of “hospitalists” in the American health care system. N Engl J Med. 1996;335(7):514-517. https://doi.org/10.1056/NEJM199608153350713
17. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400
18. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340
19. Northcutt N, Papp S, Keniston A, et al, Society of Hospital Medicine Diversity, Equity and Inclusion Special Interest Group. SPEAKers at the National Society of Hospital Medicine Meeting: a follow-up study of gender equity for conference speakers from 2015 to 2019. The SPEAK UP Study. J Hosp Med. 2020;15(4):228-231. https://doi.org/10.12788/jhm.3401
20. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247
21. Shah SS, Shaughnessy EE, Spector ND. Promoting gender equity at the Journal of Hospital Medicine [editorial]. J Hosp Med. 2020;15(9):517. https://doi.org/10.12788/jhm.3522
22. Sheehy AM, Kolehmainen C, Carnes M. We specialize in change leadership: a call for hospitalists to lead the quest for workforce gender equity [editorial]. J Hosp Med. 2015;10(8):551-552. https://doi.org/10.1002/jhm.2399
23. Evans MK, Rosenbaum L, Malina D, Morrissey S, Rubin EJ. Diagnosing and treating systemic racism [editorial]. N Engl J Med. 2020;383(3):274-276. https://doi.org/10.1056/NEJMe2021693
24. Rock D, Grant H. Why diverse teams are smarter. Harvard Business Review. Published November 4, 2016. Accessed July 24, 2019. https://hbr.org/2016/11/why-diverse-teams-are-smarter
25. Johnson RL, Saha S, Arbelaez JJ, Beach MC, Cooper LA. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med. 2004;19(2):101-110. https://doi.org/10.1111/j.1525-1497.2004.30262.x
26. Betancourt JR, Green AR, Carrillo JE, Park ER. Cultural competence and health care disparities: key perspectives and trends. Health Aff (Millwood). 2005;24(2):499-505. https://doi.org/10.1377/hlthaff.24.2.499
27. Acosta D, Ackerman-Barger K. Breaking the silence: time to talk about race and racism [comment]. Acad Med. 2017;92(3):285-288. https://doi.org/10.1097/ACM.0000000000001416
28. Cohen JJ, Gabriel BA, Terrell C. The case for diversity in the health care workforce. Health Aff (Millwood). 2002;21(5):90-102. https://doi.org/10.1377/hlthaff.21.5.90
29. Chang E, Simon M, Dong X. Integrating cultural humility into health care professional education and training. Adv Health Sci Educ Theory Pract. 2012;17(2):269-278. https://doi.org/10.1007/s10459-010-9264-1
30. Foronda C, Baptiste DL, Reinholdt MM, Ousman K. Cultural humility: a concept analysis. J Transcult Nurs. 2016;27(3):210-217. https://doi.org/10.1177/1043659615592677
31. Butkus R, Serchen J, Moyer DV, et al; Health and Public Policy Committee of the American College of Physicians. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168(10):721-723. https://doi.org/10.7326/M17-3438
32. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. GWIMS Equity Recruitment Toolkit. Accessed July 27, 2019. https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf
33. AAMC Faculty Salary Report. AAMC website. Accessed September 6, 2020. https://www.aamc.org/data-reports/workforce/report/aamc-faculty-salary-report
34. Promotion process. University of Colorado Anschutz Medical Campus website. Accessed September 7, 2020. https://medschool.cuanschutz.edu/faculty-affairs/for-faculty/promotion-and-tenure/promotion-process
35. Pierce RG, Diaz M, Kneeland P. Optimizing well-being, practice culture, and professional thriving in an era of turbulence. J Hosp Med. 2019;14(2):126-128. https://doi.org/10.12788/jhm.3101
© 2021 Society of Hospital Medicine
Sexual Harassment and Gender Discrimination in Hospital Medicine: A Call to Action
Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.1 Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.
The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.1
Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.3
The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?4 These questions and a host of others merit our attention.
Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.2 This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.5 If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.
1. Bhandari S, Jha P, Cooper C, Slawski B. Gender-based discrimination and sexual harassment among academic internal medicine hospitalists. J Hosp Med. 2021;16:XXX-XXX. https://doi.org/10.12788/jhm.3561
2. National Academies of Sciences, Engineering, and Medicine. Sexual harassment of women: climate, culture, and consequences in academic sciences, engineering, and medicine. National Academies Press; 2018. https://doi.org/10.17226/24994
3. Stentz NC, Griffith KA, Perkins E, Jones RD, Jagsi R. Fertility and childbearing among American female physicians. J Womens Health (Larchmt). 2016;25(10):1059-1065. https://doi.org/10.1089/jwh.2015.5638
4. Leiter MP, Laschinger HKS, Day A, Oore DG. The impact of civility interventions on employee social behavior, distress, and attitudes. J Appl Psychol. 2011;96(6):1258-1274. https://doi.org/10.1037/a0024442
5. Mello MM, Jagsi R. Standing up against gender bias and harassment - a matter of professional ethics. N Engl J Med. 2020;382(15):1385-1387. https://doi.org/10.1056/nejmp1915351
Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.1 Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.
The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.1
Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.3
The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?4 These questions and a host of others merit our attention.
Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.2 This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.5 If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.
Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.1 Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.
The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.1
Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.3
The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?4 These questions and a host of others merit our attention.
Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.2 This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.5 If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.
1. Bhandari S, Jha P, Cooper C, Slawski B. Gender-based discrimination and sexual harassment among academic internal medicine hospitalists. J Hosp Med. 2021;16:XXX-XXX. https://doi.org/10.12788/jhm.3561
2. National Academies of Sciences, Engineering, and Medicine. Sexual harassment of women: climate, culture, and consequences in academic sciences, engineering, and medicine. National Academies Press; 2018. https://doi.org/10.17226/24994
3. Stentz NC, Griffith KA, Perkins E, Jones RD, Jagsi R. Fertility and childbearing among American female physicians. J Womens Health (Larchmt). 2016;25(10):1059-1065. https://doi.org/10.1089/jwh.2015.5638
4. Leiter MP, Laschinger HKS, Day A, Oore DG. The impact of civility interventions on employee social behavior, distress, and attitudes. J Appl Psychol. 2011;96(6):1258-1274. https://doi.org/10.1037/a0024442
5. Mello MM, Jagsi R. Standing up against gender bias and harassment - a matter of professional ethics. N Engl J Med. 2020;382(15):1385-1387. https://doi.org/10.1056/nejmp1915351
1. Bhandari S, Jha P, Cooper C, Slawski B. Gender-based discrimination and sexual harassment among academic internal medicine hospitalists. J Hosp Med. 2021;16:XXX-XXX. https://doi.org/10.12788/jhm.3561
2. National Academies of Sciences, Engineering, and Medicine. Sexual harassment of women: climate, culture, and consequences in academic sciences, engineering, and medicine. National Academies Press; 2018. https://doi.org/10.17226/24994
3. Stentz NC, Griffith KA, Perkins E, Jones RD, Jagsi R. Fertility and childbearing among American female physicians. J Womens Health (Larchmt). 2016;25(10):1059-1065. https://doi.org/10.1089/jwh.2015.5638
4. Leiter MP, Laschinger HKS, Day A, Oore DG. The impact of civility interventions on employee social behavior, distress, and attitudes. J Appl Psychol. 2011;96(6):1258-1274. https://doi.org/10.1037/a0024442
5. Mello MM, Jagsi R. Standing up against gender bias and harassment - a matter of professional ethics. N Engl J Med. 2020;382(15):1385-1387. https://doi.org/10.1056/nejmp1915351
© 2021 Society of Hospital Medicine
Developing a Patient- and Family-Centered Research Agenda for Hospital Medicine: The Improving Hospital Outcomes through Patient Engagement (i-HOPE) Study
Thirty-six million people are hospitalized annually in the United States,1 and a significant proportion of these patients are rehospitalized within 30 days.2 Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.
Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.10-13
The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.14 The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-partnered research agenda for improving the care of hospitalized adult patients.
METHODS
Guiding Frameworks for Study Methods
We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:
- The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,18 which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.18
- The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.19
The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.20 Our process is described below and summarized in Table 1.
Phases of Question Development
Phase 1: Steering Committee Formation
Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.21 Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.
Phase 2: Stakeholder Identification
We created a list of potential stakeholder organizations to participate in the study based on the following:
- Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
- Organizations with which steering committee members had worked
- Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
- Suggestions from stakeholders identified through the first two approaches as described above
We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.
Phase 3: Stakeholder Engagement and Awareness Training
Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.
Phase 4: Survey Development and Administration
We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.
We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.19 We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).
Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.22 Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.
Phase 5: Initial Question Categorization Using Qualitative Content Analysis
Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.23,24 This analytic approach identifies, analyzes, and reports patterns within the data.23,24 We hypothesized that some of the submitted questions would relate to already-known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,25 personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.23
Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.23 All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).
While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.26 Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).
Phase 6: Initial Question Identification Using Quantitative Content Analysis
Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.27 A question was determined to be a commonly submitted question when it appeared at least 10 times.
Phase 7: Interim Priority Setting
We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.
Phase 8: In-person Meeting for Final Question Prioritization and Refinement
Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.28 NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.28 The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.
Ethical Oversight
This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).
RESULTS
In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.
An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).
From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories23 created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach23 to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.
DISCUSSION
Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.
The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,29 a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.30 While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.31 This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.
Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,32 may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.
Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.
Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.33 Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.34 We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.
Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.
The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.
In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.
Acknowledgments
The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.
Disclaimer
The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.
1. American Hospital Association. 2019 American Hospital Association Hospital Statistics. Chicago, Illinois: American Hospital Association; 2019.
2. Alper E, O’Malley T, Greenwald J. UptoDate: Hospital discharge and readmission. https://www.uptodate.com/contents/hospital-discharge-and-readmission. Accessed August 8, 2019.
3. de Vries EN, Ramrattan MA, Smorenburg SM, Gouma DJ, Boermeester MA. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Heal Care. 2008;17(3):216-223. https://doi.org/10.1136/qshc.2007.023622.
4. Agency for Healthcare Research and Quality. Readmissions and Adverse Events After Discharge. https://psnet.ahrq.gov/primers/primer/11/Readmissions-and-Adverse-Events-After-Discharge. Accessed August 8, 2019.
5. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC; National Academies Press; 2001. https://doi.org/10.17226/10027.
6. Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and equity of care in U.S. hospitals. N Engl J Med. 2014;371(24):2298-2308. https://doi.org/10.1056/NEJMsa1405003.
7. National Patient Safety Foundation. Free from Harm: Accelerating Patient Safety Improvement Fifteen Years after To Err Is Human. Boston: National Patient Safety Foundation; 2015.
8. Agency for Healthcare Research and Quality. AHRQ National Scorecard on Hospital-Acquired Conditions Updated Baseline Rates and Preliminary Results 2014–2017. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-safety/pfp/hacreport-2019.pdf. Accessed August 8, 2019.
9. Hansen LO, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8(8):421-427. https://doi.org/10.1002/jhm.2054.
10. Snyder HJ, Fletcher KE. The hospital experience through the patients’ eyes. J Patient Exp. 2019. https://doi.org/10.1177/2374373519843056.
11. Kebede S, Shihab HM, Berger ZD, Shah NG, Yeh H-C, Brotman DJ. Patients’ understanding of their hospitalizations and association with satisfaction. JAMA Intern Med. 2014;174(10):1698-1700. https://doi.org/10.1001/jamainternmed.2014.3765.
12. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. https://doi.org/10.1002/jhm.1937.
13. Neeman N, Quinn K, Shoeb M, Mourad M, Sehgal NL, Sliwka D. Postdischarge focus groups to improve the hospital experience. Am J Med Qual. 2013;28(6):536-538. https://doi.org/10.1177/1062860613488623.
14. Duffett L. Patient engagement: what partnering with patients in research is all about. Thromb Res. 2017;150:113-120. https://doi.org/10.1016/j.thromres.2016.10.029.
15. Pomey M, Hihat H, Khalifa M, Lebel P, Neron A, Dumez V. Patient partnership in quality improvement of healthcare services: patients’ inputs and challenges faced. Patient Exp J. 2015;2:29-42. https://doi.org/10.35680/2372-0247.1064.
16. Robbins M, Tufte J, Hsu C. Learning to “swim” with the experts: experiences of two patient co-investigators for a project funded by the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):85-88. https://doi.org/10.7812/TPP/15-162.
17. Tai-Seale M, Sullivan G, Cheney A, Thomas K, Frosch D. The language of engagement: “aha!” moments from engaging patients and community partners in two pilot projects of the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):89-92. https://doi.org/10.7812/TPP/15-123.
18. Patient-Centered Outcomes Research Institute (PCORI). PCORI Methodology Standards: Standards for Formulating Research Questions. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Formulating Research Questions. Accessed August 8, 2019.
19. James Lind Alliance. The James Lind Alliance Guidebook. Version 8. Southampton, England: James Lind Alliance; 2018.
20. Society of Hospital Medicine (SHM). Improving Hospital Outcomes through Patient Engagement: The i-HOPE Study. https://www.hospitalmedicine.org/clinical-topics/i-hope-study/. Accessed August 8, 2019.
21. Society of Hospital Medicine (SHM). Committees. https://www.hospitalmedicine.org/membership/committees/. Accessed August 8, 2019.
22. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research Electronic Data Capture (REDCap) - a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
23. Schreier M. Qualitative content analysis in practice. Los Angeles, CA: SAGE Publications; 2012.
24. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-115. https://doi.org/10.1111/j.1365-2648.2007.04569.x.
25. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine—2017 revision: introduction and methodology. J Hosp Med. 2017;12(4):283-287. https://doi.org/10.12788/jhm.2715.
26. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Serv Res. 2007;42(4):1758-1772. https://doi.org/10.1111/j.1475-6773.2006.00684.x.
27. Coe K, Scacco JM. Content analysis, quantitative. Int Encycl Commun Res Methods. 2017:1-11. https://doi.org/10.1002/9781118901731.iecrm0045.
28. Centers for Disease Control and Prevention. Evaluation Briefs: Gaining Consensus Among Stakeholders Through the Nominal Group Technique. Atlanta, GA; 2018. https://www.cdc.gov/healthyyouth/evaluation/pdf/brief7.pdf. Accessed August 8, 2019.
29. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. 1997;44(5):681-692. https://doi.org/10.1016/s0277-9536(96)00221-3.
30. Blankenburg R, Hilton JF, Yuan P, et al. Shared decision-making during inpatient rounds: opportunities for improvement in patient engagement and communication. J Hosp Med. 2018;13(7):453-461. https://doi.org/10.12788/jhm.2909.
31. Legare F, Adekpedjou R, Stacey D, et al. Interventions for increasing the use of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2018;7(7):CD006732. https://doi.org/10.1002/14651858.CD006732.pub4.
32. Roberts JP, Fisher TR, Trowbridge MJ, Bent C. A design thinking framework for healthcare management and innovation. Healthc (Amst). 2016;4(1):11-14. https://doi.org/10.1016/j.hjdsi.2015.12.002.
33. Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA. 2012;307(15):1583-1584. https://doi.org/10.1001/jama.2012.500.
34. Harrison J, Auerbach A, Anderson W, et al. Patient stakeholder engagement in research: a narrative review to describe foundational principles and best practice activities. Health Expect. 2019;22(3):307-316. https://doi.org/10.1111/hex.12873.
Thirty-six million people are hospitalized annually in the United States,1 and a significant proportion of these patients are rehospitalized within 30 days.2 Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.
Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.10-13
The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.14 The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-partnered research agenda for improving the care of hospitalized adult patients.
METHODS
Guiding Frameworks for Study Methods
We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:
- The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,18 which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.18
- The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.19
The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.20 Our process is described below and summarized in Table 1.
Phases of Question Development
Phase 1: Steering Committee Formation
Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.21 Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.
Phase 2: Stakeholder Identification
We created a list of potential stakeholder organizations to participate in the study based on the following:
- Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
- Organizations with which steering committee members had worked
- Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
- Suggestions from stakeholders identified through the first two approaches as described above
We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.
Phase 3: Stakeholder Engagement and Awareness Training
Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.
Phase 4: Survey Development and Administration
We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.
We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.19 We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).
Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.22 Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.
Phase 5: Initial Question Categorization Using Qualitative Content Analysis
Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.23,24 This analytic approach identifies, analyzes, and reports patterns within the data.23,24 We hypothesized that some of the submitted questions would relate to already-known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,25 personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.23
Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.23 All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).
While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.26 Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).
Phase 6: Initial Question Identification Using Quantitative Content Analysis
Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.27 A question was determined to be a commonly submitted question when it appeared at least 10 times.
Phase 7: Interim Priority Setting
We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.
Phase 8: In-person Meeting for Final Question Prioritization and Refinement
Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.28 NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.28 The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.
Ethical Oversight
This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).
RESULTS
In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.
An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).
From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories23 created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach23 to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.
DISCUSSION
Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.
The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,29 a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.30 While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.31 This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.
Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,32 may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.
Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.
Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.33 Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.34 We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.
Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.
The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.
In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.
Acknowledgments
The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.
Disclaimer
The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.
Thirty-six million people are hospitalized annually in the United States,1 and a significant proportion of these patients are rehospitalized within 30 days.2 Gaps in hospital care are many and well documented, including high rates of adverse events, hospital-acquired conditions, and suboptimal care transitions.3-5 Despite significant efforts to improve the care of hospitalized patients and some incremental improvement in the safety of hospital care, hospital care remains suboptimal.6-9 Importantly, hospitalization remains a challenging and vulnerable time for patients and caregivers.
Despite research efforts to improve hospital care, there remains very little data regarding what patients, caregivers, and other stakeholders believe are the most important priorities for improving hospital care, experiences, and outcomes. Small studies described in brief reports provide limited insights into what aspects of hospital care are most important to patients and to their families.10-13 These small studies suggest that communication and the comfort of caregivers and of patient family members are important priorities, as are the provision of adequate sleeping arrangements, food choices, and psychosocial support. However, the limited nature of these studies precludes the possibility of larger conclusions regarding patient priorities.10-13
The evolution of patient-centered care has led to increasing efforts to engage, and partner, with patients, caregivers, and other stakeholders to obtain their input on healthcare, research, and improvement efforts.14 The guiding principle of this engagement is that patients and their caregivers are uniquely positioned to share their lived experiences of care and that their involvement ensures their voices are represented.15-17 Therefore to obtain greater insight into priority areas from the perspectives of patients, caregivers, and other healthcare stakeholders, we undertook a systematic engagement process to create a patient-partnered and stakeholder-partnered research agenda for improving the care of hospitalized adult patients.
METHODS
Guiding Frameworks for Study Methods
We used two established, validated methods to guide our collaborative, inclusive, and consultative approach to patient and stakeholder engagement and research prioritization:
- The Patient-Centered Outcomes Research Institute (PCORI) standards for formulating patient-centered research questions,18 which includes methods for stakeholder engagement that ensures the representativeness of engaged groups and dissemination of study results.18
- The James Lind Alliance (JLA) approach to “priority setting partnerships,” through which patients, caregivers, and clinicians partner to identify and prioritize unanswered questions.19
The Improving Hospital Outcomes through Patient Engagement (i-HOPE) study included eight stepwise phases to formulate and prioritize a set of patient-centered research questions to improve the care and experiences of hospitalized patients and their families.20 Our process is described below and summarized in Table 1.
Phases of Question Development
Phase 1: Steering Committee Formation
Nine clinical researchers, nine patients and/or caregivers, and two administrators from eight academic and community hospitals from across the United States formed a steering committee to participate in teleconferences every other week to manage all stages of the project including design, implementation, and dissemination. At the time of the project conceptualization, the researchers were a subgroup of the Society of Hospital Medicine Research Committee.21 Patient partners on the steering committee were identified from local patient and family advisory councils (PFACs) of the researchers’ institutions. Patients partners had previously participated in research or improvement initiatives with their hospitalist partners. Patient partners received stipends throughout the project in recognition of their participation and expertise. Included in the committee was a representative from the Society of Hospital Medicine (SHM)—our supporting and dissemination partner.
Phase 2: Stakeholder Identification
We created a list of potential stakeholder organizations to participate in the study based on the following:
- Organizations with which SHM has worked on initiatives related to the care of hospitalized adult patients
- Organizations with which steering committee members had worked
- Internet searches of organizations participating in similar PCORI-funded projects and of other professional societies that represented patients or providers who work in hospital or post-acute care settings
- Suggestions from stakeholders identified through the first two approaches as described above
We intended to have a broad representation of stakeholders to ensure diverse perspectives were included in the study. Stakeholder organizations included patient advocacy groups, providers, researchers, payers, policy makers and funding agencies.
Phase 3: Stakeholder Engagement and Awareness Training
Representatives from 39 stakeholder organizations who agreed to participate in the study were further orientated to the study rationale and methods via a series of interactive online webinars. This included reminding organziations that everyone’s input and perspective were valued and that we had a flat organization structure that ensured all stakeholders were equal.
Phase 4: Survey Development and Administration
We chose a survey approach to solicit input on identifying gaps in patient care and to generate research questions. The steering committee developed an online survey collaboratively with stakeholder organization representatives. We used survey pretesting with patient and researcher members from the steering committee. The goal of pretesting was to ensure accessibility and comprehension for all potential respondents, particularly patients and caregivers. The final survey asked respondents to record up to three questions that they thought would improve the care of hospitalized adult patients and their families. The specific wording of the survey is shown in the Figure and the entire survey in Appendix Document 1.
We chose three questions because that is the number of entries per participant that is recommended by JLA; it also minimizes responder burden.19 We asked respondents to identify the stakeholder group they represented (eg, patient, caregiver, healthcare provider, researcher) and for providers to identify where they primarily worked (eg, acute care hospital, post-acute care, advocacy group).
Survey Administration. We administered the survey electronically using Research Electronic Data Capture (REDCap), a secure web-based application used for collecting research data.22 Stakeholders were asked to disseminate the survey broadly using whatever methods that they felt was appropriate to their leadership or members.
Phase 5: Initial Question Categorization Using Qualitative Content Analysis
Six members of the steering committee independently performed qualitative content analysis to categorize all submitted questions.23,24 This analytic approach identifies, analyzes, and reports patterns within the data.23,24 We hypothesized that some of the submitted questions would relate to already-known problems with hospitalization. Therefore the steering committee developed an a priori codebook of 48 categories using common systems-based issues and diseases related to the care of hospitalized patients based on the hospitalist core competency topics developed by hospitalists and the SHM Education Committee,25 personal and clinical knowledge and experience related to the care of hospitalized adult patients, and published literature on the topic. These a priori categories and their definitions are shown in Appendix Document 2 and were the basis for our initial theory-driven (deductive) approach to data analysis.23
Once coding began, we identified 32 new and additional categories based on our review of the submitted questions, and these were the basis of our data-driven (inductive) approach to analysis.23 All proposed new codes and definitions were discussed with and approved by the entire steering committee prior to being added to the codebook (Appendix Document 2).
While coding categories were mutually exclusive, multiple codes could be attributed to a question depending on the content and meaning of a question. To ensure methodological rigor, reviewers met regularly via teleconference or communicated via email throughout the analysis to iteratively refine and define coding categories. All questions were reviewed independently, and then discussed, by at least two members of the analysis team. Any coding disparities were discussed and resolved by negotiated consensus.26 Analysis was conducted using Dedoose V8.0.35 (Sociocultural Research Consultants, Los Angeles, California).
Phase 6: Initial Question Identification Using Quantitative Content Analysis
Following thematic categorization, all steering committee members then reviewed each category to identify and quantify the most commonly submitted questions.27 A question was determined to be a commonly submitted question when it appeared at least 10 times.
Phase 7: Interim Priority Setting
We sent the list of the most commonly submitted questions (Appendix Document 3) to stakeholder organizations and patient partner networks for review and evaluation. Each organization was asked to engage with their constituents and leaders to collectively decide on which of these questions resonated and was most important. These preferences would then be used during the in-person meeting (Phase 8). We did not provide stakeholder organizations with information about how many times each question was submitted by respondents because we felt this could potentially bias their decision-making processes such that true importance and relevance would not obtained.
Phase 8: In-person Meeting for Final Question Prioritization and Refinement
Representatives from all 39 participating stakeholder organizations were invited to participate in a 2-day, in-person meeting to create a final prioritized list of questions to be used to guide patient-centered research seeking to improve the care of hospitalized adult patients and their caregivers. This meeting was attended by 43 stakeholders (26 stakeholder organization representatives and 17 steering committee members) from 37 unique stakeholder organizations. To facilitate the inclusiveness and to ensure a consensus-driven process, we used nominal group technique (NGT) to allow all of the meeting participants to discuss the list of prioritized questions in small groups.28 NGT allows participants to comprehend each other’s point of view to ensure no perpsectives are excluded.28 The NGT was followed by two rounds of individual voting. Stakeholders were then asked to frame their discussions and their votes based on the perspectives of their organizations or PFACs that they represent. The voting process required participants to make choices regarding the relative importance of all of the questions, which therefore makes the resulting list a true prioritized list. In the first round of voting, participants voted for up to five questions for inclusion on the prioritized list. Based on the distribution of votes, where one vote equals one point, each of the 36 questions was then ranked in order of the assigned points. The rank-ordering process resulted in a natural cut point or delineated point, resulting in the 11 questions considered to be the highest prioritized questions. Following this, a second round of voting took place with the same parameters as the first round and allowed us to rank order questions by order of importance and priority. Finally, during small and large group discussions, the original text of each question was edited, refined, and reformatted into questions that could drive a research agenda.
Ethical Oversight
This study was reviewed by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and deemed not to be human subject research (UT Health San Antonio IRB Protocol Number: HSC20170058N).
RESULTS
In total, 499 respondents from 39 unique stakeholder organizations responded to our survey. Respondents self-identified into multiple categorizes resulting in 267 healthcare providers, 244 patients and caregivers, and 63 researchers. Characteristics of respondents to the survey are shown in Table 2.
An overview of study results is shown in Table 1. Respondents submitted a total of 782 questions related to improving the care of hospitalized patients. These questions were categorized during thematic analysis into 70 distinct categories—52 that were health system related and 18 that were disease specific (Appendix 2). The most frequently used health system–related categories were related to discharge care transitions, medications, patient understanding, and patient-family-care team communication (Appendix 2).
From these categories, 36 questions met our criteria for “commonly identified,” ie, submitted at least 10 times (Appendix Document 3). Notably, these 36 questions were derived from 67 different coding categories, of which 24 (36%) were a priori (theory-driven) categories23 created by the Steering Committee before analysis began and 43 (64%) categories were created as a result of this study’s stakeholder-engaged process and a data-driven approach23 to analysis (Appendix Document 3). These groups of questions were then presented during the 2-day, in-person meeting and reduced to a final 11 questions that were identified in rank order as top priorities (Table 3). The questions considered highest priority related to ensuring shared treatment and goals of care decision making, improving hospital discharge handoff to other care facilities and providers, and reducing the confusion related to education on medications, conditions, hospital care, and discharge.
DISCUSSION
Using a dynamic and collaborative stakeholder engagement process, we identified 11 questions prioritized in order of importance by patients, caregivers, and other healthcare stakeholders to improve the care of hospitalized adult patients. While some of the topics identified are already well-known topics in need of research and improvement, our findings frame these topics according to the perspectives of patients, caregivers, and stakeholders. This unique perspective adds a level of richness and nuance that provides insight into how to better address these topics and ultimately inform research and quality improvement efforts.
The question considered to be the highest priority area for future research and improvement surmised how it may be possible to implement interventions that engage patients in shared decision making. Shared decision making involves patients and their care team working together to make decisions about treatment, and other aspects of care based on sound clinical evidence that balances the risks and outcomes with patient preferences and values. Although considered critically important,29 a recent evaluation of shared decision making practices in over 250 inpatient encounters identified significant gaps in physicians’ abilities to perform key elements of a shared decision making approach and reinforced the need to identify what strategies can best promote widespread shared decision making.30 While there has been considerable effort to faciliate shared decision making in practice, there remains mixed evidence regarding the sustainability and impact of tools seeking to support shared decision making, such as decision aids, question prompt lists, and coaches.31 This suggests that new approaches to shared decision making may be required and likely explains why this question was rated as a top priority by stakeholders in the current study.
Respondents frequently framed their questions in terms of their lived experiences, providing stories and scenarios to illustrate the importance of the questions they submitted. This personal framing highlighted to us the need to think about improving care delivery from the end-user perspective. For example, respondents framed questions about care transitions not with regard to early appointments, instructions, or medication lists, but rather in terms of whom to call with questions or how best to reach their physician, nurse, or other identified provider. These perspectives suggest that strategies and approaches to improvement that start with patient and caregiver experiences, such as design thinking,32 may be important to continued efforts to improve hospital care. Additionally, the focus on the interpersonal aspects of care delivery highlights the need to focus on the patient-provider relationship and communication.
Questions submitted by respondents demonstrated a stark difference between “patient education” and “patient understanding,” which suggests that being provided with education or education materials regarding care did not necessarily lead to a clear patient understanding. The potential for lack of understanding was particularly prominent in the context of care plan development and during times of care transition—topics that were encompassed in 9 out of 11 of our prioritized research questions. This may suggest that approaches that improve the ability for healthcare providers to deliver information may not be sufficient to meet the needs of patients and caregivers. Rather, partnering to develop a shared understanding—whether about prognosis, medications, hospital, or discharge care plans—is critical. Improved communication practices are not an endpoint for information delivery, but rather a starting point leading to a shared understanding.
Several of the priority areas identified in our study reflect the immensely complex intersections among patients, caregivers, clinicians, and the healthcare delivery system. Addressing these gaps in order to reach the goal of ideal hospital care and an improved patient experience will likely require coordinated approaches and strong involvement and buy-in from multiple stakeholders including the voices of patients and caregivers. Creating patient-centered and stakeholder-driven research has been an increasing priority nationally.33 Yet to realize this, we must continue to understand the foundations and best practices of authentic stakeholder engagement so that it can be achieved in practice.34 We intend for this prioritized list of questions to galvanize funders, researchers, clinicians, professional societies, and patient and caregiver advocacy groups to work together to address these topics through the creation of new research evidence or the sustainable implementation of existing evidence.
Our findings provide a foundation for stakeholder groups to work in partnership to find research and improvement solutions to the problems identified. Our efforts demonstrate the value and importance of a systematic and broad engagement process to ensure that the voices of patients, caregivers, and other healthcare stakeholders are included in guiding hospital research and quality improvement efforts. This is highlighted by the fact our results of prioritized category areas for research were largely only uncovered following the creation of coding categories during the analysis process and were not captured using a priori catgeories that were expected by the steering committee.
The strengths of this study include our attempts to systematically identify and engage a wide range of perspectives in hospital medicine, including perspectives from patients and their caregivers. There are also acknowledged limitations in our study. While we included patients and PFACs from across the country, the opinions of the people we included may not be representative of all patients. Similarly, the perspectives of the other participants may not have completely represented their stakeholder organizations. While we attempted to include a broad range of organizations, there may be other relevant groups who were not represented in our sample.
In summary, our findings provide direction for the multiple stakeholders involved in improving hospital care. The results will allow the research community to focus on questions that are most important to patients, caregivers, and other stakeholders, reframing them in ways that are more relevant to patients’ lived experiences and that reflect the complexity of the issues. Our findings can also be used by healthcare providers and delivery organizations to target local improvement efforts. We hope that patients and caregivers will use our results to advocate for research and improvement in areas that matter the most to them. We hope that policy makers and funding agencies use our results to promote work in these areas and drive a national conversation about how to most effectively improve hospital care.
Acknowledgments
The authors would like to thank all patients, caregivers, and stakeholders who completed the survey. The authors also would like to acknowledge the organizations and individuals who participated in this study (see Appendix Document 4 for full list). At SHM, the authors would like to specifically thank Claudia Stahl, Jenna Goldstein, Kevin Vuernick, Dr Brad Sharpe, and Dr Larry Wellikson for their support.
Disclaimer
The statements presented in this publication are solely the responsibility of the authors and do not necessarily represent the views of the Department of Veterans Affairs, Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.
1. American Hospital Association. 2019 American Hospital Association Hospital Statistics. Chicago, Illinois: American Hospital Association; 2019.
2. Alper E, O’Malley T, Greenwald J. UptoDate: Hospital discharge and readmission. https://www.uptodate.com/contents/hospital-discharge-and-readmission. Accessed August 8, 2019.
3. de Vries EN, Ramrattan MA, Smorenburg SM, Gouma DJ, Boermeester MA. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Heal Care. 2008;17(3):216-223. https://doi.org/10.1136/qshc.2007.023622.
4. Agency for Healthcare Research and Quality. Readmissions and Adverse Events After Discharge. https://psnet.ahrq.gov/primers/primer/11/Readmissions-and-Adverse-Events-After-Discharge. Accessed August 8, 2019.
5. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC; National Academies Press; 2001. https://doi.org/10.17226/10027.
6. Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and equity of care in U.S. hospitals. N Engl J Med. 2014;371(24):2298-2308. https://doi.org/10.1056/NEJMsa1405003.
7. National Patient Safety Foundation. Free from Harm: Accelerating Patient Safety Improvement Fifteen Years after To Err Is Human. Boston: National Patient Safety Foundation; 2015.
8. Agency for Healthcare Research and Quality. AHRQ National Scorecard on Hospital-Acquired Conditions Updated Baseline Rates and Preliminary Results 2014–2017. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-safety/pfp/hacreport-2019.pdf. Accessed August 8, 2019.
9. Hansen LO, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8(8):421-427. https://doi.org/10.1002/jhm.2054.
10. Snyder HJ, Fletcher KE. The hospital experience through the patients’ eyes. J Patient Exp. 2019. https://doi.org/10.1177/2374373519843056.
11. Kebede S, Shihab HM, Berger ZD, Shah NG, Yeh H-C, Brotman DJ. Patients’ understanding of their hospitalizations and association with satisfaction. JAMA Intern Med. 2014;174(10):1698-1700. https://doi.org/10.1001/jamainternmed.2014.3765.
12. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. https://doi.org/10.1002/jhm.1937.
13. Neeman N, Quinn K, Shoeb M, Mourad M, Sehgal NL, Sliwka D. Postdischarge focus groups to improve the hospital experience. Am J Med Qual. 2013;28(6):536-538. https://doi.org/10.1177/1062860613488623.
14. Duffett L. Patient engagement: what partnering with patients in research is all about. Thromb Res. 2017;150:113-120. https://doi.org/10.1016/j.thromres.2016.10.029.
15. Pomey M, Hihat H, Khalifa M, Lebel P, Neron A, Dumez V. Patient partnership in quality improvement of healthcare services: patients’ inputs and challenges faced. Patient Exp J. 2015;2:29-42. https://doi.org/10.35680/2372-0247.1064.
16. Robbins M, Tufte J, Hsu C. Learning to “swim” with the experts: experiences of two patient co-investigators for a project funded by the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):85-88. https://doi.org/10.7812/TPP/15-162.
17. Tai-Seale M, Sullivan G, Cheney A, Thomas K, Frosch D. The language of engagement: “aha!” moments from engaging patients and community partners in two pilot projects of the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):89-92. https://doi.org/10.7812/TPP/15-123.
18. Patient-Centered Outcomes Research Institute (PCORI). PCORI Methodology Standards: Standards for Formulating Research Questions. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Formulating Research Questions. Accessed August 8, 2019.
19. James Lind Alliance. The James Lind Alliance Guidebook. Version 8. Southampton, England: James Lind Alliance; 2018.
20. Society of Hospital Medicine (SHM). Improving Hospital Outcomes through Patient Engagement: The i-HOPE Study. https://www.hospitalmedicine.org/clinical-topics/i-hope-study/. Accessed August 8, 2019.
21. Society of Hospital Medicine (SHM). Committees. https://www.hospitalmedicine.org/membership/committees/. Accessed August 8, 2019.
22. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research Electronic Data Capture (REDCap) - a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
23. Schreier M. Qualitative content analysis in practice. Los Angeles, CA: SAGE Publications; 2012.
24. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-115. https://doi.org/10.1111/j.1365-2648.2007.04569.x.
25. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine—2017 revision: introduction and methodology. J Hosp Med. 2017;12(4):283-287. https://doi.org/10.12788/jhm.2715.
26. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Serv Res. 2007;42(4):1758-1772. https://doi.org/10.1111/j.1475-6773.2006.00684.x.
27. Coe K, Scacco JM. Content analysis, quantitative. Int Encycl Commun Res Methods. 2017:1-11. https://doi.org/10.1002/9781118901731.iecrm0045.
28. Centers for Disease Control and Prevention. Evaluation Briefs: Gaining Consensus Among Stakeholders Through the Nominal Group Technique. Atlanta, GA; 2018. https://www.cdc.gov/healthyyouth/evaluation/pdf/brief7.pdf. Accessed August 8, 2019.
29. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. 1997;44(5):681-692. https://doi.org/10.1016/s0277-9536(96)00221-3.
30. Blankenburg R, Hilton JF, Yuan P, et al. Shared decision-making during inpatient rounds: opportunities for improvement in patient engagement and communication. J Hosp Med. 2018;13(7):453-461. https://doi.org/10.12788/jhm.2909.
31. Legare F, Adekpedjou R, Stacey D, et al. Interventions for increasing the use of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2018;7(7):CD006732. https://doi.org/10.1002/14651858.CD006732.pub4.
32. Roberts JP, Fisher TR, Trowbridge MJ, Bent C. A design thinking framework for healthcare management and innovation. Healthc (Amst). 2016;4(1):11-14. https://doi.org/10.1016/j.hjdsi.2015.12.002.
33. Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA. 2012;307(15):1583-1584. https://doi.org/10.1001/jama.2012.500.
34. Harrison J, Auerbach A, Anderson W, et al. Patient stakeholder engagement in research: a narrative review to describe foundational principles and best practice activities. Health Expect. 2019;22(3):307-316. https://doi.org/10.1111/hex.12873.
1. American Hospital Association. 2019 American Hospital Association Hospital Statistics. Chicago, Illinois: American Hospital Association; 2019.
2. Alper E, O’Malley T, Greenwald J. UptoDate: Hospital discharge and readmission. https://www.uptodate.com/contents/hospital-discharge-and-readmission. Accessed August 8, 2019.
3. de Vries EN, Ramrattan MA, Smorenburg SM, Gouma DJ, Boermeester MA. The incidence and nature of in-hospital adverse events: a systematic review. Qual Saf Heal Care. 2008;17(3):216-223. https://doi.org/10.1136/qshc.2007.023622.
4. Agency for Healthcare Research and Quality. Readmissions and Adverse Events After Discharge. https://psnet.ahrq.gov/primers/primer/11/Readmissions-and-Adverse-Events-After-Discharge. Accessed August 8, 2019.
5. Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC; National Academies Press; 2001. https://doi.org/10.17226/10027.
6. Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and equity of care in U.S. hospitals. N Engl J Med. 2014;371(24):2298-2308. https://doi.org/10.1056/NEJMsa1405003.
7. National Patient Safety Foundation. Free from Harm: Accelerating Patient Safety Improvement Fifteen Years after To Err Is Human. Boston: National Patient Safety Foundation; 2015.
8. Agency for Healthcare Research and Quality. AHRQ National Scorecard on Hospital-Acquired Conditions Updated Baseline Rates and Preliminary Results 2014–2017. https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-safety/pfp/hacreport-2019.pdf. Accessed August 8, 2019.
9. Hansen LO, Greenwald JL, Budnitz T, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8(8):421-427. https://doi.org/10.1002/jhm.2054.
10. Snyder HJ, Fletcher KE. The hospital experience through the patients’ eyes. J Patient Exp. 2019. https://doi.org/10.1177/2374373519843056.
11. Kebede S, Shihab HM, Berger ZD, Shah NG, Yeh H-C, Brotman DJ. Patients’ understanding of their hospitalizations and association with satisfaction. JAMA Intern Med. 2014;174(10):1698-1700. https://doi.org/10.1001/jamainternmed.2014.3765.
12. Shoeb M, Merel SE, Jackson MB, Anawalt BD. “Can we just stop and talk?” patients value verbal communication about discharge care plans. J Hosp Med. 2012;7(6):504-507. https://doi.org/10.1002/jhm.1937.
13. Neeman N, Quinn K, Shoeb M, Mourad M, Sehgal NL, Sliwka D. Postdischarge focus groups to improve the hospital experience. Am J Med Qual. 2013;28(6):536-538. https://doi.org/10.1177/1062860613488623.
14. Duffett L. Patient engagement: what partnering with patients in research is all about. Thromb Res. 2017;150:113-120. https://doi.org/10.1016/j.thromres.2016.10.029.
15. Pomey M, Hihat H, Khalifa M, Lebel P, Neron A, Dumez V. Patient partnership in quality improvement of healthcare services: patients’ inputs and challenges faced. Patient Exp J. 2015;2:29-42. https://doi.org/10.35680/2372-0247.1064.
16. Robbins M, Tufte J, Hsu C. Learning to “swim” with the experts: experiences of two patient co-investigators for a project funded by the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):85-88. https://doi.org/10.7812/TPP/15-162.
17. Tai-Seale M, Sullivan G, Cheney A, Thomas K, Frosch D. The language of engagement: “aha!” moments from engaging patients and community partners in two pilot projects of the Patient-Centered Outcomes Research Institute. Perm J. 2016;20(2):89-92. https://doi.org/10.7812/TPP/15-123.
18. Patient-Centered Outcomes Research Institute (PCORI). PCORI Methodology Standards: Standards for Formulating Research Questions. https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards#Formulating Research Questions. Accessed August 8, 2019.
19. James Lind Alliance. The James Lind Alliance Guidebook. Version 8. Southampton, England: James Lind Alliance; 2018.
20. Society of Hospital Medicine (SHM). Improving Hospital Outcomes through Patient Engagement: The i-HOPE Study. https://www.hospitalmedicine.org/clinical-topics/i-hope-study/. Accessed August 8, 2019.
21. Society of Hospital Medicine (SHM). Committees. https://www.hospitalmedicine.org/membership/committees/. Accessed August 8, 2019.
22. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research Electronic Data Capture (REDCap) - a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
23. Schreier M. Qualitative content analysis in practice. Los Angeles, CA: SAGE Publications; 2012.
24. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-115. https://doi.org/10.1111/j.1365-2648.2007.04569.x.
25. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine—2017 revision: introduction and methodology. J Hosp Med. 2017;12(4):283-287. https://doi.org/10.12788/jhm.2715.
26. Bradley EH, Curry LA, Devers KJ. Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Serv Res. 2007;42(4):1758-1772. https://doi.org/10.1111/j.1475-6773.2006.00684.x.
27. Coe K, Scacco JM. Content analysis, quantitative. Int Encycl Commun Res Methods. 2017:1-11. https://doi.org/10.1002/9781118901731.iecrm0045.
28. Centers for Disease Control and Prevention. Evaluation Briefs: Gaining Consensus Among Stakeholders Through the Nominal Group Technique. Atlanta, GA; 2018. https://www.cdc.gov/healthyyouth/evaluation/pdf/brief7.pdf. Accessed August 8, 2019.
29. Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med. 1997;44(5):681-692. https://doi.org/10.1016/s0277-9536(96)00221-3.
30. Blankenburg R, Hilton JF, Yuan P, et al. Shared decision-making during inpatient rounds: opportunities for improvement in patient engagement and communication. J Hosp Med. 2018;13(7):453-461. https://doi.org/10.12788/jhm.2909.
31. Legare F, Adekpedjou R, Stacey D, et al. Interventions for increasing the use of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2018;7(7):CD006732. https://doi.org/10.1002/14651858.CD006732.pub4.
32. Roberts JP, Fisher TR, Trowbridge MJ, Bent C. A design thinking framework for healthcare management and innovation. Healthc (Amst). 2016;4(1):11-14. https://doi.org/10.1016/j.hjdsi.2015.12.002.
33. Selby JV, Beal AC, Frank L. The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. JAMA. 2012;307(15):1583-1584. https://doi.org/10.1001/jama.2012.500.
34. Harrison J, Auerbach A, Anderson W, et al. Patient stakeholder engagement in research: a narrative review to describe foundational principles and best practice activities. Health Expect. 2019;22(3):307-316. https://doi.org/10.1111/hex.12873.
© 2020 Society of Hospital Medicine
SPEAKers at the National Society of Hospital Medicine Meeting: A Follow-UP Study of Gender Equity for Conference Speakers from 2015 to 2019. The SPEAK UP Study
Persistent gender disparities exist in pay,1,2 leadership opportunities,3,4 promotion,5 and speaking opportunities.6 While the gender distribution of the hospitalist workforce may be approaching parity,3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.3 Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).3
The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.
METHODS
The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.
We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.
Speaker Categories and Identification Process
We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.
Open Call Process
We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.
After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.
For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)
Gender
Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.
ANALYSIS
REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.9
All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.
RESULTS
Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.
From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).
The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).
A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).
Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).
We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).
DISCUSSION
The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.
To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.
A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.11
It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.
Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.14 The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.15
Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.
Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).
Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,6 which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.3
CONCLUSIONS
An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.
Acknowledgments
Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group
Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick
1. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400.
2. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284.
3. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340.
4. Silver JK, Ghalib R, Poorman JA, et al. Analysis of gender equity in leadership of physician-focused medical specialty societies, 2008-2017. JAMA Intern Med. 2019;179(3):433-435. https://doi.org/10.1001/jamainternmed.2018.5303.
5. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680.
6. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the Proportion of Female Speakers at Medical Conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
7. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. https://doi.org/10.1007/s11606-011-1892-5.
8. Today’s Hospitalist 2018 Compensation and Career Survey Results. https://www.todayshospitalist.com/salary-survey-results/. Accessed September 28, 2019.
9. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
10. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. Association of American Medical Colleagues (AAMC) Group on Women in Medicine and Science. Recruitment Toolkit: https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf. Accessed July 27, 2019.
11. Casadevall A. Achieving speaker gender equity at the american society for microbiology general meeting. MBio. 2015;6:e01146. https://doi.org/10.1128/mBio.01146-15.
12. Westring A, McDonald JM, Carr P, Grisso JA. An integrated framework for gender equity in academic medicine. Acad Med. 2016;91(8):1041-1044. https://doi.org/10.1097/ACM.0000000000001275.
13. Martin JL. Ten simple rules to achieve conference speaker gender balance. PLoS Comput Biol. 2014;10(11):e1003903. https://doi.org/10.1371/journal.pcbi.1003903.
14. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247.
15. Butkus R, Serchen J, Moyer DV, et al. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168:721-723. https://doi.org/10.7326/M17-3438.
Persistent gender disparities exist in pay,1,2 leadership opportunities,3,4 promotion,5 and speaking opportunities.6 While the gender distribution of the hospitalist workforce may be approaching parity,3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.3 Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).3
The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.
METHODS
The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.
We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.
Speaker Categories and Identification Process
We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.
Open Call Process
We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.
After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.
For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)
Gender
Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.
ANALYSIS
REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.9
All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.
RESULTS
Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.
From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).
The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).
A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).
Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).
We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).
DISCUSSION
The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.
To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.
A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.11
It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.
Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.14 The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.15
Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.
Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).
Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,6 which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.3
CONCLUSIONS
An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.
Acknowledgments
Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group
Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick
Persistent gender disparities exist in pay,1,2 leadership opportunities,3,4 promotion,5 and speaking opportunities.6 While the gender distribution of the hospitalist workforce may be approaching parity,3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.3 Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).3
The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.
METHODS
The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.
We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.
Speaker Categories and Identification Process
We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.
Open Call Process
We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.
After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.
For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)
Gender
Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.
ANALYSIS
REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.9
All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.
RESULTS
Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.
From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).
The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).
A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).
Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).
We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).
DISCUSSION
The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.
To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.
A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.11
It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.
Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.14 The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.15
Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.
Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).
Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,6 which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.3
CONCLUSIONS
An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.
Acknowledgments
Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group
Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick
1. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400.
2. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284.
3. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340.
4. Silver JK, Ghalib R, Poorman JA, et al. Analysis of gender equity in leadership of physician-focused medical specialty societies, 2008-2017. JAMA Intern Med. 2019;179(3):433-435. https://doi.org/10.1001/jamainternmed.2018.5303.
5. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680.
6. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the Proportion of Female Speakers at Medical Conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
7. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. https://doi.org/10.1007/s11606-011-1892-5.
8. Today’s Hospitalist 2018 Compensation and Career Survey Results. https://www.todayshospitalist.com/salary-survey-results/. Accessed September 28, 2019.
9. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
10. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. Association of American Medical Colleagues (AAMC) Group on Women in Medicine and Science. Recruitment Toolkit: https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf. Accessed July 27, 2019.
11. Casadevall A. Achieving speaker gender equity at the american society for microbiology general meeting. MBio. 2015;6:e01146. https://doi.org/10.1128/mBio.01146-15.
12. Westring A, McDonald JM, Carr P, Grisso JA. An integrated framework for gender equity in academic medicine. Acad Med. 2016;91(8):1041-1044. https://doi.org/10.1097/ACM.0000000000001275.
13. Martin JL. Ten simple rules to achieve conference speaker gender balance. PLoS Comput Biol. 2014;10(11):e1003903. https://doi.org/10.1371/journal.pcbi.1003903.
14. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247.
15. Butkus R, Serchen J, Moyer DV, et al. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168:721-723. https://doi.org/10.7326/M17-3438.
1. Weaver AC, Wetterneck TB, Whelan CT, Hinami K. A matter of priorities? Exploring the persistent gender pay gap in hospital medicine. J Hosp Med. 2015;10(8):486-490. https://doi.org/10.1002/jhm.2400.
2. Jena AB, Olenski AR, Blumenthal DM. Sex differences in physician salary in US public medical schools. JAMA Intern Med. 2016;176(9):1294-1304. https://doi.org/10.1001/jamainternmed.2016.3284.
3. Burden M, Frank MG, Keniston A, et al. Gender disparities in leadership and scholarly productivity of academic hospitalists. J Hosp Med. 2015;10(8):481-485. https://doi.org/10.1002/jhm.2340.
4. Silver JK, Ghalib R, Poorman JA, et al. Analysis of gender equity in leadership of physician-focused medical specialty societies, 2008-2017. JAMA Intern Med. 2019;179(3):433-435. https://doi.org/10.1001/jamainternmed.2018.5303.
5. Jena AB, Khullar D, Ho O, Olenski AR, Blumenthal DM. Sex differences in academic rank in US medical schools in 2014. JAMA. 2015;314(11):1149-1158. https://doi.org/10.1001/jama.2015.10680.
6. Ruzycki SM, Fletcher S, Earp M, Bharwani A, Lithgow KC. Trends in the Proportion of Female Speakers at Medical Conferences in the United States and in Canada, 2007 to 2017. JAMA Netw Open. 2019;2(4):e192103. https://doi.org/10.1001/jamanetworkopen.2019.2103
7. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. https://doi.org/10.1007/s11606-011-1892-5.
8. Today’s Hospitalist 2018 Compensation and Career Survey Results. https://www.todayshospitalist.com/salary-survey-results/. Accessed September 28, 2019.
9. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
10. Burden M, del Pino-Jones A, Shafer M, Sheth S, Rexrode K. Association of American Medical Colleagues (AAMC) Group on Women in Medicine and Science. Recruitment Toolkit: https://www.aamc.org/download/492864/data/equityinrecruitmenttoolkit.pdf. Accessed July 27, 2019.
11. Casadevall A. Achieving speaker gender equity at the american society for microbiology general meeting. MBio. 2015;6:e01146. https://doi.org/10.1128/mBio.01146-15.
12. Westring A, McDonald JM, Carr P, Grisso JA. An integrated framework for gender equity in academic medicine. Acad Med. 2016;91(8):1041-1044. https://doi.org/10.1097/ACM.0000000000001275.
13. Martin JL. Ten simple rules to achieve conference speaker gender balance. PLoS Comput Biol. 2014;10(11):e1003903. https://doi.org/10.1371/journal.pcbi.1003903.
14. Shah SS, Shaughnessy EE, Spector ND. Leading by example: how medical journals can improve representation in academic medicine. J Hosp Med. 2019;14(7):393. https://doi.org/10.12788/jhm.3247.
15. Butkus R, Serchen J, Moyer DV, et al. Achieving gender equity in physician compensation and career advancement: a position paper of the American College of Physicians. Ann Intern Med. 2018;168:721-723. https://doi.org/10.7326/M17-3438.
© 2020 Society of Hospital Medicine
Opioid Utilization and Perception of Pain Control in Hospitalized Patients: A Cross-Sectional Study of 11 Sites in 8 Countries
Since 2000, the United States has seen a marked increase in opioid prescribing1-3 and opioid-related complications, including overdoses, hospitalizations, and deaths.2,4,5 A study from 2015 showed that more than one-third of the US civilian noninstitutionalized population reported receiving an opioid prescription in the prior year, with 12.5% reporting misuse, and, of those, 16.7% reported a prescription use disorder.6 While there has been a slight decrease in opioid prescriptions in the US since 2012, rates of opioid prescribing in 2015 were three times higher than in 1999 and approximately four times higher than in Europe in 2015.3,7
Pain is commonly reported by hospitalized patients,8,9 and opioids are often a mainstay of treatment;9,10 however, treatment with opioids can have a number of adverse outcomes.2,10,11 Short-term exposure to opioids can lead to long-term use,12-16 and patients on opioids are at an increased risk for subsequent hospitalization and longer inpatient lengths of stay.5
Physician prescribing practices for opioids and patient expectations for pain control vary as a function of geographic region and culture,10,12,17,18 and pain is influenced by the cultural context in which it occurs.17,19-22 Treatment of pain may also be affected by limited access to or restrictions on selected medications, as well as by cultural biases.23 Whether these variations in the treatment of pain are reflected in patients’ satisfaction with pain control is uncertain.
We sought to compare the inpatient analgesic prescribing practices and patients’ perceptions of pain control for medical patients in four teaching hospitals in the US and in seven teaching hospitals in seven other countries.
METHODS
Study Design
We utilized a cross-sectional, observational design. The study was approved by the Institutional Review Boards at all participating sites.
Setting
The study was conducted at 11 academic hospitals in eight countries from October 8, 2013 to August 31, 2015. Sites in the US included Denver Health in Denver, Colorado; the University of Colorado Hospital in Aurora, Colorado; Hennepin Healthcare in Minneapolis, Minnesota; and Legacy Health in Portland, Oregon. Sites outside the US included McMaster University in Hamilton, Ontario, Canada; Hospital de la Santa Creu i Sant Pau, Universitat Autonòma de Barcelona in Barcelona, Spain; the University of Study of Milan and the University Ospedale “Luigi Sacco” in Milan, Italy, the National Taiwan University Hospital, in Taipei, Taiwan, the University of Ulsan College of Medicine, Asan Medical Center, in Seoul, Korea, the Imperial College, Chelsea and Westminster Hospital, in London, United Kingdom and Dunedin Hospital, Dunedin, New Zealand.
Inclusion and Exclusion Criteria
We included patients 18-89 years of age (20-89 in Taiwan because patients under 20 years of age in this country are a restricted group with respect to participating in research), admitted to an internal medicine service from the Emergency Department or Urgent Care clinic with an acute illness for a minimum of 24 hours (with time zero defined as the time care was initiated in the Emergency Department or Urgent Care Clinic), who reported pain at some time during the first 24-36 hours of their hospitalization and who provided informed consent. In the US, “admission” included both observation and inpatient status. We limited the patient population to those admitted via emergency departments and urgent care clinics in order to enroll similar patient populations across sites.
Scheduled admissions, patients transferred from an outside facility, patients admitted directly from a clinic, and those receiving care in intensive care units were excluded. We also excluded patients who were incarcerated, pregnant, those who received major surgery within the previous 14 days, those with a known diagnosis of active cancer, and those who were receiving palliative or hospice care. Patients receiving care from an investigator in the study at the time of enrollment were not eligible due to the potential conflict of interest.
Patient Screening
Primary teams were contacted to determine if any patients on their service might meet the criteria for inclusion in the study on preselected study days chosen on the basis of the research team’s availability. Identified patients were then screened to establish if they met the eligibility criteria. Patients were asked directly if they had experienced pain during their preadmission evaluation or during their hospitalization.
Data Collection
All patients were hospitalized at the time they gave consent and when data were collected. Data were collected via interviews with patients, as well as through chart review. We recorded patients’ age, gender, race, admitting diagnosis(es), length of stay, psychiatric illness, illicit drug use, whether they reported receiving opioid analgesics at the time of hospitalization, whether they were prescribed opioids and/or nonopioid analgesics during their hospitalization, the median and maximum doses of opioids prescribed and dispensed, and whether they were discharged on opioids. The question of illicit drug use was asked of all patients with the exception of those hospitalized in South Korea due to potential legal implications.
Opioid prescribing and receipt of opioids was recorded based upon current provider orders and medication administration records, respectively. Perception of and satisfaction with pain control was assessed with the American Pain Society Patient Outcome Questionnaire–Modified (APS-POQ-Modified).24,25 Versions of this survey have been validated in English as well as in other languages and cultures.26-28 Because hospitalization practices could differ across hospitals and in different countries, we compared patients’ severity of illness by using Charlson comorbidity scores. Consent forms and the APS-POQ were translated into each country’s primary language according to established processes.29 The survey was filled out by having site investigators read questions aloud and by use of a large-font visual analog scale to aid patients’ verbal responses.
Data were collected and managed using a secure, web-based application electronic data capture tool (Research Electronic Data Capture [REDCap], Nashville, Tennessee), hosted at Denver Health.30
Study Size
Preliminary data from the internal medicine units at our institution suggested that 40% of patients without cancer received opioid analgesics during their hospitalization. Assuming 90% power to detect an absolute difference in the proportion of inpatient medical patients who are receiving opioid analgesics during their hospital stay of 17%, a two-sided type 1 error rate of 0.05, six hospitals in the US, and nine hospitals from all other countries, we calculated an initial sample size of 150 patients per site. This sample size was considered feasible for enrollment in a busy inpatient clinical setting. Study end points were to either reach the goal number of patients (150 per site) or the predetermined study end date, whichever came first.
Data Analysis
We generated means with standard deviations (SDs) and medians with interquartile ranges (IQRs) for normally and nonnormally distributed continuous variables, respectively, and frequencies for categorical variables. We used linear mixed modeling for the analysis of continuous variables. For binary outcomes, our data were fitted to a generalized linear mixed model with logit as the link function and a binary distribution. For ordinal variables, specifically patient-reported satisfaction with pain control and the opinion statements, the data were fitted to a generalized linear mixed model with a cumulative logit link and a multinomial distribution. Hospital was included as a random effect in all models to account for patients cared for in the same hospital.
Country of origin, dichotomized as US or non-US, was the independent variable of interest for all models. An interaction term for exposure to opioids prior to admission and country was entered into all models to explore whether differences in the effect of country existed for patients who reported taking opioids prior to admission and those who did not.
The models for the frequency with which analgesics were given, doses of opioids given during hospitalization and at discharge, patient-reported pain score, and patient-reported satisfaction with pain control were adjusted for (1) age, (2) gender, (3) Charlson Comorbidity Index, (4) length of stay, (5) history of illicit drug use, (6) history of psychiatric illness, (7) daily dose in morphine milligram equivalents (MME) for opioids prior to admission, (8) average pain score, and (9) hospital. The patient-reported satisfaction with pain control model was also adjusted for whether or not opioids were given to the patient during their hospitalization. P < .05 was considered to indicate significance. All analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute, Inc., Cary, North Carolina). We reported data on medications that were prescribed and dispensed (as opposed to just prescribed and not necessarily given). Opioids prescribed at discharge represented the total possible opioids that could be given based upon the order/prescription (eg, oxycodone 5 mg every 6 hours as needed for pain would be counted as 20 mg/24 hours maximum possible dose followed by conversion to MME).
Missing Data
When there were missing data, a query was sent to sites to verify if the data were retrievable. If retrievable, the data were then entered. Data were missing in 5% and 2% of patients who did or did not report taking an opioid prior to admission, respectively. If a variable was included in a specific statistical test, then subjects with missing data were excluded from that analysis (ie, complete case analysis).
RESULTS
We approached 1,309 eligible patients, of which 981 provided informed consent, for a response rate of 75%; 503 from the US and 478 patients from other countries (Figure). In unadjusted analyses, we found no significant differences between US and non-US patients in age (mean age 51, SD 15 vs 59, SD 19; P = .30), race, ethnicity, or Charlson comorbidity index scores (median 2, IQR 1-3 vs 3, IQR 1-4; P = .45). US patients had shorter lengths of stay (median 3 days, IQR 2-4 vs 6 days, IQR 3-11; P = .04), a more frequent history of illicit drug use (33% vs 6%; P = .003), a higher frequency of psychiatric illness (27% vs 8%; P < .0001), and more were receiving opioid analgesics prior to admission (38% vs 17%; P = .007) than those hospitalized in other countries (Table 1, Appendix 1). The primary admitting diagnoses for all patients in the study are listed in Appendix 2. Opioid prescribing practices across the individual sites are shown in Appendix 3.
Patients Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found that more patients in the US were given opioids during their hospitalization and in higher doses than patients from other countries and more were prescribed opioids at discharge. Fewer patients in the US were dispensed nonopioid analgesics during their hospitalization than patients from other countries, but this difference was not significant (Table 2). Appendix 4 shows the types of nonopioid pain medications prescribed in the US and other countries.
After adjustment for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys. We found no significant difference in satisfaction with pain control between patients from the US and other countries in the models, regardless of whether we included average pain score or opioid receipt during hospitalization in the model (Table 3).
In unadjusted analyses, compared with patients hospitalized in other countries, more patients in the US stated that they would like a stronger dose of analgesic if they were still in pain, though the difference was nonsignificant, and US patients were more likely to agree with the statement that people become addicted to pain medication easily and less likely to agree with the statement that it is easier to endure pain than deal with the side effects of pain medications (Table 3).
Patients Not Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found no significant difference in the proportion of US patients provided with nonopioid pain medications during their hospitalization compared with patients in other countries, but a greater percentage of US patients were given opioids during their hospitalization and at discharge and in higher doses (Table 2).
After adjusting for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys and greater pain severity in the 24-36 hours prior to completing the survey than patients from other countries, but we found no difference in patient satisfaction with pain control (Table 3). After we included the average pain score and whether or not opioids were given to the patient during their hospitalization in this model, patients in the US were more likely to report a higher level of satisfaction with pain control than patients in all other countries (P = .001).
In unadjusted analyses, compared with patients hospitalized in other countries, those in the US were less likely to agree with the statement that good patients avoid talking about pain (Table 3).
Patient Satisfaction and Opioid Receipt
Among patients cared for in the US, after controlling for the average pain score, we did not find a significant association between receiving opioids while in the hospital and satisfaction with pain control for patients who either did or did not endorse taking opioids prior to admission (P = .38 and P = .24, respectively). Among patients cared for in all other countries, after controlling for the average pain score, we found a significant association between receiving opioids while in the hospital and a lower level of satisfaction with pain control for patients who reported taking opioids prior to admission (P = .02) but not for patients who did not report taking opioids prior to admission (P = .08).
DISCUSSION
Compared with patients hospitalized in other countries, a greater percentage of those hospitalized in the US were prescribed opioid analgesics both during hospitalization and at the time of discharge, even after adjustment for pain severity. In addition, patients hospitalized in the US reported greater pain severity at the time they completed their pain surveys and in the 24 to 36 hours prior to completing the survey than patients from other countries. In this sample, satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Our study also suggests that opioid receipt did not lead to improved patient satisfaction with pain control.
The frequency with which we observed opioid analgesics being prescribed during hospitalization in US hospitals (79%) was higher than the 51% of patients who received opioids reported by Herzig and colleagues.10 Patients in our study had a higher prevalence of illicit drug abuse and psychiatric illness, and our study only included patients who reported pain at some point during their hospitalization. We also studied prescribing practices through analysis of provider orders and medication administration records at the time the patient was hospitalized.
While we observed that physicians in the US more frequently prescribed opioid analgesics during hospitalizations than physicians working in other countries, we also observed that patients in the US reported higher levels of pain during their hospitalization. After adjusting for a number of variables, including pain severity, however, we still found that opioids were more commonly prescribed during hospitalizations by physicians working in the US sites studied than by physicians in the non-US sites.
Opioid prescribing practices varied across the sites sampled in our study. While the US sites, Taiwan, and Korea tended to be heavier utilizers of opioids during hospitalization, there were notable differences in discharge prescribing of opioids, with the US sites more commonly prescribing opioids and higher MME for patients who did not report taking opioids prior to their hospitalization (Appendix 3). A sensitivity analysis was conducted excluding South Korea from modeling, given that patients there were not asked about illicit opioid use. There were no important changes in the magnitude or direction of the results.
Our study supports previous studies indicating that there are cultural and societal differences when it comes to the experience of pain and the expectations around pain control.17,20-22,31 Much of the focus on reducing opioid utilization has been on provider practices32 and on prescription drug monitoring programs.33 Our findings suggest that another area of focus that may be important in mitigating the opioid epidemic is patient expectations of pain control.
Our study has a number of strengths. First, we included 11 hospitals from eight different countries. Second, we believe this is the first study to assess opioid prescribing and dispensing practices during hospitalization as well as at the time of discharge. Third, patient perceptions of pain control were assessed in conjunction with analgesic prescribing and were assessed during hospitalization. Fourth, we had high response rates for patient participation in our study. Fifth, we found much larger differences in opioid prescribing than anticipated, and thus, while we did not achieve the sample size originally planned for either the number of hospitals or patients enrolled per hospital, we were sufficiently powered. This is likely secondary to the fact that the population we studied was one that specifically reported pain, resulting in the larger differences seen.
Our study also had a number of limitations. First, the prescribing practices in countries other than the US are represented by only one hospital per country and, in some countries, by limited numbers of patients. While we studied four sites in the US, we did not have a site in the Northeast, a region previously shown to have lower prescribing rates.10 Additionally, patient samples for the US sites compared with the sites in other countries varied considerably with respect to ethnicity. While some studies in US patients have shown that opioid prescribing may vary based on race/ethnicity,34 we are uncertain as to how this might impact a study that crosses multiple countries. We also had a low number of patients receiving opioids prior to hospitalization for several of the non-US countries, which reduced the power to detect differences in this subgroup. Previous research has shown that there are wide variations in prescribing practices even within countries;10,12,18 therefore, caution should be taken when generalizing our findings. Second, we assessed analgesic prescribing patterns and pain control during the first 24 to 36 hours of hospitalization and did not consider hospital days beyond this timeframe with the exception of noting what medications were prescribed at discharge. We chose this methodology in an attempt to eliminate as many differences that might exist in the duration of hospitalization across many countries. Third, investigators in the study administered the survey, and respondents may have been affected by social desirability bias in how the survey questions were answered. Because investigators were not a part of the care team of any study patients, we believe this to be unlikely. Fourth, our study was conducted from October 8, 2013 to August 31, 2015 and the opioid epidemic is dynamic. Accordingly, our data may not reflect current opioid prescribing practices or patients’ current beliefs regarding pain control. Fifth, we did not collect demographic data on the patients who did not participate and could not look for systematic differences between participants and nonparticipants. Sixth, we relied on patients to self-report whether they were taking opioids prior to hospitalization or using illicit drugs. Seventh, we found comorbid mental health conditions to be more frequent in the US population studied. Previous work has shown regional variation in mental health conditions,35,36 which could have affected our findings. To account for this, our models included psychiatric illness.
CONCLUSIONS
Our data suggest that physicians in the US may prescribe opioids more frequently during patients’ hospitalizations and at discharge than their colleagues in other countries. We also found that patient satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Although the small number of hospitals included in our sample coupled with the small sample size in some of the non-US countries limits the generalizability of our findings, the data suggest that reducing the opioid epidemic in the US may require addressing patients’ expectations regarding pain control in addition to providers’ inpatient analgesic prescribing patterns.
Disclosures
The authors report no conflicts of interest.
Funding
The authors report no funding source for this work.
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16. Calcaterra SL, Scarbro S, Hull ML, et al. Prediction of future chronic opioid use Among hospitalized patients. J Gen Intern Med. 2018;33(6):898-905. https://doi.org/10.1007/s11606-018-4335-8.
17. Callister LC. Cultural influences on pain perceptions and behaviors. Home Health Care Manag Pract. 2003;15(3):207-211. https://doi.org/10.1177/1084822302250687.
18. Paulozzi LJ, Mack KA, Hockenberry JM. Vital signs: Variation among states in prescribing of opioid pain relievers and benzodiazepines--United States, 2012. J Saf Res. 2014;63(26):563-568. https://doi.org/10.1016/j.jsr.2014.09.001.
19. Callister LC, Khalaf I, Semenic S, Kartchner R, Vehvilainen-Julkunen K. The pain of childbirth: perceptions of culturally diverse women. Pain Manag Nurs. 2003;4(4):145-154. https://doi.org/10.1016/S1524-9042(03)00028-6.
20. Moore R, Brødsgaard I, Mao TK, Miller ML, Dworkin SF. Perceived need for local anesthesia in tooth drilling among Anglo-Americans, Chinese, and Scandinavians. Anesth Prog. 1998;45(1):22-28.
21. Kankkunen PM, Vehviläinen-Julkunen KM, Pietilä AM, et al. A tale of two countries: comparison of the perceptions of analgesics among Finnish and American parents. Pain Manag Nurs. 2008;9(3):113-119. https://doi.org/10.1016/j.pmn.2007.12.003.
22. Hanoch Y, Katsikopoulos KV, Gummerum M, Brass EP. American and German students’ knowledge, perceptions, and behaviors with respect to over-the-counter pain relievers. Health Psychol. 2007;26(6):802-806. https://doi.org/10.1037/0278-6133.26.6.802.
23. Manjiani D, Paul DB, Kunnumpurath S, Kaye AD, Vadivelu N. Availability and utilization of opioids for pain management: global issues. Ochsner J. 2014;14(2):208-215.
24. Quality improvement guidelines for the treatment of acute pain and cancer pain. JAMA. 1995;274(23):1874-1880.
25. McNeill JA, Sherwood GD, Starck PL, Thompson CJ. Assessing clinical outcomes: patient satisfaction with pain management. J Pain Symptom Manag. 1998;16(1):29-40. https://doi.org/10.1016/S0885-3924(98)00034-7.
26. Ferrari R, Novello C, Catania G, Visentin M. Patients’ satisfaction with pain management: the Italian version of the Patient Outcome Questionnaire of the American Pain Society. Recenti Prog Med. 2010;101(7–8):283-288.
27. Malouf J, Andión O, Torrubia R, Cañellas M, Baños JE. A survey of perceptions with pain management in Spanish inpatients. J Pain Symptom Manag. 2006;32(4):361-371. https://doi.org/10.1016/j.jpainsymman.2006.05.006.
28. Gordon DB, Polomano RC, Pellino TA, et al. Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for quality improvement of pain management in hospitalized adults: preliminary psychometric evaluation. J Pain. 2010;11(11):1172-1186. https://doi.org/10.1016/j.jpain.2010.02.012.
29. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000;25(24):3186-3191. https://doi.org/10.1097/00007632-200012150-00014.
30. Harris PA, Taylor R, Thielke R, et al. Research Electronic Data Capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
31. Duman F. After surgery in Germany, I wanted Vicodin, not herbal tea. New York Times. January 27, 2018. https://www.nytimes.com/2018/01/27/opinion/sunday/surgery-germany-vicodin.html. Accessed November 6, 2018.
32. Beaudoin FL, Banerjee GN, Mello MJ. State-level and system-level opioid prescribing policies: the impact on provider practices and overdose deaths, a systematic review. J Opioid Manag. 2016;12(2):109-118. https://doi.org/10.5055/jom.2016.0322.
33. Bao Y, Wen K, Johnson P, et al. Assessing the impact of state policies for prescription drug monitoring programs on high-risk opioid prescriptions. Health Aff (Millwood). 2018;37(10):1596-1604. https://doi.org/10.1377/hlthaff.2018.0512.
34. Friedman J, Kim D, Schneberk T, et al. Assessment of racial/ethnic and income disparities in the prescription of opioids and other controlled medications in California. JAMA Intern Med. 2019. https://doi.org/10.1001/jamainternmed.2018.6721.
35. Steel Z, Marnane C, Iranpour C, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. Int J Epidemiol. 2014;43(2):476-493. https://doi.org/10.1093/ije/dyu038.
36. Simon GE, Goldberg DP, Von Korff M, Ustün TB. Understanding cross-national differences in depression prevalence. Psychol Med. 2002;32(4):585-594. https://doi.org/10.1017/S0033291702005457.
Since 2000, the United States has seen a marked increase in opioid prescribing1-3 and opioid-related complications, including overdoses, hospitalizations, and deaths.2,4,5 A study from 2015 showed that more than one-third of the US civilian noninstitutionalized population reported receiving an opioid prescription in the prior year, with 12.5% reporting misuse, and, of those, 16.7% reported a prescription use disorder.6 While there has been a slight decrease in opioid prescriptions in the US since 2012, rates of opioid prescribing in 2015 were three times higher than in 1999 and approximately four times higher than in Europe in 2015.3,7
Pain is commonly reported by hospitalized patients,8,9 and opioids are often a mainstay of treatment;9,10 however, treatment with opioids can have a number of adverse outcomes.2,10,11 Short-term exposure to opioids can lead to long-term use,12-16 and patients on opioids are at an increased risk for subsequent hospitalization and longer inpatient lengths of stay.5
Physician prescribing practices for opioids and patient expectations for pain control vary as a function of geographic region and culture,10,12,17,18 and pain is influenced by the cultural context in which it occurs.17,19-22 Treatment of pain may also be affected by limited access to or restrictions on selected medications, as well as by cultural biases.23 Whether these variations in the treatment of pain are reflected in patients’ satisfaction with pain control is uncertain.
We sought to compare the inpatient analgesic prescribing practices and patients’ perceptions of pain control for medical patients in four teaching hospitals in the US and in seven teaching hospitals in seven other countries.
METHODS
Study Design
We utilized a cross-sectional, observational design. The study was approved by the Institutional Review Boards at all participating sites.
Setting
The study was conducted at 11 academic hospitals in eight countries from October 8, 2013 to August 31, 2015. Sites in the US included Denver Health in Denver, Colorado; the University of Colorado Hospital in Aurora, Colorado; Hennepin Healthcare in Minneapolis, Minnesota; and Legacy Health in Portland, Oregon. Sites outside the US included McMaster University in Hamilton, Ontario, Canada; Hospital de la Santa Creu i Sant Pau, Universitat Autonòma de Barcelona in Barcelona, Spain; the University of Study of Milan and the University Ospedale “Luigi Sacco” in Milan, Italy, the National Taiwan University Hospital, in Taipei, Taiwan, the University of Ulsan College of Medicine, Asan Medical Center, in Seoul, Korea, the Imperial College, Chelsea and Westminster Hospital, in London, United Kingdom and Dunedin Hospital, Dunedin, New Zealand.
Inclusion and Exclusion Criteria
We included patients 18-89 years of age (20-89 in Taiwan because patients under 20 years of age in this country are a restricted group with respect to participating in research), admitted to an internal medicine service from the Emergency Department or Urgent Care clinic with an acute illness for a minimum of 24 hours (with time zero defined as the time care was initiated in the Emergency Department or Urgent Care Clinic), who reported pain at some time during the first 24-36 hours of their hospitalization and who provided informed consent. In the US, “admission” included both observation and inpatient status. We limited the patient population to those admitted via emergency departments and urgent care clinics in order to enroll similar patient populations across sites.
Scheduled admissions, patients transferred from an outside facility, patients admitted directly from a clinic, and those receiving care in intensive care units were excluded. We also excluded patients who were incarcerated, pregnant, those who received major surgery within the previous 14 days, those with a known diagnosis of active cancer, and those who were receiving palliative or hospice care. Patients receiving care from an investigator in the study at the time of enrollment were not eligible due to the potential conflict of interest.
Patient Screening
Primary teams were contacted to determine if any patients on their service might meet the criteria for inclusion in the study on preselected study days chosen on the basis of the research team’s availability. Identified patients were then screened to establish if they met the eligibility criteria. Patients were asked directly if they had experienced pain during their preadmission evaluation or during their hospitalization.
Data Collection
All patients were hospitalized at the time they gave consent and when data were collected. Data were collected via interviews with patients, as well as through chart review. We recorded patients’ age, gender, race, admitting diagnosis(es), length of stay, psychiatric illness, illicit drug use, whether they reported receiving opioid analgesics at the time of hospitalization, whether they were prescribed opioids and/or nonopioid analgesics during their hospitalization, the median and maximum doses of opioids prescribed and dispensed, and whether they were discharged on opioids. The question of illicit drug use was asked of all patients with the exception of those hospitalized in South Korea due to potential legal implications.
Opioid prescribing and receipt of opioids was recorded based upon current provider orders and medication administration records, respectively. Perception of and satisfaction with pain control was assessed with the American Pain Society Patient Outcome Questionnaire–Modified (APS-POQ-Modified).24,25 Versions of this survey have been validated in English as well as in other languages and cultures.26-28 Because hospitalization practices could differ across hospitals and in different countries, we compared patients’ severity of illness by using Charlson comorbidity scores. Consent forms and the APS-POQ were translated into each country’s primary language according to established processes.29 The survey was filled out by having site investigators read questions aloud and by use of a large-font visual analog scale to aid patients’ verbal responses.
Data were collected and managed using a secure, web-based application electronic data capture tool (Research Electronic Data Capture [REDCap], Nashville, Tennessee), hosted at Denver Health.30
Study Size
Preliminary data from the internal medicine units at our institution suggested that 40% of patients without cancer received opioid analgesics during their hospitalization. Assuming 90% power to detect an absolute difference in the proportion of inpatient medical patients who are receiving opioid analgesics during their hospital stay of 17%, a two-sided type 1 error rate of 0.05, six hospitals in the US, and nine hospitals from all other countries, we calculated an initial sample size of 150 patients per site. This sample size was considered feasible for enrollment in a busy inpatient clinical setting. Study end points were to either reach the goal number of patients (150 per site) or the predetermined study end date, whichever came first.
Data Analysis
We generated means with standard deviations (SDs) and medians with interquartile ranges (IQRs) for normally and nonnormally distributed continuous variables, respectively, and frequencies for categorical variables. We used linear mixed modeling for the analysis of continuous variables. For binary outcomes, our data were fitted to a generalized linear mixed model with logit as the link function and a binary distribution. For ordinal variables, specifically patient-reported satisfaction with pain control and the opinion statements, the data were fitted to a generalized linear mixed model with a cumulative logit link and a multinomial distribution. Hospital was included as a random effect in all models to account for patients cared for in the same hospital.
Country of origin, dichotomized as US or non-US, was the independent variable of interest for all models. An interaction term for exposure to opioids prior to admission and country was entered into all models to explore whether differences in the effect of country existed for patients who reported taking opioids prior to admission and those who did not.
The models for the frequency with which analgesics were given, doses of opioids given during hospitalization and at discharge, patient-reported pain score, and patient-reported satisfaction with pain control were adjusted for (1) age, (2) gender, (3) Charlson Comorbidity Index, (4) length of stay, (5) history of illicit drug use, (6) history of psychiatric illness, (7) daily dose in morphine milligram equivalents (MME) for opioids prior to admission, (8) average pain score, and (9) hospital. The patient-reported satisfaction with pain control model was also adjusted for whether or not opioids were given to the patient during their hospitalization. P < .05 was considered to indicate significance. All analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute, Inc., Cary, North Carolina). We reported data on medications that were prescribed and dispensed (as opposed to just prescribed and not necessarily given). Opioids prescribed at discharge represented the total possible opioids that could be given based upon the order/prescription (eg, oxycodone 5 mg every 6 hours as needed for pain would be counted as 20 mg/24 hours maximum possible dose followed by conversion to MME).
Missing Data
When there were missing data, a query was sent to sites to verify if the data were retrievable. If retrievable, the data were then entered. Data were missing in 5% and 2% of patients who did or did not report taking an opioid prior to admission, respectively. If a variable was included in a specific statistical test, then subjects with missing data were excluded from that analysis (ie, complete case analysis).
RESULTS
We approached 1,309 eligible patients, of which 981 provided informed consent, for a response rate of 75%; 503 from the US and 478 patients from other countries (Figure). In unadjusted analyses, we found no significant differences between US and non-US patients in age (mean age 51, SD 15 vs 59, SD 19; P = .30), race, ethnicity, or Charlson comorbidity index scores (median 2, IQR 1-3 vs 3, IQR 1-4; P = .45). US patients had shorter lengths of stay (median 3 days, IQR 2-4 vs 6 days, IQR 3-11; P = .04), a more frequent history of illicit drug use (33% vs 6%; P = .003), a higher frequency of psychiatric illness (27% vs 8%; P < .0001), and more were receiving opioid analgesics prior to admission (38% vs 17%; P = .007) than those hospitalized in other countries (Table 1, Appendix 1). The primary admitting diagnoses for all patients in the study are listed in Appendix 2. Opioid prescribing practices across the individual sites are shown in Appendix 3.
Patients Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found that more patients in the US were given opioids during their hospitalization and in higher doses than patients from other countries and more were prescribed opioids at discharge. Fewer patients in the US were dispensed nonopioid analgesics during their hospitalization than patients from other countries, but this difference was not significant (Table 2). Appendix 4 shows the types of nonopioid pain medications prescribed in the US and other countries.
After adjustment for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys. We found no significant difference in satisfaction with pain control between patients from the US and other countries in the models, regardless of whether we included average pain score or opioid receipt during hospitalization in the model (Table 3).
In unadjusted analyses, compared with patients hospitalized in other countries, more patients in the US stated that they would like a stronger dose of analgesic if they were still in pain, though the difference was nonsignificant, and US patients were more likely to agree with the statement that people become addicted to pain medication easily and less likely to agree with the statement that it is easier to endure pain than deal with the side effects of pain medications (Table 3).
Patients Not Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found no significant difference in the proportion of US patients provided with nonopioid pain medications during their hospitalization compared with patients in other countries, but a greater percentage of US patients were given opioids during their hospitalization and at discharge and in higher doses (Table 2).
After adjusting for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys and greater pain severity in the 24-36 hours prior to completing the survey than patients from other countries, but we found no difference in patient satisfaction with pain control (Table 3). After we included the average pain score and whether or not opioids were given to the patient during their hospitalization in this model, patients in the US were more likely to report a higher level of satisfaction with pain control than patients in all other countries (P = .001).
In unadjusted analyses, compared with patients hospitalized in other countries, those in the US were less likely to agree with the statement that good patients avoid talking about pain (Table 3).
Patient Satisfaction and Opioid Receipt
Among patients cared for in the US, after controlling for the average pain score, we did not find a significant association between receiving opioids while in the hospital and satisfaction with pain control for patients who either did or did not endorse taking opioids prior to admission (P = .38 and P = .24, respectively). Among patients cared for in all other countries, after controlling for the average pain score, we found a significant association between receiving opioids while in the hospital and a lower level of satisfaction with pain control for patients who reported taking opioids prior to admission (P = .02) but not for patients who did not report taking opioids prior to admission (P = .08).
DISCUSSION
Compared with patients hospitalized in other countries, a greater percentage of those hospitalized in the US were prescribed opioid analgesics both during hospitalization and at the time of discharge, even after adjustment for pain severity. In addition, patients hospitalized in the US reported greater pain severity at the time they completed their pain surveys and in the 24 to 36 hours prior to completing the survey than patients from other countries. In this sample, satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Our study also suggests that opioid receipt did not lead to improved patient satisfaction with pain control.
The frequency with which we observed opioid analgesics being prescribed during hospitalization in US hospitals (79%) was higher than the 51% of patients who received opioids reported by Herzig and colleagues.10 Patients in our study had a higher prevalence of illicit drug abuse and psychiatric illness, and our study only included patients who reported pain at some point during their hospitalization. We also studied prescribing practices through analysis of provider orders and medication administration records at the time the patient was hospitalized.
While we observed that physicians in the US more frequently prescribed opioid analgesics during hospitalizations than physicians working in other countries, we also observed that patients in the US reported higher levels of pain during their hospitalization. After adjusting for a number of variables, including pain severity, however, we still found that opioids were more commonly prescribed during hospitalizations by physicians working in the US sites studied than by physicians in the non-US sites.
Opioid prescribing practices varied across the sites sampled in our study. While the US sites, Taiwan, and Korea tended to be heavier utilizers of opioids during hospitalization, there were notable differences in discharge prescribing of opioids, with the US sites more commonly prescribing opioids and higher MME for patients who did not report taking opioids prior to their hospitalization (Appendix 3). A sensitivity analysis was conducted excluding South Korea from modeling, given that patients there were not asked about illicit opioid use. There were no important changes in the magnitude or direction of the results.
Our study supports previous studies indicating that there are cultural and societal differences when it comes to the experience of pain and the expectations around pain control.17,20-22,31 Much of the focus on reducing opioid utilization has been on provider practices32 and on prescription drug monitoring programs.33 Our findings suggest that another area of focus that may be important in mitigating the opioid epidemic is patient expectations of pain control.
Our study has a number of strengths. First, we included 11 hospitals from eight different countries. Second, we believe this is the first study to assess opioid prescribing and dispensing practices during hospitalization as well as at the time of discharge. Third, patient perceptions of pain control were assessed in conjunction with analgesic prescribing and were assessed during hospitalization. Fourth, we had high response rates for patient participation in our study. Fifth, we found much larger differences in opioid prescribing than anticipated, and thus, while we did not achieve the sample size originally planned for either the number of hospitals or patients enrolled per hospital, we were sufficiently powered. This is likely secondary to the fact that the population we studied was one that specifically reported pain, resulting in the larger differences seen.
Our study also had a number of limitations. First, the prescribing practices in countries other than the US are represented by only one hospital per country and, in some countries, by limited numbers of patients. While we studied four sites in the US, we did not have a site in the Northeast, a region previously shown to have lower prescribing rates.10 Additionally, patient samples for the US sites compared with the sites in other countries varied considerably with respect to ethnicity. While some studies in US patients have shown that opioid prescribing may vary based on race/ethnicity,34 we are uncertain as to how this might impact a study that crosses multiple countries. We also had a low number of patients receiving opioids prior to hospitalization for several of the non-US countries, which reduced the power to detect differences in this subgroup. Previous research has shown that there are wide variations in prescribing practices even within countries;10,12,18 therefore, caution should be taken when generalizing our findings. Second, we assessed analgesic prescribing patterns and pain control during the first 24 to 36 hours of hospitalization and did not consider hospital days beyond this timeframe with the exception of noting what medications were prescribed at discharge. We chose this methodology in an attempt to eliminate as many differences that might exist in the duration of hospitalization across many countries. Third, investigators in the study administered the survey, and respondents may have been affected by social desirability bias in how the survey questions were answered. Because investigators were not a part of the care team of any study patients, we believe this to be unlikely. Fourth, our study was conducted from October 8, 2013 to August 31, 2015 and the opioid epidemic is dynamic. Accordingly, our data may not reflect current opioid prescribing practices or patients’ current beliefs regarding pain control. Fifth, we did not collect demographic data on the patients who did not participate and could not look for systematic differences between participants and nonparticipants. Sixth, we relied on patients to self-report whether they were taking opioids prior to hospitalization or using illicit drugs. Seventh, we found comorbid mental health conditions to be more frequent in the US population studied. Previous work has shown regional variation in mental health conditions,35,36 which could have affected our findings. To account for this, our models included psychiatric illness.
CONCLUSIONS
Our data suggest that physicians in the US may prescribe opioids more frequently during patients’ hospitalizations and at discharge than their colleagues in other countries. We also found that patient satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Although the small number of hospitals included in our sample coupled with the small sample size in some of the non-US countries limits the generalizability of our findings, the data suggest that reducing the opioid epidemic in the US may require addressing patients’ expectations regarding pain control in addition to providers’ inpatient analgesic prescribing patterns.
Disclosures
The authors report no conflicts of interest.
Funding
The authors report no funding source for this work.
Since 2000, the United States has seen a marked increase in opioid prescribing1-3 and opioid-related complications, including overdoses, hospitalizations, and deaths.2,4,5 A study from 2015 showed that more than one-third of the US civilian noninstitutionalized population reported receiving an opioid prescription in the prior year, with 12.5% reporting misuse, and, of those, 16.7% reported a prescription use disorder.6 While there has been a slight decrease in opioid prescriptions in the US since 2012, rates of opioid prescribing in 2015 were three times higher than in 1999 and approximately four times higher than in Europe in 2015.3,7
Pain is commonly reported by hospitalized patients,8,9 and opioids are often a mainstay of treatment;9,10 however, treatment with opioids can have a number of adverse outcomes.2,10,11 Short-term exposure to opioids can lead to long-term use,12-16 and patients on opioids are at an increased risk for subsequent hospitalization and longer inpatient lengths of stay.5
Physician prescribing practices for opioids and patient expectations for pain control vary as a function of geographic region and culture,10,12,17,18 and pain is influenced by the cultural context in which it occurs.17,19-22 Treatment of pain may also be affected by limited access to or restrictions on selected medications, as well as by cultural biases.23 Whether these variations in the treatment of pain are reflected in patients’ satisfaction with pain control is uncertain.
We sought to compare the inpatient analgesic prescribing practices and patients’ perceptions of pain control for medical patients in four teaching hospitals in the US and in seven teaching hospitals in seven other countries.
METHODS
Study Design
We utilized a cross-sectional, observational design. The study was approved by the Institutional Review Boards at all participating sites.
Setting
The study was conducted at 11 academic hospitals in eight countries from October 8, 2013 to August 31, 2015. Sites in the US included Denver Health in Denver, Colorado; the University of Colorado Hospital in Aurora, Colorado; Hennepin Healthcare in Minneapolis, Minnesota; and Legacy Health in Portland, Oregon. Sites outside the US included McMaster University in Hamilton, Ontario, Canada; Hospital de la Santa Creu i Sant Pau, Universitat Autonòma de Barcelona in Barcelona, Spain; the University of Study of Milan and the University Ospedale “Luigi Sacco” in Milan, Italy, the National Taiwan University Hospital, in Taipei, Taiwan, the University of Ulsan College of Medicine, Asan Medical Center, in Seoul, Korea, the Imperial College, Chelsea and Westminster Hospital, in London, United Kingdom and Dunedin Hospital, Dunedin, New Zealand.
Inclusion and Exclusion Criteria
We included patients 18-89 years of age (20-89 in Taiwan because patients under 20 years of age in this country are a restricted group with respect to participating in research), admitted to an internal medicine service from the Emergency Department or Urgent Care clinic with an acute illness for a minimum of 24 hours (with time zero defined as the time care was initiated in the Emergency Department or Urgent Care Clinic), who reported pain at some time during the first 24-36 hours of their hospitalization and who provided informed consent. In the US, “admission” included both observation and inpatient status. We limited the patient population to those admitted via emergency departments and urgent care clinics in order to enroll similar patient populations across sites.
Scheduled admissions, patients transferred from an outside facility, patients admitted directly from a clinic, and those receiving care in intensive care units were excluded. We also excluded patients who were incarcerated, pregnant, those who received major surgery within the previous 14 days, those with a known diagnosis of active cancer, and those who were receiving palliative or hospice care. Patients receiving care from an investigator in the study at the time of enrollment were not eligible due to the potential conflict of interest.
Patient Screening
Primary teams were contacted to determine if any patients on their service might meet the criteria for inclusion in the study on preselected study days chosen on the basis of the research team’s availability. Identified patients were then screened to establish if they met the eligibility criteria. Patients were asked directly if they had experienced pain during their preadmission evaluation or during their hospitalization.
Data Collection
All patients were hospitalized at the time they gave consent and when data were collected. Data were collected via interviews with patients, as well as through chart review. We recorded patients’ age, gender, race, admitting diagnosis(es), length of stay, psychiatric illness, illicit drug use, whether they reported receiving opioid analgesics at the time of hospitalization, whether they were prescribed opioids and/or nonopioid analgesics during their hospitalization, the median and maximum doses of opioids prescribed and dispensed, and whether they were discharged on opioids. The question of illicit drug use was asked of all patients with the exception of those hospitalized in South Korea due to potential legal implications.
Opioid prescribing and receipt of opioids was recorded based upon current provider orders and medication administration records, respectively. Perception of and satisfaction with pain control was assessed with the American Pain Society Patient Outcome Questionnaire–Modified (APS-POQ-Modified).24,25 Versions of this survey have been validated in English as well as in other languages and cultures.26-28 Because hospitalization practices could differ across hospitals and in different countries, we compared patients’ severity of illness by using Charlson comorbidity scores. Consent forms and the APS-POQ were translated into each country’s primary language according to established processes.29 The survey was filled out by having site investigators read questions aloud and by use of a large-font visual analog scale to aid patients’ verbal responses.
Data were collected and managed using a secure, web-based application electronic data capture tool (Research Electronic Data Capture [REDCap], Nashville, Tennessee), hosted at Denver Health.30
Study Size
Preliminary data from the internal medicine units at our institution suggested that 40% of patients without cancer received opioid analgesics during their hospitalization. Assuming 90% power to detect an absolute difference in the proportion of inpatient medical patients who are receiving opioid analgesics during their hospital stay of 17%, a two-sided type 1 error rate of 0.05, six hospitals in the US, and nine hospitals from all other countries, we calculated an initial sample size of 150 patients per site. This sample size was considered feasible for enrollment in a busy inpatient clinical setting. Study end points were to either reach the goal number of patients (150 per site) or the predetermined study end date, whichever came first.
Data Analysis
We generated means with standard deviations (SDs) and medians with interquartile ranges (IQRs) for normally and nonnormally distributed continuous variables, respectively, and frequencies for categorical variables. We used linear mixed modeling for the analysis of continuous variables. For binary outcomes, our data were fitted to a generalized linear mixed model with logit as the link function and a binary distribution. For ordinal variables, specifically patient-reported satisfaction with pain control and the opinion statements, the data were fitted to a generalized linear mixed model with a cumulative logit link and a multinomial distribution. Hospital was included as a random effect in all models to account for patients cared for in the same hospital.
Country of origin, dichotomized as US or non-US, was the independent variable of interest for all models. An interaction term for exposure to opioids prior to admission and country was entered into all models to explore whether differences in the effect of country existed for patients who reported taking opioids prior to admission and those who did not.
The models for the frequency with which analgesics were given, doses of opioids given during hospitalization and at discharge, patient-reported pain score, and patient-reported satisfaction with pain control were adjusted for (1) age, (2) gender, (3) Charlson Comorbidity Index, (4) length of stay, (5) history of illicit drug use, (6) history of psychiatric illness, (7) daily dose in morphine milligram equivalents (MME) for opioids prior to admission, (8) average pain score, and (9) hospital. The patient-reported satisfaction with pain control model was also adjusted for whether or not opioids were given to the patient during their hospitalization. P < .05 was considered to indicate significance. All analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute, Inc., Cary, North Carolina). We reported data on medications that were prescribed and dispensed (as opposed to just prescribed and not necessarily given). Opioids prescribed at discharge represented the total possible opioids that could be given based upon the order/prescription (eg, oxycodone 5 mg every 6 hours as needed for pain would be counted as 20 mg/24 hours maximum possible dose followed by conversion to MME).
Missing Data
When there were missing data, a query was sent to sites to verify if the data were retrievable. If retrievable, the data were then entered. Data were missing in 5% and 2% of patients who did or did not report taking an opioid prior to admission, respectively. If a variable was included in a specific statistical test, then subjects with missing data were excluded from that analysis (ie, complete case analysis).
RESULTS
We approached 1,309 eligible patients, of which 981 provided informed consent, for a response rate of 75%; 503 from the US and 478 patients from other countries (Figure). In unadjusted analyses, we found no significant differences between US and non-US patients in age (mean age 51, SD 15 vs 59, SD 19; P = .30), race, ethnicity, or Charlson comorbidity index scores (median 2, IQR 1-3 vs 3, IQR 1-4; P = .45). US patients had shorter lengths of stay (median 3 days, IQR 2-4 vs 6 days, IQR 3-11; P = .04), a more frequent history of illicit drug use (33% vs 6%; P = .003), a higher frequency of psychiatric illness (27% vs 8%; P < .0001), and more were receiving opioid analgesics prior to admission (38% vs 17%; P = .007) than those hospitalized in other countries (Table 1, Appendix 1). The primary admitting diagnoses for all patients in the study are listed in Appendix 2. Opioid prescribing practices across the individual sites are shown in Appendix 3.
Patients Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found that more patients in the US were given opioids during their hospitalization and in higher doses than patients from other countries and more were prescribed opioids at discharge. Fewer patients in the US were dispensed nonopioid analgesics during their hospitalization than patients from other countries, but this difference was not significant (Table 2). Appendix 4 shows the types of nonopioid pain medications prescribed in the US and other countries.
After adjustment for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys. We found no significant difference in satisfaction with pain control between patients from the US and other countries in the models, regardless of whether we included average pain score or opioid receipt during hospitalization in the model (Table 3).
In unadjusted analyses, compared with patients hospitalized in other countries, more patients in the US stated that they would like a stronger dose of analgesic if they were still in pain, though the difference was nonsignificant, and US patients were more likely to agree with the statement that people become addicted to pain medication easily and less likely to agree with the statement that it is easier to endure pain than deal with the side effects of pain medications (Table 3).
Patients Not Taking Opioids Prior to Admission
After adjusting for relevant covariates, we found no significant difference in the proportion of US patients provided with nonopioid pain medications during their hospitalization compared with patients in other countries, but a greater percentage of US patients were given opioids during their hospitalization and at discharge and in higher doses (Table 2).
After adjusting for relevant covariates, US patients reported greater pain severity at the time they completed their pain surveys and greater pain severity in the 24-36 hours prior to completing the survey than patients from other countries, but we found no difference in patient satisfaction with pain control (Table 3). After we included the average pain score and whether or not opioids were given to the patient during their hospitalization in this model, patients in the US were more likely to report a higher level of satisfaction with pain control than patients in all other countries (P = .001).
In unadjusted analyses, compared with patients hospitalized in other countries, those in the US were less likely to agree with the statement that good patients avoid talking about pain (Table 3).
Patient Satisfaction and Opioid Receipt
Among patients cared for in the US, after controlling for the average pain score, we did not find a significant association between receiving opioids while in the hospital and satisfaction with pain control for patients who either did or did not endorse taking opioids prior to admission (P = .38 and P = .24, respectively). Among patients cared for in all other countries, after controlling for the average pain score, we found a significant association between receiving opioids while in the hospital and a lower level of satisfaction with pain control for patients who reported taking opioids prior to admission (P = .02) but not for patients who did not report taking opioids prior to admission (P = .08).
DISCUSSION
Compared with patients hospitalized in other countries, a greater percentage of those hospitalized in the US were prescribed opioid analgesics both during hospitalization and at the time of discharge, even after adjustment for pain severity. In addition, patients hospitalized in the US reported greater pain severity at the time they completed their pain surveys and in the 24 to 36 hours prior to completing the survey than patients from other countries. In this sample, satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Our study also suggests that opioid receipt did not lead to improved patient satisfaction with pain control.
The frequency with which we observed opioid analgesics being prescribed during hospitalization in US hospitals (79%) was higher than the 51% of patients who received opioids reported by Herzig and colleagues.10 Patients in our study had a higher prevalence of illicit drug abuse and psychiatric illness, and our study only included patients who reported pain at some point during their hospitalization. We also studied prescribing practices through analysis of provider orders and medication administration records at the time the patient was hospitalized.
While we observed that physicians in the US more frequently prescribed opioid analgesics during hospitalizations than physicians working in other countries, we also observed that patients in the US reported higher levels of pain during their hospitalization. After adjusting for a number of variables, including pain severity, however, we still found that opioids were more commonly prescribed during hospitalizations by physicians working in the US sites studied than by physicians in the non-US sites.
Opioid prescribing practices varied across the sites sampled in our study. While the US sites, Taiwan, and Korea tended to be heavier utilizers of opioids during hospitalization, there were notable differences in discharge prescribing of opioids, with the US sites more commonly prescribing opioids and higher MME for patients who did not report taking opioids prior to their hospitalization (Appendix 3). A sensitivity analysis was conducted excluding South Korea from modeling, given that patients there were not asked about illicit opioid use. There were no important changes in the magnitude or direction of the results.
Our study supports previous studies indicating that there are cultural and societal differences when it comes to the experience of pain and the expectations around pain control.17,20-22,31 Much of the focus on reducing opioid utilization has been on provider practices32 and on prescription drug monitoring programs.33 Our findings suggest that another area of focus that may be important in mitigating the opioid epidemic is patient expectations of pain control.
Our study has a number of strengths. First, we included 11 hospitals from eight different countries. Second, we believe this is the first study to assess opioid prescribing and dispensing practices during hospitalization as well as at the time of discharge. Third, patient perceptions of pain control were assessed in conjunction with analgesic prescribing and were assessed during hospitalization. Fourth, we had high response rates for patient participation in our study. Fifth, we found much larger differences in opioid prescribing than anticipated, and thus, while we did not achieve the sample size originally planned for either the number of hospitals or patients enrolled per hospital, we were sufficiently powered. This is likely secondary to the fact that the population we studied was one that specifically reported pain, resulting in the larger differences seen.
Our study also had a number of limitations. First, the prescribing practices in countries other than the US are represented by only one hospital per country and, in some countries, by limited numbers of patients. While we studied four sites in the US, we did not have a site in the Northeast, a region previously shown to have lower prescribing rates.10 Additionally, patient samples for the US sites compared with the sites in other countries varied considerably with respect to ethnicity. While some studies in US patients have shown that opioid prescribing may vary based on race/ethnicity,34 we are uncertain as to how this might impact a study that crosses multiple countries. We also had a low number of patients receiving opioids prior to hospitalization for several of the non-US countries, which reduced the power to detect differences in this subgroup. Previous research has shown that there are wide variations in prescribing practices even within countries;10,12,18 therefore, caution should be taken when generalizing our findings. Second, we assessed analgesic prescribing patterns and pain control during the first 24 to 36 hours of hospitalization and did not consider hospital days beyond this timeframe with the exception of noting what medications were prescribed at discharge. We chose this methodology in an attempt to eliminate as many differences that might exist in the duration of hospitalization across many countries. Third, investigators in the study administered the survey, and respondents may have been affected by social desirability bias in how the survey questions were answered. Because investigators were not a part of the care team of any study patients, we believe this to be unlikely. Fourth, our study was conducted from October 8, 2013 to August 31, 2015 and the opioid epidemic is dynamic. Accordingly, our data may not reflect current opioid prescribing practices or patients’ current beliefs regarding pain control. Fifth, we did not collect demographic data on the patients who did not participate and could not look for systematic differences between participants and nonparticipants. Sixth, we relied on patients to self-report whether they were taking opioids prior to hospitalization or using illicit drugs. Seventh, we found comorbid mental health conditions to be more frequent in the US population studied. Previous work has shown regional variation in mental health conditions,35,36 which could have affected our findings. To account for this, our models included psychiatric illness.
CONCLUSIONS
Our data suggest that physicians in the US may prescribe opioids more frequently during patients’ hospitalizations and at discharge than their colleagues in other countries. We also found that patient satisfaction, beliefs, and expectations about pain control differed between patients in the US and other sites. Although the small number of hospitals included in our sample coupled with the small sample size in some of the non-US countries limits the generalizability of our findings, the data suggest that reducing the opioid epidemic in the US may require addressing patients’ expectations regarding pain control in addition to providers’ inpatient analgesic prescribing patterns.
Disclosures
The authors report no conflicts of interest.
Funding
The authors report no funding source for this work.
1. Pletcher MJ, Kertesz SG, Kohn MA, Gonzales R. Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. JAMA. 2008;299(1):70-78. https://doi.org/10.1001/jama.2007.64.
2. Herzig SJ. Growing concerns regarding long-term opioid use: the hospitalization hazard. J Hosp Med. 2015;10(7):469-470. https://doi.org/10.1002/jhm.2369.
3. Guy GP Jr, Zhang K, Bohm MK, et al. Vital Signs: changes in opioid prescribing in the United States, 2006–2015. MMWR Morb Mortal Wkly Rep. 2017;66(26):697-704. https://doi.org/10.15585/mmwr.mm6626a4.
4. Okie S. A flood of opioids, a rising tide of deaths. N Engl J Med. 2010;363(21):1981-1985. https://doi.org/10.1056/NEJMp1011512.
5. Liang Y, Turner BJ. National cohort study of opioid analgesic dose and risk of future hospitalization. J Hosp Med. 2015;10(7):425-431. https://doi.org/10.1002/jhm.2350.
6. Han B, Compton WM, Blanco C, et al. Prescription opioid use, misuse, and use disorders in U.S. Adults: 2015 national survey on drug use and health. Ann Intern Med. 2017;167(5):293-301. https://doi.org/10.7326/M17-0865.
7. Schuchat A, Houry D, Guy GP, Jr. New data on opioid use and prescribing in the United States. JAMA. 2017;318(5):425-426. https://doi.org/10.1001/jama.2017.8913.
8. Sawyer J, Haslam L, Robinson S, Daines P, Stilos K. Pain prevalence study in a large Canadian teaching hospital. Pain Manag Nurs. 2008;9(3):104-112. https://doi.org/10.1016/j.pmn.2008.02.001.
9. Gupta A, Daigle S, Mojica J, Hurley RW. Patient perception of pain care in hospitals in the United States. J Pain Res. 2009;2:157-164. https://doi.org/10.2147/JPR.S7903.
10. Herzig SJ, Rothberg MB, Cheung M, Ngo LH, Marcantonio ER. Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals. J Hosp Med. 2014;9(2):73-81. https://doi.org/10.1002/jhm.2102.
11. Kanjanarat P, Winterstein AG, Johns TE, et al. Nature of preventable adverse drug events in hospitals: a literature review. Am J Health Syst Pharm. 2003;60(17):1750-1759. https://doi.org/10.1093/ajhp/60.17.1750.
12. Jena AB, Goldman D, Karaca-Mandic P. Hospital prescribing of opioids to medicare beneficiaries. JAMA Intern Med. 2016;176(7):990-997. https://doi.org/10.1001/jamainternmed.2016.2737.
13. Hooten WM, St Sauver JL, McGree ME, Jacobson DJ, Warner DO. Incidence and risk factors for progression From short-term to episodic or long-term opioid prescribing: A population-based study. Mayo Clin Proc. 2015;90(7):850-856. https://doi.org/10.1016/j.mayocp.2015.04.012.
14. Alam A, Gomes T, Zheng H, et al. Long-term analgesic use after low-risk surgery: a retrospective cohort study. Arch Intern Med. 2012;172(5):425-430. https://doi.org/10.1001/archinternmed.2011.1827.
15. Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med. 2017;376(7):663-673. https://doi.org/10.1056/NEJMsa1610524.
16. Calcaterra SL, Scarbro S, Hull ML, et al. Prediction of future chronic opioid use Among hospitalized patients. J Gen Intern Med. 2018;33(6):898-905. https://doi.org/10.1007/s11606-018-4335-8.
17. Callister LC. Cultural influences on pain perceptions and behaviors. Home Health Care Manag Pract. 2003;15(3):207-211. https://doi.org/10.1177/1084822302250687.
18. Paulozzi LJ, Mack KA, Hockenberry JM. Vital signs: Variation among states in prescribing of opioid pain relievers and benzodiazepines--United States, 2012. J Saf Res. 2014;63(26):563-568. https://doi.org/10.1016/j.jsr.2014.09.001.
19. Callister LC, Khalaf I, Semenic S, Kartchner R, Vehvilainen-Julkunen K. The pain of childbirth: perceptions of culturally diverse women. Pain Manag Nurs. 2003;4(4):145-154. https://doi.org/10.1016/S1524-9042(03)00028-6.
20. Moore R, Brødsgaard I, Mao TK, Miller ML, Dworkin SF. Perceived need for local anesthesia in tooth drilling among Anglo-Americans, Chinese, and Scandinavians. Anesth Prog. 1998;45(1):22-28.
21. Kankkunen PM, Vehviläinen-Julkunen KM, Pietilä AM, et al. A tale of two countries: comparison of the perceptions of analgesics among Finnish and American parents. Pain Manag Nurs. 2008;9(3):113-119. https://doi.org/10.1016/j.pmn.2007.12.003.
22. Hanoch Y, Katsikopoulos KV, Gummerum M, Brass EP. American and German students’ knowledge, perceptions, and behaviors with respect to over-the-counter pain relievers. Health Psychol. 2007;26(6):802-806. https://doi.org/10.1037/0278-6133.26.6.802.
23. Manjiani D, Paul DB, Kunnumpurath S, Kaye AD, Vadivelu N. Availability and utilization of opioids for pain management: global issues. Ochsner J. 2014;14(2):208-215.
24. Quality improvement guidelines for the treatment of acute pain and cancer pain. JAMA. 1995;274(23):1874-1880.
25. McNeill JA, Sherwood GD, Starck PL, Thompson CJ. Assessing clinical outcomes: patient satisfaction with pain management. J Pain Symptom Manag. 1998;16(1):29-40. https://doi.org/10.1016/S0885-3924(98)00034-7.
26. Ferrari R, Novello C, Catania G, Visentin M. Patients’ satisfaction with pain management: the Italian version of the Patient Outcome Questionnaire of the American Pain Society. Recenti Prog Med. 2010;101(7–8):283-288.
27. Malouf J, Andión O, Torrubia R, Cañellas M, Baños JE. A survey of perceptions with pain management in Spanish inpatients. J Pain Symptom Manag. 2006;32(4):361-371. https://doi.org/10.1016/j.jpainsymman.2006.05.006.
28. Gordon DB, Polomano RC, Pellino TA, et al. Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for quality improvement of pain management in hospitalized adults: preliminary psychometric evaluation. J Pain. 2010;11(11):1172-1186. https://doi.org/10.1016/j.jpain.2010.02.012.
29. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000;25(24):3186-3191. https://doi.org/10.1097/00007632-200012150-00014.
30. Harris PA, Taylor R, Thielke R, et al. Research Electronic Data Capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
31. Duman F. After surgery in Germany, I wanted Vicodin, not herbal tea. New York Times. January 27, 2018. https://www.nytimes.com/2018/01/27/opinion/sunday/surgery-germany-vicodin.html. Accessed November 6, 2018.
32. Beaudoin FL, Banerjee GN, Mello MJ. State-level and system-level opioid prescribing policies: the impact on provider practices and overdose deaths, a systematic review. J Opioid Manag. 2016;12(2):109-118. https://doi.org/10.5055/jom.2016.0322.
33. Bao Y, Wen K, Johnson P, et al. Assessing the impact of state policies for prescription drug monitoring programs on high-risk opioid prescriptions. Health Aff (Millwood). 2018;37(10):1596-1604. https://doi.org/10.1377/hlthaff.2018.0512.
34. Friedman J, Kim D, Schneberk T, et al. Assessment of racial/ethnic and income disparities in the prescription of opioids and other controlled medications in California. JAMA Intern Med. 2019. https://doi.org/10.1001/jamainternmed.2018.6721.
35. Steel Z, Marnane C, Iranpour C, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. Int J Epidemiol. 2014;43(2):476-493. https://doi.org/10.1093/ije/dyu038.
36. Simon GE, Goldberg DP, Von Korff M, Ustün TB. Understanding cross-national differences in depression prevalence. Psychol Med. 2002;32(4):585-594. https://doi.org/10.1017/S0033291702005457.
1. Pletcher MJ, Kertesz SG, Kohn MA, Gonzales R. Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. JAMA. 2008;299(1):70-78. https://doi.org/10.1001/jama.2007.64.
2. Herzig SJ. Growing concerns regarding long-term opioid use: the hospitalization hazard. J Hosp Med. 2015;10(7):469-470. https://doi.org/10.1002/jhm.2369.
3. Guy GP Jr, Zhang K, Bohm MK, et al. Vital Signs: changes in opioid prescribing in the United States, 2006–2015. MMWR Morb Mortal Wkly Rep. 2017;66(26):697-704. https://doi.org/10.15585/mmwr.mm6626a4.
4. Okie S. A flood of opioids, a rising tide of deaths. N Engl J Med. 2010;363(21):1981-1985. https://doi.org/10.1056/NEJMp1011512.
5. Liang Y, Turner BJ. National cohort study of opioid analgesic dose and risk of future hospitalization. J Hosp Med. 2015;10(7):425-431. https://doi.org/10.1002/jhm.2350.
6. Han B, Compton WM, Blanco C, et al. Prescription opioid use, misuse, and use disorders in U.S. Adults: 2015 national survey on drug use and health. Ann Intern Med. 2017;167(5):293-301. https://doi.org/10.7326/M17-0865.
7. Schuchat A, Houry D, Guy GP, Jr. New data on opioid use and prescribing in the United States. JAMA. 2017;318(5):425-426. https://doi.org/10.1001/jama.2017.8913.
8. Sawyer J, Haslam L, Robinson S, Daines P, Stilos K. Pain prevalence study in a large Canadian teaching hospital. Pain Manag Nurs. 2008;9(3):104-112. https://doi.org/10.1016/j.pmn.2008.02.001.
9. Gupta A, Daigle S, Mojica J, Hurley RW. Patient perception of pain care in hospitals in the United States. J Pain Res. 2009;2:157-164. https://doi.org/10.2147/JPR.S7903.
10. Herzig SJ, Rothberg MB, Cheung M, Ngo LH, Marcantonio ER. Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals. J Hosp Med. 2014;9(2):73-81. https://doi.org/10.1002/jhm.2102.
11. Kanjanarat P, Winterstein AG, Johns TE, et al. Nature of preventable adverse drug events in hospitals: a literature review. Am J Health Syst Pharm. 2003;60(17):1750-1759. https://doi.org/10.1093/ajhp/60.17.1750.
12. Jena AB, Goldman D, Karaca-Mandic P. Hospital prescribing of opioids to medicare beneficiaries. JAMA Intern Med. 2016;176(7):990-997. https://doi.org/10.1001/jamainternmed.2016.2737.
13. Hooten WM, St Sauver JL, McGree ME, Jacobson DJ, Warner DO. Incidence and risk factors for progression From short-term to episodic or long-term opioid prescribing: A population-based study. Mayo Clin Proc. 2015;90(7):850-856. https://doi.org/10.1016/j.mayocp.2015.04.012.
14. Alam A, Gomes T, Zheng H, et al. Long-term analgesic use after low-risk surgery: a retrospective cohort study. Arch Intern Med. 2012;172(5):425-430. https://doi.org/10.1001/archinternmed.2011.1827.
15. Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med. 2017;376(7):663-673. https://doi.org/10.1056/NEJMsa1610524.
16. Calcaterra SL, Scarbro S, Hull ML, et al. Prediction of future chronic opioid use Among hospitalized patients. J Gen Intern Med. 2018;33(6):898-905. https://doi.org/10.1007/s11606-018-4335-8.
17. Callister LC. Cultural influences on pain perceptions and behaviors. Home Health Care Manag Pract. 2003;15(3):207-211. https://doi.org/10.1177/1084822302250687.
18. Paulozzi LJ, Mack KA, Hockenberry JM. Vital signs: Variation among states in prescribing of opioid pain relievers and benzodiazepines--United States, 2012. J Saf Res. 2014;63(26):563-568. https://doi.org/10.1016/j.jsr.2014.09.001.
19. Callister LC, Khalaf I, Semenic S, Kartchner R, Vehvilainen-Julkunen K. The pain of childbirth: perceptions of culturally diverse women. Pain Manag Nurs. 2003;4(4):145-154. https://doi.org/10.1016/S1524-9042(03)00028-6.
20. Moore R, Brødsgaard I, Mao TK, Miller ML, Dworkin SF. Perceived need for local anesthesia in tooth drilling among Anglo-Americans, Chinese, and Scandinavians. Anesth Prog. 1998;45(1):22-28.
21. Kankkunen PM, Vehviläinen-Julkunen KM, Pietilä AM, et al. A tale of two countries: comparison of the perceptions of analgesics among Finnish and American parents. Pain Manag Nurs. 2008;9(3):113-119. https://doi.org/10.1016/j.pmn.2007.12.003.
22. Hanoch Y, Katsikopoulos KV, Gummerum M, Brass EP. American and German students’ knowledge, perceptions, and behaviors with respect to over-the-counter pain relievers. Health Psychol. 2007;26(6):802-806. https://doi.org/10.1037/0278-6133.26.6.802.
23. Manjiani D, Paul DB, Kunnumpurath S, Kaye AD, Vadivelu N. Availability and utilization of opioids for pain management: global issues. Ochsner J. 2014;14(2):208-215.
24. Quality improvement guidelines for the treatment of acute pain and cancer pain. JAMA. 1995;274(23):1874-1880.
25. McNeill JA, Sherwood GD, Starck PL, Thompson CJ. Assessing clinical outcomes: patient satisfaction with pain management. J Pain Symptom Manag. 1998;16(1):29-40. https://doi.org/10.1016/S0885-3924(98)00034-7.
26. Ferrari R, Novello C, Catania G, Visentin M. Patients’ satisfaction with pain management: the Italian version of the Patient Outcome Questionnaire of the American Pain Society. Recenti Prog Med. 2010;101(7–8):283-288.
27. Malouf J, Andión O, Torrubia R, Cañellas M, Baños JE. A survey of perceptions with pain management in Spanish inpatients. J Pain Symptom Manag. 2006;32(4):361-371. https://doi.org/10.1016/j.jpainsymman.2006.05.006.
28. Gordon DB, Polomano RC, Pellino TA, et al. Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for quality improvement of pain management in hospitalized adults: preliminary psychometric evaluation. J Pain. 2010;11(11):1172-1186. https://doi.org/10.1016/j.jpain.2010.02.012.
29. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000;25(24):3186-3191. https://doi.org/10.1097/00007632-200012150-00014.
30. Harris PA, Taylor R, Thielke R, et al. Research Electronic Data Capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. https://doi.org/10.1016/j.jbi.2008.08.010.
31. Duman F. After surgery in Germany, I wanted Vicodin, not herbal tea. New York Times. January 27, 2018. https://www.nytimes.com/2018/01/27/opinion/sunday/surgery-germany-vicodin.html. Accessed November 6, 2018.
32. Beaudoin FL, Banerjee GN, Mello MJ. State-level and system-level opioid prescribing policies: the impact on provider practices and overdose deaths, a systematic review. J Opioid Manag. 2016;12(2):109-118. https://doi.org/10.5055/jom.2016.0322.
33. Bao Y, Wen K, Johnson P, et al. Assessing the impact of state policies for prescription drug monitoring programs on high-risk opioid prescriptions. Health Aff (Millwood). 2018;37(10):1596-1604. https://doi.org/10.1377/hlthaff.2018.0512.
34. Friedman J, Kim D, Schneberk T, et al. Assessment of racial/ethnic and income disparities in the prescription of opioids and other controlled medications in California. JAMA Intern Med. 2019. https://doi.org/10.1001/jamainternmed.2018.6721.
35. Steel Z, Marnane C, Iranpour C, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. Int J Epidemiol. 2014;43(2):476-493. https://doi.org/10.1093/ije/dyu038.
36. Simon GE, Goldberg DP, Von Korff M, Ustün TB. Understanding cross-national differences in depression prevalence. Psychol Med. 2002;32(4):585-594. https://doi.org/10.1017/S0033291702005457.
© 2019 Society of Hospital Medicine
State of Research in Adult Hospital Medicine: Results of a National Survey
Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.1 For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.2,3
Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.4 Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.
Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.
METHODS
Study Setting and Participants
Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult
Survey Development
A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.
Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.
Statistical Analysis
Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).
Ethical and Regulatory Considerations
The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).
RESULTS
General Characteristics of Research Programs and Faculty
Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).
Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.
Key Attributes of Research Programs
In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.
A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).
Research Fellowship Programs/Training Programs
Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).
The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.
Research Faculty
Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).
Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).
In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.
DISCUSSION
In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.
Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.
Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.5 The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.8 Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.
While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).9 Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.10 Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.
Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.14 Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.
In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.
Disclosures
Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.
1. International Working Party to Promote and Revitalise Academic Medicine. Academic medicine: the evidence base. BMJ. 2004;329(7469):789-792. PubMed
2. Flanders SA, Saint S, McMahon LF, Howell JD. Where should hospitalists sit within the academic medical center? J Gen Intern Med. 2008;23(8):1269-1272. PubMed
3. Flanders SA, Centor B, Weber V, McGinn T, Desalvo K, Auerbach A. Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit. J Gen Intern Med. 2009;24(5):636-641. PubMed
4. Dang Do AN, Munchhof AM, Terry C, Emmett T, Kara A. Research and publication trends in hospital medicine. J Hosp Med. 2014;9(3):148-154. PubMed
5. Harrison R, Hunter AJ, Sharpe B, Auerbach AD. Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6(1):5-9. PubMed
6. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6(3):161-166. PubMed
7. Nagarur A, O’Neill RM, Lawton D, Greenwald JL. Supporting faculty development in hospital medicine: design and implementation of a personalized structured mentoring program. J Hosp Med. 2018;13(2):96-99. PubMed
8. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. PubMed
9. Flanders SA, Kaufman SR, Nallamothu BK, Saint S. The University of Michigan Specialist-Hospitalist Allied Research Program: jumpstarting hospital medicine research. J Hosp Med. 2008;3(4):308-313. PubMed
10. Auerbach AD, Patel MS, Metlay JP, et al. The Hospital Medicine Reengineering Network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
11. Souba WW. Academic medicine’s core values: what do they mean? J Surg Res. 2003;115(2):171-173. PubMed
12. Bonsall J, Chopra V. Building an academic pipeline: a combined society of hospital medicine committee initiative. J Hosp Med. 2016;11(10):735-736. PubMed
13. Sweigart JR, Tad YD, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12(3):173-176. PubMed
14. Cunningham CT, Quan H, Hemmelgarn B, et al. Exploring physician specialist response rates to web-based surveys. BMC Med Res Methodol. 2015;15(1):32. PubMed
Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.1 For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.2,3
Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.4 Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.
Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.
METHODS
Study Setting and Participants
Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult
Survey Development
A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.
Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.
Statistical Analysis
Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).
Ethical and Regulatory Considerations
The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).
RESULTS
General Characteristics of Research Programs and Faculty
Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).
Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.
Key Attributes of Research Programs
In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.
A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).
Research Fellowship Programs/Training Programs
Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).
The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.
Research Faculty
Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).
Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).
In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.
DISCUSSION
In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.
Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.
Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.5 The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.8 Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.
While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).9 Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.10 Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.
Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.14 Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.
In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.
Disclosures
Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.
Almost all specialties in internal medicine have a sound scientific research base through which clinical practice is informed.1 For the field of Hospital Medicine (HM), this evidence has largely comprised research generated from fields outside of the specialty. The need to develop, invest, and grow investigators in hospital-based medicine remains unmet as HM and its footprint in hospital systems continue to grow.2,3
Despite this fact, little is known about the current state of research in HM. A 2014 survey of the members of the Society of Hospital Medicine (SHM) found that research output across the field of HM, as measured on the basis of peer-reviewed publications, was growing.4 Since then, however, the numbers of individuals engaged in research activities, their background and training, publication output, or funding sources have not been quantified. Similarly, little is known about which institutions support the development of junior investigators (ie, HM research fellowships), how these programs are funded, and whether or not matriculants enter the field as investigators. These gaps must be measured, evaluated, and ideally addressed through strategic policy and funding initiatives to advance the state of science within HM.
Members of the SHM Research Committee developed, designed, and deployed a survey to improve the understanding of the state of research in HM. In this study, we aimed to establish the baseline of research in HM to enable the measurement of progress through periodic waves of data collection. Specifically, we sought to quantify and describe the characteristics of existing research programs, the sources and types of funding, the number and background of faculty, and the availability of resources for training researchers in HM.
METHODS
Study Setting and Participants
Given that no defined list, database, or external resource that identifies research programs and contacts in HM exists, we began by creating a strategy to identify and sample adult
Survey Development
A workgroup within the SHM Research Committee was tasked to create a survey that would achieve four distinct goals: (1) identify institutions currently engaging in hospital-based research; (2) define the characteristics, including sources of research funding, training opportunities, criteria for promotion, and grant support, of research programs within institutions; (3) understand the prevalence of research fellowship programs, including size, training curricula, and funding sources; and (4) evaluate the productivity and funding sources of HM investigators at each site.
Survey questions that target each of these domains were drafted by the workgroup. Questions were pretested with colleagues outside the workgroup focused on this project (ie, from the main research committee). The instrument was refined and edited to improve the readability and clarity of questions on the basis of the feedback obtained through the iterative process. The revised instrument was then programmed into an online survey administration tool (SurveyMonkey®) to facilitate electronic dissemination. Finally, the members of the workgroup tested the online survey to ensure functionality. No identifiable information was collected from respondents, and no monetary incentive was offered for the completion of the survey. An invitation to participate in the survey was sent via e-mail to each of the program contacts identified.
Statistical Analysis
Descriptive statistics, including proportions, means, and percentages, were used to tabulate results. All analyses were conducted using Stata 13 MP/SE (StataCorp, College Station, Texas).
Ethical and Regulatory Considerations
The study was reviewed and deemed exempt from regulation by the University of Michigan Institutional Review Board (HUM000138628).
RESULTS
General Characteristics of Research Programs and Faculty
Out of 100 program contacts, 28 (representing 1,586 faculty members) responded and were included in the survey (program response rate = 28%). When comparing programs that did respond with those that did not, a greater proportion of programs in university settings were noted among respondents (79% vs 21%). Respondents represented programs from all regions of the United States, with most representing university-based (79%), university-affiliated (14%) or Veterans Health Administration (VHA; 11%) programs. Most respondents were in leadership roles, including division chiefs (32%), research directors/leads (21%), section chiefs (18%), and related titles, such as program director. Respondents indicated that the total number of faculty members in their programs (including nonclinicians and advance practice providers) varied from eight to 152 (mean [SD] = 57 [36]) members, with physicians representing the majority of faculty members (Table 1).
Among the 1,586 faculty members within the 28 programs, respondents identified 192 faculty members (12%) as currently receiving extra- or intramural support for research activities. Of these faculty, over half (58%) received <25% of effort from intra or extramural sources, and 28 (15%) and 52 (27%) faculty members received 25%-50% or >50% of support for their effort, respectively. The number of investigators who received funding across programs ranged from 0 to 28 faculty members. Compared with the 192 funded investigators, respondents indicated that a larger number of faculty in their programs (n = 656 or 41%) were involved in local quality improvement (QI) efforts. Of the 656 faculty members involved in QI efforts, 241 individuals (37%) were internally funded and received protected time/effort for their work.
Key Attributes of Research Programs
In the evaluation of the amount of total grant funding, respondents from 17 programs indicated that they received $500,000 in annual extra and intramural funding, and those from three programs stated that they received $500,000 to $999,999 in funding. Five respondents indicated that their programs currently received $1 million to $5 million in grant funding, and three reported >$5 million in research support. The sources of research funding included several divisions within the National Institute of Health (NIH, 12 programs), Agency for Healthcare Research and Quality (AHRQ, four programs), foundations (four programs), and internal grants (six programs). Additionally, six programs indicated “other” sources of funding that included the VHA, Patient-Centered Outcomes Research Institute (PCORI), Centers for Medicare and Medicaid Services, Centers for Disease Control (CDC), and industry sources.
A range of grants, including career development awards (11 programs); small grants, such as R21 and R03s (eight programs); R-level grants, including VA merit awards (five programs); program series grants, such as P and U grants (five programs), and foundation grants (eight programs), were reported as types of awards. Respondents from 16 programs indicated that they provided internal pilot grants. Amounts for such grants ranged from <$50,000 (14 programs) to $50,000-$100,000 (two programs).
Research Fellowship Programs/Training Programs
Only five of the 28 surveyed programs indicated that they currently had a research training or fellowship program for developing hospitalist investigators. The age of these programs varied from <1 year to 10 years. Three of the five programs stated that they had two fellows per year, and two stated they had spots for one trainee annually. All respondents indicated that fellows received training on study design, research methods, quantitative (eg, large database and secondary analyses) and qualitative data analysis. In addition, two programs included training in systematic review and meta-analyses, and three included focused courses on healthcare policy. Four of the five programs included training in QI tools, such as LEAN and Six Sigma. Funding for four of the five fellowship programs came from internal sources (eg, department and CTSA). However, two programs added they received some support from extramural funding and philanthropy. Following training, respondents from programs indicated that the majority of their graduates (60%) went on to hybrid research/QI roles (50/50 research/clinical effort), whereas 40% obtained dedicated research investigator (80/20) positions (Table 2).
The 23 institutions without research training programs cited that the most important barrier for establishing such programs was lack of funding (12 programs) and the lack of a pipeline of hospitalists seeking such training (six programs). However, 15 programs indicated that opportunities for hospitalists to gain research training in the form of courses were available internally (eg, courses in the department or medical school) or externally (eg, School of Public Health). Seven programs indicated that they were planning to start a HM research fellowship within the next five years.
Research Faculty
Among the 28 respondents, 15 stated that they have faculty members who conduct research as their main professional activity (ie, >50% effort). The number of faculty members in each program in such roles varied from one to 10. Respondents indicated that faculty members in this category were most often midcareer assistant or associate professors with few full professors. All programs indicated that scholarship in the form of peer-reviewed publications was required for the promotion of faculty. Faculty members who performed research as their main activity had all received formal fellowship training and consequently had dual degrees (MD with MPH or MD, with MSc being the two most common combinations). With respect to clinical activities, most respondents indicated that research faculty spent 10% to 49% of their effort on clinical work. However, five respondents indicated that research faculty had <10% effort on clinical duties (Table 3).
Eleven respondents (39%) identified the main focus of faculty as health service research, where four (14%) identified their main focus as clinical trials. Regardless of funding status, all respondents stated that their faculty were interested in studying quality and process improvement efforts (eg, transitions or readmissions, n = 19), patient safety initiatives (eg, hospital-acquired complications, n = 17), and disease-specific areas (eg, thrombosis, n = 15).
In terms of research output, 12 respondents stated that their research/QI faculty collectively published 11-50 peer-reviewed papers during the academic year, and 10 programs indicated that their faculty published 0-10 papers per year. Only three programs reported that their faculty collectively published 50-99 peer-reviewed papers per year. With respect to abstract presentations at national conferences, 13 programs indicated that they presented 0-10 abstracts, and 12 indicated that they presented 11-50.
DISCUSSION
In this first survey quantifying research activities in HM, respondents from 28 programs shared important insights into research activities at their institutions. Although our sample size was small, substantial variation in the size, composition, and structure of research programs in HM among respondents was observed. For example, few respondents indicated the availability of training programs for research in HM at their institutions. Similarly, among faculty who focused mainly on research, variation in funding streams and effort protection was observed. A preponderance of midcareer faculty with a range of funding sources, including NIH, AHRQ, VHA, CMS, and CDC was reported. Collectively, these data not only provide a unique glimpse into the state of research in HM but also help establish a baseline of the status of the field at large.
Some findings of our study are intuitive given our sampling strategy and the types of programs that responded. For example, the fact that most respondents for research programs represented university-based or affiliated institutions is expected given the tripartite academic mission. However, even within our sample of highly motivated programs, some findings are surprising and merit further exploration. For example, the observation that some respondents identified HM investigators within their program with <25% in intra- or extramural funding was unexpected. On the other extreme, we were surprised to find that three programs reported >$5 million in research funding. Understanding whether specific factors, such as the availability of experienced mentors within and outside departments or assistance from support staff (eg, statisticians and project managers), are associated with success and funding within these programs are important questions to answer. By focusing on these issues, we will be well poised as a field to understand what works, what does not work, and why.
Likewise, the finding that few programs within our sample offer formal training in the form of fellowships to research investigators represents an improvement opportunity. A pipeline for growing investigators is critical for the specialty that is HM. Notably, this call is not new; rather, previous investigators have highlighted the importance of developing academically oriented hospitalists for the future of the field.5 The implementation of faculty scholarship development programs has improved the scholarly output, mentoring activities, and succession planning of academics within HM.6,7 Conversely, lack of adequate mentorship and support for academic activities remains a challenge and as a factor associated with the failure to produce academic work.8 Without a cadre of investigators asking critical questions related to care delivery, the legitimacy of our field may be threatened.
While extrapolating to the field is difficult given the small number of our respondents, highlighting the progress that has been made is important. For example, while misalignment between funding and clinical and research mission persists, our survey found that several programs have been successful in securing extramural funding for their investigators. Additionally, internal funding for QI work appears to be increasing, with hospitalists receiving dedicated effort for much of this work. Innovation in how best to support and develop these types of efforts have also emerged. For example, the University of Michigan Specialist Hospitalist Allied Research Program offers dedicated effort and funding for hospitalists tackling projects germane to HM (eg, ordering of blood cultures for febrile inpatients) that overlap with subspecialists (eg, infectious diseases).9 Thus, hospitalists are linked with other specialties in the development of research agendas and academic products. Similarly, the launch of the HOMERUN network, a coalition of investigators who bridge health systems to study problems central to HM, has helped usher in a new era of research opportunities in the specialty.10 Fundamentally, the culture of HM has begun to place an emphasis on academic and scholarly productivity in addition to clinical prowess.11-13 Increased support and funding for training programs geared toward innovation and research in HM is needed to continue this mission. The Society for General Internal Medicine, American College of Physicians, and SHM have important roles to play as the largest professional organizations for generalists in this respect. Support for research, QI, and investigators in HM remains an urgent and largely unmet need.
Our study has limitations. First, our response rate was low at 28% but is consistent with the response rates of other surveys of physician groups.14 Caution in making inferences to the field at large is necessary given the potential for selection and nonresponse bias. However, we expect that respondents are likely biased toward programs actively conducting research and engaged in QI, thus better reflecting the state of these activities in HM. Second, given that we did not ask for any identifying information, we have no way of establishing the accuracy of the data provided by respondents. However, we have no reason to believe that responses would be altered in a systematic fashion. Future studies that link our findings to publicly available data (eg, databases of active grants and funding) might be useful. Third, while our survey instrument was created and internally validated by hospitalist researchers, its lack of external validation could limit findings. Finally, our results vary on the basis of how respondents answered questions related to effort and time allocation given that these measures differ across programs.
In summary, the findings from this study highlight substantial variations in the number, training, and funding of research faculty across HM programs. Understanding the factors behind the success of some programs and the failures of others appears important in informing and growing the research in the field. Future studies that aim to expand survey participation, raise the awareness of the state of research in HM, and identify barriers and facilitators to academic success in HM are needed.
Disclosures
Dr. Chopra discloses grant funding from the Agency for Healthcare Research and Quality (AHRQ), VA Health Services and Research Department, and Centers for Disease Control. Dr. Jones discloses grant funding from AHRQ. All other authors disclose no conflicts of interest.
1. International Working Party to Promote and Revitalise Academic Medicine. Academic medicine: the evidence base. BMJ. 2004;329(7469):789-792. PubMed
2. Flanders SA, Saint S, McMahon LF, Howell JD. Where should hospitalists sit within the academic medical center? J Gen Intern Med. 2008;23(8):1269-1272. PubMed
3. Flanders SA, Centor B, Weber V, McGinn T, Desalvo K, Auerbach A. Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit. J Gen Intern Med. 2009;24(5):636-641. PubMed
4. Dang Do AN, Munchhof AM, Terry C, Emmett T, Kara A. Research and publication trends in hospital medicine. J Hosp Med. 2014;9(3):148-154. PubMed
5. Harrison R, Hunter AJ, Sharpe B, Auerbach AD. Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6(1):5-9. PubMed
6. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6(3):161-166. PubMed
7. Nagarur A, O’Neill RM, Lawton D, Greenwald JL. Supporting faculty development in hospital medicine: design and implementation of a personalized structured mentoring program. J Hosp Med. 2018;13(2):96-99. PubMed
8. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. PubMed
9. Flanders SA, Kaufman SR, Nallamothu BK, Saint S. The University of Michigan Specialist-Hospitalist Allied Research Program: jumpstarting hospital medicine research. J Hosp Med. 2008;3(4):308-313. PubMed
10. Auerbach AD, Patel MS, Metlay JP, et al. The Hospital Medicine Reengineering Network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
11. Souba WW. Academic medicine’s core values: what do they mean? J Surg Res. 2003;115(2):171-173. PubMed
12. Bonsall J, Chopra V. Building an academic pipeline: a combined society of hospital medicine committee initiative. J Hosp Med. 2016;11(10):735-736. PubMed
13. Sweigart JR, Tad YD, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12(3):173-176. PubMed
14. Cunningham CT, Quan H, Hemmelgarn B, et al. Exploring physician specialist response rates to web-based surveys. BMC Med Res Methodol. 2015;15(1):32. PubMed
1. International Working Party to Promote and Revitalise Academic Medicine. Academic medicine: the evidence base. BMJ. 2004;329(7469):789-792. PubMed
2. Flanders SA, Saint S, McMahon LF, Howell JD. Where should hospitalists sit within the academic medical center? J Gen Intern Med. 2008;23(8):1269-1272. PubMed
3. Flanders SA, Centor B, Weber V, McGinn T, Desalvo K, Auerbach A. Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit. J Gen Intern Med. 2009;24(5):636-641. PubMed
4. Dang Do AN, Munchhof AM, Terry C, Emmett T, Kara A. Research and publication trends in hospital medicine. J Hosp Med. 2014;9(3):148-154. PubMed
5. Harrison R, Hunter AJ, Sharpe B, Auerbach AD. Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6(1):5-9. PubMed
6. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6(3):161-166. PubMed
7. Nagarur A, O’Neill RM, Lawton D, Greenwald JL. Supporting faculty development in hospital medicine: design and implementation of a personalized structured mentoring program. J Hosp Med. 2018;13(2):96-99. PubMed
8. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. PubMed
9. Flanders SA, Kaufman SR, Nallamothu BK, Saint S. The University of Michigan Specialist-Hospitalist Allied Research Program: jumpstarting hospital medicine research. J Hosp Med. 2008;3(4):308-313. PubMed
10. Auerbach AD, Patel MS, Metlay JP, et al. The Hospital Medicine Reengineering Network (HOMERuN): a learning organization focused on improving hospital care. Acad Med. 2014;89(3):415-420. PubMed
11. Souba WW. Academic medicine’s core values: what do they mean? J Surg Res. 2003;115(2):171-173. PubMed
12. Bonsall J, Chopra V. Building an academic pipeline: a combined society of hospital medicine committee initiative. J Hosp Med. 2016;11(10):735-736. PubMed
13. Sweigart JR, Tad YD, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12(3):173-176. PubMed
14. Cunningham CT, Quan H, Hemmelgarn B, et al. Exploring physician specialist response rates to web-based surveys. BMC Med Res Methodol. 2015;15(1):32. PubMed
© 2019 Society of Hospital Medicine
Barriers to Early Hospital Discharge: A Cross-Sectional Study at Five Academic Hospitals
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
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26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed
33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
1. Wertheimer B, Jacobs RE, Bailey M, et al. Discharge before noon: an achievable hospital goal. J Hosp Med. 2014;9(4):210-214. doi: 10.1002/jhm.2154. PubMed
2. Kane M, Weinacker A, Arthofer R, et al. A multidisciplinary initiative to increase inpatient discharges before noon. J Nurs Adm. 2016;46(12):630-635. doi: 10.1097/NNA.0000000000000418. PubMed
3. Khanna S, Sier D, Boyle J, Zeitz K. Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emerg Med Australas. 2016;28(2):164-170. doi: 10.1111/1742-6723.12543. PubMed
4. Kravet SJ, Levine RB, Rubin HR, Wright SM. Discharging patients earlier in the day: a concept worth evaluating. Health Care Manag (Frederick). 2007;26:142-146. doi: 10.1097/01.HCM.0000268617.33491.60. PubMed
5. Khanna S, Boyle J, Good N, Lind J. Impact of admission and discharge peak times on hospital overcrowding. Stud Health Technol Inform. 2011;168:82-88. doi: 10.3233/978-1-60750-791-8-82. PubMed
6. McGowan JE, Truwit JD, Cipriano P, et al. Operating room efficiency and hospital capacity: factors affecting operating room use during maximum hospital census. J Am Coll Surg. 2007;204(5):865-871; discussion 71-72. doi: 10.1016/j.jamcollsurg.2007.01.052 PubMed
7. Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92-98. doi: 10.3233/978-1-61499-078-9-92 PubMed
8. Powell ES, Khare RK, Venkatesh AK, Van Roo BD, Adams JG, Reinhardt G. The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med. 2012;42(2):186-196. doi: 10.1016/j.jemermed.2010.06.028. PubMed
9. Wertheimer B, Jacobs RE, Iturrate E, Bailey M, Hochman K. Discharge before noon: Effect on throughput and sustainability. J Hosp Med. 2015;10(10):664-669. doi: 10.1002/jhm.2412. PubMed
10. Sikka R, Mehta S, Kaucky C, Kulstad EB. ED crowding is associated with an increased time to pneumonia treatment. Am J Emerg Med. 2010;28(7):809-812. doi: 10.1016/j.ajem.2009.06.023. PubMed
11. Coil CJ, Flood JD, Belyeu BM, Young P, Kaji AH, Lewis RJ. The effect of emergency department boarding on order completion. Ann Emerg Med. 2016;67:730-736 e2. doi: 10.1016/j.annemergmed.2015.09.018. PubMed
12. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. doi: 10.1016/j.ajem.2017.01.061. PubMed
13. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med. 2007;50(5):510-516. doi: 10.1016/j.annemergmed.2007.07.021. PubMed
14. Hwang U, Richardson L, Livote E, Harris B, Spencer N, Sean Morrison R. Emergency department crowding and decreased quality of pain care. Acad Emerg Med. 2008;15:1248-1255. doi: 10.1111/j.1553-2712.2008.00267.x. PubMed
15. Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med. 2009;16:603-608. doi: 10.1111/j.1553-2712.2009.00441.x. PubMed
16. Pines JM, Shofer FS, Isserman JA, Abbuhl SB, Mills AM. The effect of emergency department crowding on analgesia in patients with back pain in two hospitals. Acad Emerg Med. 2010;17(3):276-283. doi: 10.1111/j.1553-2712.2009.00676.x. PubMed
17. Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010;28:304-309. doi: 10.1016/j.ajem.2008.12.014. PubMed
18. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006;184(5):213-216. PubMed
19. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. doi: 10.1016/j.annemergmed.2008.03.014. PubMed
20. Singer AJ, Thode HC, Jr., Viccellio P, Pines JM. The association between length of emergency department boarding and mortality. Acad Emerg Med. 2011;18(12):1324-1329. doi: 10.1111/j.1553-2712.2011.01236.x. PubMed
21. White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DF. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med. 2013;44(1):230-235. doi: 10.1016/j.jemermed.2012.05.007. PubMed
22. Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127-133. doi: 10.1197/aemj.10.2.127. PubMed
23. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192-197. PubMed
24. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825-831. doi: 10.1111/j.1553-2712.2008.00200.x. PubMed
25. Durvasula R, Kayihan A, Del Bene S, et al. A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center. Qual Manag Health Care. 2015;24:45-51. doi: 10.1097/QMH.0000000000000049. PubMed
26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed
33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
1. Wertheimer B, Jacobs RE, Bailey M, et al. Discharge before noon: an achievable hospital goal. J Hosp Med. 2014;9(4):210-214. doi: 10.1002/jhm.2154. PubMed
2. Kane M, Weinacker A, Arthofer R, et al. A multidisciplinary initiative to increase inpatient discharges before noon. J Nurs Adm. 2016;46(12):630-635. doi: 10.1097/NNA.0000000000000418. PubMed
3. Khanna S, Sier D, Boyle J, Zeitz K. Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emerg Med Australas. 2016;28(2):164-170. doi: 10.1111/1742-6723.12543. PubMed
4. Kravet SJ, Levine RB, Rubin HR, Wright SM. Discharging patients earlier in the day: a concept worth evaluating. Health Care Manag (Frederick). 2007;26:142-146. doi: 10.1097/01.HCM.0000268617.33491.60. PubMed
5. Khanna S, Boyle J, Good N, Lind J. Impact of admission and discharge peak times on hospital overcrowding. Stud Health Technol Inform. 2011;168:82-88. doi: 10.3233/978-1-60750-791-8-82. PubMed
6. McGowan JE, Truwit JD, Cipriano P, et al. Operating room efficiency and hospital capacity: factors affecting operating room use during maximum hospital census. J Am Coll Surg. 2007;204(5):865-871; discussion 71-72. doi: 10.1016/j.jamcollsurg.2007.01.052 PubMed
7. Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92-98. doi: 10.3233/978-1-61499-078-9-92 PubMed
8. Powell ES, Khare RK, Venkatesh AK, Van Roo BD, Adams JG, Reinhardt G. The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med. 2012;42(2):186-196. doi: 10.1016/j.jemermed.2010.06.028. PubMed
9. Wertheimer B, Jacobs RE, Iturrate E, Bailey M, Hochman K. Discharge before noon: Effect on throughput and sustainability. J Hosp Med. 2015;10(10):664-669. doi: 10.1002/jhm.2412. PubMed
10. Sikka R, Mehta S, Kaucky C, Kulstad EB. ED crowding is associated with an increased time to pneumonia treatment. Am J Emerg Med. 2010;28(7):809-812. doi: 10.1016/j.ajem.2009.06.023. PubMed
11. Coil CJ, Flood JD, Belyeu BM, Young P, Kaji AH, Lewis RJ. The effect of emergency department boarding on order completion. Ann Emerg Med. 2016;67:730-736 e2. doi: 10.1016/j.annemergmed.2015.09.018. PubMed
12. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. doi: 10.1016/j.ajem.2017.01.061. PubMed
13. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med. 2007;50(5):510-516. doi: 10.1016/j.annemergmed.2007.07.021. PubMed
14. Hwang U, Richardson L, Livote E, Harris B, Spencer N, Sean Morrison R. Emergency department crowding and decreased quality of pain care. Acad Emerg Med. 2008;15:1248-1255. doi: 10.1111/j.1553-2712.2008.00267.x. PubMed
15. Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med. 2009;16:603-608. doi: 10.1111/j.1553-2712.2009.00441.x. PubMed
16. Pines JM, Shofer FS, Isserman JA, Abbuhl SB, Mills AM. The effect of emergency department crowding on analgesia in patients with back pain in two hospitals. Acad Emerg Med. 2010;17(3):276-283. doi: 10.1111/j.1553-2712.2009.00676.x. PubMed
17. Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010;28:304-309. doi: 10.1016/j.ajem.2008.12.014. PubMed
18. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006;184(5):213-216. PubMed
19. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. doi: 10.1016/j.annemergmed.2008.03.014. PubMed
20. Singer AJ, Thode HC, Jr., Viccellio P, Pines JM. The association between length of emergency department boarding and mortality. Acad Emerg Med. 2011;18(12):1324-1329. doi: 10.1111/j.1553-2712.2011.01236.x. PubMed
21. White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DF. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med. 2013;44(1):230-235. doi: 10.1016/j.jemermed.2012.05.007. PubMed
22. Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127-133. doi: 10.1197/aemj.10.2.127. PubMed
23. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192-197. PubMed
24. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825-831. doi: 10.1111/j.1553-2712.2008.00200.x. PubMed
25. Durvasula R, Kayihan A, Del Bene S, et al. A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center. Qual Manag Health Care. 2015;24:45-51. doi: 10.1097/QMH.0000000000000049. PubMed
26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed
33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
You Can’t Have It All: The Experience of Academic Hospitalists During Pregnancy, Parental Leave, and Return to Work
Despite recent advances made in medicine, gender-based disparities persist.1-3 In particular, women with children have barriers to career advancement and show evidence of slower career advancement.1,2 Multiple challenges for working women experiencing motherhood have been described. In academic medicine in the United States, women have limited access to paid parental leave.4-6 For women who choose to breastfeed, there is limited time, space, and support available for breastfeeding.7 Furthermore, sleep deprivation in the postpartum period significantly impacts the ability to function at work.8
Hospital medicine is a unique specialty as it comprises 47% women, 80% of whom are aged less than 40 years, suggesting that a large portion are women of childbearing age.9 The field poses known challenges to this population, including shift work, atypical schedules, and unpredictable hours. We conducted a descriptive qualitative study to improve our understanding of the experience of female academic hospitalists who have experienced pregnancy, parental leave, and the return to work as faculty. Our goal was to both explore the challenges to undergoing this experience and discover solutions to support female academic hospitalists.
METHODS
Study Design
We conducted a qualitative descriptive study of female hospitalists recruited from academic institutions represented in Society of Hospital Medicine (SHM) committees. Interviews were conducted between November 2017 and February 2018. Participants completed an informed consent and a demographic survey prior to the interview. Each interview lasted approximately 30 minutes; discussions were recorded on digital records and transcribed verbatim. This protocol was reviewed and granted exemption by the Institutional Review Board at the University of Colorado.
Population
We recruited participants from a selection of hospital medicine groups nationally, chosen from SHM committee representation. A purposeful snowball approach was used to identify hospitalists from representative programs and seek their recommendation for hospitalists from other targeted programs. Ten hospitalists were approached by e-mail to determine their interest in participation, and all of them agreed to participate. Each participant experienced new parenthood within the last seven years.
Framework
We constructed our interview to represent the following timeline associated with having children as it pertains to a hospitalist position: pregnancy, parental leave, and the return to work. The interview guide was structured to invoke the positive aspects, challenges, and solutions within each domain (Appendix 1).
Analysis
Codes were inductively developed from the interview data by a team of three board-certified internal medicine physicians (E.G., A.M., and C.J.), one of whom had prior training and experience with qualitative interviews and analysis (C.J.). Among the coders, two (E.G. and A.M.) conducted the semistructured interviews. Code disparities were reconciled by team consensus, where the primary coder facilitated the discussions. Themes were developed inductively from the codes, and the analysis was completed using a team-based iterative approach that was facilitated using ATLAS.ti.10 Thematic saturation was achieved. This study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
The demographics and the characteristics of the hospital medicine group are shown in Table 1. Although we asked questions about both the positive and challenging aspects of the experience of parenthood, the interviews tended to focus more on the challenges faced and on areas for optimization.
Paid Parental leave
Most of the participants described inadequate paid parental leave, with minimal transparency in the processes for ensuring time off following the birth of their child, resulting in “haggling” with bosses, human resources, and the administrative staff. Rarely was a formal parental leave policy in place. Once a parental leave plan was established, several women reported the financial burden associated with a leave that was partially, or fully, unpaid.
“All of my leave was unpaid. .. managed to finagle short-term disability into paying for it… the system was otherwise set up to screw me financially.”
For the three women who did experience sufficient paid parental leave, they recognized the financial and emotional benefit and suggested that further optimization would include a prebirth schedule to account for the physical challenges and potential complications.
Physical Challenges
All of the women described significant physical challenges when working during pregnancy, resulting in limited bandwidth for additional academic activities outside of direct clinical care responsibilities.
“Exhaustion that hits you in your pregnancy and then you have to round. I used to lie on the floor of my office, take a little nap, wake up, write some notes, go home, take another nap, wake up, write some more notes.”
Upon return to work, women reported additional physical challenges related to sleep deprivation, impacting their productivity with academic work and emotional well-being.
“I came back from maternity leave and I was sleep-deprived and exhausted, I didn’t have the energy. All of these great projects that I had started or dreamed of … dwindled and died on the vine.”
Solutions suggested by the participants included creation of a flexible schedule with a ramp-up and ramp-down period around the birth.
Breastfeeding
The majority of participants in this study encountered several challenges associated with a shared goal of breastfeeding according to evidence-based guidelines.11 Designated pumping areas were often inconveniently located and not conducive to multitasking.
“It’s two chairs that are behind a curtain in a women’s locker room in the basement of the hospital, that are tiny and gross. No computers, so I felt like I was wasting time.”
One hospitalist described carving out time for pumping in her office while multitasking with clinical work.
“I would get to work, set up, and pump while chart reviewing. Then I would go and see people… and come back to my office and pump and write a few notes. And go out and see more patients, and then pump and write a few more notes. And then pump, and then go home. I was like a cow.”
Women highlighted the barriers that could be optimized such as creating time in the clinical schedule for pumping, a physical space to breastfeed or pump, and accessible milk storage facilities.
Career Opportunities
When asked about the impact of parental leave on career opportunities, a few of the women described a phenomenon of no longer being asked to participate or being left out of prior projects.
“People didn’t want to offer you things or give you things because they realize you’re having this transition in your life. Not out of animosity, but out of courtesy that they don’t want to fill up your place even more. Her plate is full; we are not going to ask her to do anything extra.”
However, two women specifically reported a supportive environment without a loss of opportunities, often referenced as a boss who “saved” projects for their return.
Colleague Responses
One participant used the term “microaggressions,” to describe passive aggressions encountered by their colleagues or leadership.
“(A colleague) was diagnosed with pre-eclampsia, and very urgently had to deliver and couldn’t cover a week of shifts…She was asked initially to find her own coverage…Not treating (pregnancy) similar to other serious illnesses is what I would term a microaggression.”
Yet, women in our study also reported positive responses from colleagues and the importance of support networks of physician mothers (Table 2).
Empathy in Patient Care
Finally, the experience of motherhood impacted all of the women as physicians, described as increased empathy, patience, and understanding of difficult family situations.
“I’m just more sensitive to people’s lives outside the hospital, so, you know, when it’s difficult for a family member to get there because they have three other kids they are taking care of or, somebody that says they are leaving AMA, but it’s because they have a sick kid at home. I just have a better context for that.”
DISCUSSION
Gender disparities persist in both internal medicine and hospital medicine.1 Providers in this descriptive qualitative study suggested that the following factors contribute: lack of paid parental leave and the associated financial penalties, loss of career opportunities, the physical challenges associated with pregnancy, decreasing productivity, and the amount of time and effort involved in breastfeeding. However, the participants also shared valuable ideas for future solutions to relieve the challenges imposed on working physician mothers (Table 2).
Breaking the Glass Ceiling
Participants noted the importance of a paid leave policy that encompasses not only maternity leave but also a flexible scheduling period before and after the leave to account for the challenges of pregnancy and new motherhood. Paid parental leave is rare in academic settings, but studies from other industries show that when women take paid leave, they are more likely to remain in the workforce 9-12 months afterward, work more weekly hours, and feel more loyal to their organization.12,13 In the rare instance when negotiations around leave violate local policy or the law, women should be encouraged to seek guidance from their human resources department.
Me Too: Building Solidarity
Women in our study reported the value of a supportive workplace in easing their transition into motherhood. Specifically, they noted that a supportive boss who protected their career opportunities prevented momentum loss in their career trajectory. Access to mutual supports such as the Physicians Mom Group, a well-established Facebook group comprising more than 70,000 women, was referenced as a meaningful way to share joys and tribulations related to balancing a career as a physician and motherhood. Growth of similar support systems within institutions will further support this experience.
Time’s Up: The Promotion Clock
Women in our study described a prolonged period of diminished productivity related to having children, coinciding with a set time to promotion in academics. Flexible promotion schedules may impact women’s ability to successfully undergo promotion.
FUTURE DIRECTION
The aim of this study was to represent a shared set of experiences of female academic hospitalists who participated; therefore, the results may not be generalizable beyond this group. Due to the use of a purposeful snowball approach, there was a potential for selection bias. Future research may include comparing the experience of women at institutions that offer paid leave versus those that do not and the impact on retention, promotion, and well-being.
CONCLUSION
Women in hospital medicine encounter several challenges to having children, but they are also motivated to provide solutions. Efforts to improve the institutional and cultural landscape to better support women physicians with children are critical to prevent attrition of women and ensure equitable academic promotion and achievement of leadership positions.
Disclosures
The authors have no conflicts of interest to report.
Author Contributions
Each author was involved in the creation of the study protocol, data collection and analysis, and creation of the manuscript.
1. Association of American Medical Colleges. The State of Women in Academic Medicine: The pipeline and pathways to leadership, 2013-2014. https://www.hopkinsmedicine.org/women_science_medicine/_pdfs/The%20State%20of%20Women%20in%20Academic%20Medicine%202013-2014%20FINAL.pdf. Accessed February 26, 2018.
2. Carr PL, Ash AS, Friedman RH, et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Int Med. 1998;129(7):532-538. doi: 10.7326/0003-4819-129-7-199810010-00004. PubMed
3. Burden M, Frank MG, Keniston A, et al. Gender disparities for academic hospitalists. J Hosp Med. 2015;10(8):481-485. doi:10.1002/jhm.2340. PubMed
4. Bristol MN, Abbuhl S, Cappola AR, Sonnad SS. Work-life policies for faculty at the top ten medical schools. J Women’s Health. 2008;17(8):1311-1320. doi: 10.1089/jwh.2007.0682. PubMed
5. Welch JL, Wiehe SE, Palmer-Smith V, Dankoski ME. Flexibility in faculty work-life policies at medical schools in the big ten conference. J Women’s Health. 2011;20(5):725-732. doi: 10.1089/jwh.2010.2553. PubMed
6. Riano NS, Linos E, Accurso EC, et al. Paid family and childbearing leave policies at top US medical schools. JAMA. 2018;319(6):611-614. doi: 10.1001/jama.2017.19519. PubMed
7. Arthur CR, Saenz RB, Replogle WH. The employment-related breastfeeding decisions of physician mothers. J Miss State Med Assoc. 2003;44(12):383-387. PubMed
8. Filtness AJ, MacKenzie J, Armstrong K. Longitudinal change in sleep and daytime sleepiness in postpartum women. PLoS ONE. 2014;9(7):e103513. doi: 10.1371/journal.pone.0103513. PubMed
9. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. doi: 10.1007/s11606-011-1892-5. PubMed
10. Jones J, Nowels CT, Sudore R, Ahluwalia S, Bekelman DB. The future as a series of transitions: qualitative study of heart failure patients and their informal caregivers. J Gen Intern Med. 2015;30(2):176-182. doi: 10.1007/s11606-014-3085-5. PubMed
11. American Academy of Pediatrics. Breastfeeding and the use of human milk. Pediatrics. 2012;129(3):e827-e841. doi: 10.1542/peds.2011-3552. PubMed
12. Houser, L, Vartanian, T. Pay matters: the positive economic impact of paid family Leave for families, businesses and the public. Center for Women and Work at Rutgers. January, 2012. http://go.nationalpartnership.org/site/DocServer/Pay_Matters_Positive_Economic_Impacts_of_Paid_Fam ily_L.pdf?docID=9681. Accessed February 26, 2018.
13. Rossin-Slater M, Ruhm C, Waldfogel J. The effects of California’s paid family leave program on mothers’ leave-taking and subsequent labor market outcomes. J Policy Anal Manage. 2013;32(2):224-2 45. doi: 10.1002/pam.21676. PubMed
Despite recent advances made in medicine, gender-based disparities persist.1-3 In particular, women with children have barriers to career advancement and show evidence of slower career advancement.1,2 Multiple challenges for working women experiencing motherhood have been described. In academic medicine in the United States, women have limited access to paid parental leave.4-6 For women who choose to breastfeed, there is limited time, space, and support available for breastfeeding.7 Furthermore, sleep deprivation in the postpartum period significantly impacts the ability to function at work.8
Hospital medicine is a unique specialty as it comprises 47% women, 80% of whom are aged less than 40 years, suggesting that a large portion are women of childbearing age.9 The field poses known challenges to this population, including shift work, atypical schedules, and unpredictable hours. We conducted a descriptive qualitative study to improve our understanding of the experience of female academic hospitalists who have experienced pregnancy, parental leave, and the return to work as faculty. Our goal was to both explore the challenges to undergoing this experience and discover solutions to support female academic hospitalists.
METHODS
Study Design
We conducted a qualitative descriptive study of female hospitalists recruited from academic institutions represented in Society of Hospital Medicine (SHM) committees. Interviews were conducted between November 2017 and February 2018. Participants completed an informed consent and a demographic survey prior to the interview. Each interview lasted approximately 30 minutes; discussions were recorded on digital records and transcribed verbatim. This protocol was reviewed and granted exemption by the Institutional Review Board at the University of Colorado.
Population
We recruited participants from a selection of hospital medicine groups nationally, chosen from SHM committee representation. A purposeful snowball approach was used to identify hospitalists from representative programs and seek their recommendation for hospitalists from other targeted programs. Ten hospitalists were approached by e-mail to determine their interest in participation, and all of them agreed to participate. Each participant experienced new parenthood within the last seven years.
Framework
We constructed our interview to represent the following timeline associated with having children as it pertains to a hospitalist position: pregnancy, parental leave, and the return to work. The interview guide was structured to invoke the positive aspects, challenges, and solutions within each domain (Appendix 1).
Analysis
Codes were inductively developed from the interview data by a team of three board-certified internal medicine physicians (E.G., A.M., and C.J.), one of whom had prior training and experience with qualitative interviews and analysis (C.J.). Among the coders, two (E.G. and A.M.) conducted the semistructured interviews. Code disparities were reconciled by team consensus, where the primary coder facilitated the discussions. Themes were developed inductively from the codes, and the analysis was completed using a team-based iterative approach that was facilitated using ATLAS.ti.10 Thematic saturation was achieved. This study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
The demographics and the characteristics of the hospital medicine group are shown in Table 1. Although we asked questions about both the positive and challenging aspects of the experience of parenthood, the interviews tended to focus more on the challenges faced and on areas for optimization.
Paid Parental leave
Most of the participants described inadequate paid parental leave, with minimal transparency in the processes for ensuring time off following the birth of their child, resulting in “haggling” with bosses, human resources, and the administrative staff. Rarely was a formal parental leave policy in place. Once a parental leave plan was established, several women reported the financial burden associated with a leave that was partially, or fully, unpaid.
“All of my leave was unpaid. .. managed to finagle short-term disability into paying for it… the system was otherwise set up to screw me financially.”
For the three women who did experience sufficient paid parental leave, they recognized the financial and emotional benefit and suggested that further optimization would include a prebirth schedule to account for the physical challenges and potential complications.
Physical Challenges
All of the women described significant physical challenges when working during pregnancy, resulting in limited bandwidth for additional academic activities outside of direct clinical care responsibilities.
“Exhaustion that hits you in your pregnancy and then you have to round. I used to lie on the floor of my office, take a little nap, wake up, write some notes, go home, take another nap, wake up, write some more notes.”
Upon return to work, women reported additional physical challenges related to sleep deprivation, impacting their productivity with academic work and emotional well-being.
“I came back from maternity leave and I was sleep-deprived and exhausted, I didn’t have the energy. All of these great projects that I had started or dreamed of … dwindled and died on the vine.”
Solutions suggested by the participants included creation of a flexible schedule with a ramp-up and ramp-down period around the birth.
Breastfeeding
The majority of participants in this study encountered several challenges associated with a shared goal of breastfeeding according to evidence-based guidelines.11 Designated pumping areas were often inconveniently located and not conducive to multitasking.
“It’s two chairs that are behind a curtain in a women’s locker room in the basement of the hospital, that are tiny and gross. No computers, so I felt like I was wasting time.”
One hospitalist described carving out time for pumping in her office while multitasking with clinical work.
“I would get to work, set up, and pump while chart reviewing. Then I would go and see people… and come back to my office and pump and write a few notes. And go out and see more patients, and then pump and write a few more notes. And then pump, and then go home. I was like a cow.”
Women highlighted the barriers that could be optimized such as creating time in the clinical schedule for pumping, a physical space to breastfeed or pump, and accessible milk storage facilities.
Career Opportunities
When asked about the impact of parental leave on career opportunities, a few of the women described a phenomenon of no longer being asked to participate or being left out of prior projects.
“People didn’t want to offer you things or give you things because they realize you’re having this transition in your life. Not out of animosity, but out of courtesy that they don’t want to fill up your place even more. Her plate is full; we are not going to ask her to do anything extra.”
However, two women specifically reported a supportive environment without a loss of opportunities, often referenced as a boss who “saved” projects for their return.
Colleague Responses
One participant used the term “microaggressions,” to describe passive aggressions encountered by their colleagues or leadership.
“(A colleague) was diagnosed with pre-eclampsia, and very urgently had to deliver and couldn’t cover a week of shifts…She was asked initially to find her own coverage…Not treating (pregnancy) similar to other serious illnesses is what I would term a microaggression.”
Yet, women in our study also reported positive responses from colleagues and the importance of support networks of physician mothers (Table 2).
Empathy in Patient Care
Finally, the experience of motherhood impacted all of the women as physicians, described as increased empathy, patience, and understanding of difficult family situations.
“I’m just more sensitive to people’s lives outside the hospital, so, you know, when it’s difficult for a family member to get there because they have three other kids they are taking care of or, somebody that says they are leaving AMA, but it’s because they have a sick kid at home. I just have a better context for that.”
DISCUSSION
Gender disparities persist in both internal medicine and hospital medicine.1 Providers in this descriptive qualitative study suggested that the following factors contribute: lack of paid parental leave and the associated financial penalties, loss of career opportunities, the physical challenges associated with pregnancy, decreasing productivity, and the amount of time and effort involved in breastfeeding. However, the participants also shared valuable ideas for future solutions to relieve the challenges imposed on working physician mothers (Table 2).
Breaking the Glass Ceiling
Participants noted the importance of a paid leave policy that encompasses not only maternity leave but also a flexible scheduling period before and after the leave to account for the challenges of pregnancy and new motherhood. Paid parental leave is rare in academic settings, but studies from other industries show that when women take paid leave, they are more likely to remain in the workforce 9-12 months afterward, work more weekly hours, and feel more loyal to their organization.12,13 In the rare instance when negotiations around leave violate local policy or the law, women should be encouraged to seek guidance from their human resources department.
Me Too: Building Solidarity
Women in our study reported the value of a supportive workplace in easing their transition into motherhood. Specifically, they noted that a supportive boss who protected their career opportunities prevented momentum loss in their career trajectory. Access to mutual supports such as the Physicians Mom Group, a well-established Facebook group comprising more than 70,000 women, was referenced as a meaningful way to share joys and tribulations related to balancing a career as a physician and motherhood. Growth of similar support systems within institutions will further support this experience.
Time’s Up: The Promotion Clock
Women in our study described a prolonged period of diminished productivity related to having children, coinciding with a set time to promotion in academics. Flexible promotion schedules may impact women’s ability to successfully undergo promotion.
FUTURE DIRECTION
The aim of this study was to represent a shared set of experiences of female academic hospitalists who participated; therefore, the results may not be generalizable beyond this group. Due to the use of a purposeful snowball approach, there was a potential for selection bias. Future research may include comparing the experience of women at institutions that offer paid leave versus those that do not and the impact on retention, promotion, and well-being.
CONCLUSION
Women in hospital medicine encounter several challenges to having children, but they are also motivated to provide solutions. Efforts to improve the institutional and cultural landscape to better support women physicians with children are critical to prevent attrition of women and ensure equitable academic promotion and achievement of leadership positions.
Disclosures
The authors have no conflicts of interest to report.
Author Contributions
Each author was involved in the creation of the study protocol, data collection and analysis, and creation of the manuscript.
Despite recent advances made in medicine, gender-based disparities persist.1-3 In particular, women with children have barriers to career advancement and show evidence of slower career advancement.1,2 Multiple challenges for working women experiencing motherhood have been described. In academic medicine in the United States, women have limited access to paid parental leave.4-6 For women who choose to breastfeed, there is limited time, space, and support available for breastfeeding.7 Furthermore, sleep deprivation in the postpartum period significantly impacts the ability to function at work.8
Hospital medicine is a unique specialty as it comprises 47% women, 80% of whom are aged less than 40 years, suggesting that a large portion are women of childbearing age.9 The field poses known challenges to this population, including shift work, atypical schedules, and unpredictable hours. We conducted a descriptive qualitative study to improve our understanding of the experience of female academic hospitalists who have experienced pregnancy, parental leave, and the return to work as faculty. Our goal was to both explore the challenges to undergoing this experience and discover solutions to support female academic hospitalists.
METHODS
Study Design
We conducted a qualitative descriptive study of female hospitalists recruited from academic institutions represented in Society of Hospital Medicine (SHM) committees. Interviews were conducted between November 2017 and February 2018. Participants completed an informed consent and a demographic survey prior to the interview. Each interview lasted approximately 30 minutes; discussions were recorded on digital records and transcribed verbatim. This protocol was reviewed and granted exemption by the Institutional Review Board at the University of Colorado.
Population
We recruited participants from a selection of hospital medicine groups nationally, chosen from SHM committee representation. A purposeful snowball approach was used to identify hospitalists from representative programs and seek their recommendation for hospitalists from other targeted programs. Ten hospitalists were approached by e-mail to determine their interest in participation, and all of them agreed to participate. Each participant experienced new parenthood within the last seven years.
Framework
We constructed our interview to represent the following timeline associated with having children as it pertains to a hospitalist position: pregnancy, parental leave, and the return to work. The interview guide was structured to invoke the positive aspects, challenges, and solutions within each domain (Appendix 1).
Analysis
Codes were inductively developed from the interview data by a team of three board-certified internal medicine physicians (E.G., A.M., and C.J.), one of whom had prior training and experience with qualitative interviews and analysis (C.J.). Among the coders, two (E.G. and A.M.) conducted the semistructured interviews. Code disparities were reconciled by team consensus, where the primary coder facilitated the discussions. Themes were developed inductively from the codes, and the analysis was completed using a team-based iterative approach that was facilitated using ATLAS.ti.10 Thematic saturation was achieved. This study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
The demographics and the characteristics of the hospital medicine group are shown in Table 1. Although we asked questions about both the positive and challenging aspects of the experience of parenthood, the interviews tended to focus more on the challenges faced and on areas for optimization.
Paid Parental leave
Most of the participants described inadequate paid parental leave, with minimal transparency in the processes for ensuring time off following the birth of their child, resulting in “haggling” with bosses, human resources, and the administrative staff. Rarely was a formal parental leave policy in place. Once a parental leave plan was established, several women reported the financial burden associated with a leave that was partially, or fully, unpaid.
“All of my leave was unpaid. .. managed to finagle short-term disability into paying for it… the system was otherwise set up to screw me financially.”
For the three women who did experience sufficient paid parental leave, they recognized the financial and emotional benefit and suggested that further optimization would include a prebirth schedule to account for the physical challenges and potential complications.
Physical Challenges
All of the women described significant physical challenges when working during pregnancy, resulting in limited bandwidth for additional academic activities outside of direct clinical care responsibilities.
“Exhaustion that hits you in your pregnancy and then you have to round. I used to lie on the floor of my office, take a little nap, wake up, write some notes, go home, take another nap, wake up, write some more notes.”
Upon return to work, women reported additional physical challenges related to sleep deprivation, impacting their productivity with academic work and emotional well-being.
“I came back from maternity leave and I was sleep-deprived and exhausted, I didn’t have the energy. All of these great projects that I had started or dreamed of … dwindled and died on the vine.”
Solutions suggested by the participants included creation of a flexible schedule with a ramp-up and ramp-down period around the birth.
Breastfeeding
The majority of participants in this study encountered several challenges associated with a shared goal of breastfeeding according to evidence-based guidelines.11 Designated pumping areas were often inconveniently located and not conducive to multitasking.
“It’s two chairs that are behind a curtain in a women’s locker room in the basement of the hospital, that are tiny and gross. No computers, so I felt like I was wasting time.”
One hospitalist described carving out time for pumping in her office while multitasking with clinical work.
“I would get to work, set up, and pump while chart reviewing. Then I would go and see people… and come back to my office and pump and write a few notes. And go out and see more patients, and then pump and write a few more notes. And then pump, and then go home. I was like a cow.”
Women highlighted the barriers that could be optimized such as creating time in the clinical schedule for pumping, a physical space to breastfeed or pump, and accessible milk storage facilities.
Career Opportunities
When asked about the impact of parental leave on career opportunities, a few of the women described a phenomenon of no longer being asked to participate or being left out of prior projects.
“People didn’t want to offer you things or give you things because they realize you’re having this transition in your life. Not out of animosity, but out of courtesy that they don’t want to fill up your place even more. Her plate is full; we are not going to ask her to do anything extra.”
However, two women specifically reported a supportive environment without a loss of opportunities, often referenced as a boss who “saved” projects for their return.
Colleague Responses
One participant used the term “microaggressions,” to describe passive aggressions encountered by their colleagues or leadership.
“(A colleague) was diagnosed with pre-eclampsia, and very urgently had to deliver and couldn’t cover a week of shifts…She was asked initially to find her own coverage…Not treating (pregnancy) similar to other serious illnesses is what I would term a microaggression.”
Yet, women in our study also reported positive responses from colleagues and the importance of support networks of physician mothers (Table 2).
Empathy in Patient Care
Finally, the experience of motherhood impacted all of the women as physicians, described as increased empathy, patience, and understanding of difficult family situations.
“I’m just more sensitive to people’s lives outside the hospital, so, you know, when it’s difficult for a family member to get there because they have three other kids they are taking care of or, somebody that says they are leaving AMA, but it’s because they have a sick kid at home. I just have a better context for that.”
DISCUSSION
Gender disparities persist in both internal medicine and hospital medicine.1 Providers in this descriptive qualitative study suggested that the following factors contribute: lack of paid parental leave and the associated financial penalties, loss of career opportunities, the physical challenges associated with pregnancy, decreasing productivity, and the amount of time and effort involved in breastfeeding. However, the participants also shared valuable ideas for future solutions to relieve the challenges imposed on working physician mothers (Table 2).
Breaking the Glass Ceiling
Participants noted the importance of a paid leave policy that encompasses not only maternity leave but also a flexible scheduling period before and after the leave to account for the challenges of pregnancy and new motherhood. Paid parental leave is rare in academic settings, but studies from other industries show that when women take paid leave, they are more likely to remain in the workforce 9-12 months afterward, work more weekly hours, and feel more loyal to their organization.12,13 In the rare instance when negotiations around leave violate local policy or the law, women should be encouraged to seek guidance from their human resources department.
Me Too: Building Solidarity
Women in our study reported the value of a supportive workplace in easing their transition into motherhood. Specifically, they noted that a supportive boss who protected their career opportunities prevented momentum loss in their career trajectory. Access to mutual supports such as the Physicians Mom Group, a well-established Facebook group comprising more than 70,000 women, was referenced as a meaningful way to share joys and tribulations related to balancing a career as a physician and motherhood. Growth of similar support systems within institutions will further support this experience.
Time’s Up: The Promotion Clock
Women in our study described a prolonged period of diminished productivity related to having children, coinciding with a set time to promotion in academics. Flexible promotion schedules may impact women’s ability to successfully undergo promotion.
FUTURE DIRECTION
The aim of this study was to represent a shared set of experiences of female academic hospitalists who participated; therefore, the results may not be generalizable beyond this group. Due to the use of a purposeful snowball approach, there was a potential for selection bias. Future research may include comparing the experience of women at institutions that offer paid leave versus those that do not and the impact on retention, promotion, and well-being.
CONCLUSION
Women in hospital medicine encounter several challenges to having children, but they are also motivated to provide solutions. Efforts to improve the institutional and cultural landscape to better support women physicians with children are critical to prevent attrition of women and ensure equitable academic promotion and achievement of leadership positions.
Disclosures
The authors have no conflicts of interest to report.
Author Contributions
Each author was involved in the creation of the study protocol, data collection and analysis, and creation of the manuscript.
1. Association of American Medical Colleges. The State of Women in Academic Medicine: The pipeline and pathways to leadership, 2013-2014. https://www.hopkinsmedicine.org/women_science_medicine/_pdfs/The%20State%20of%20Women%20in%20Academic%20Medicine%202013-2014%20FINAL.pdf. Accessed February 26, 2018.
2. Carr PL, Ash AS, Friedman RH, et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Int Med. 1998;129(7):532-538. doi: 10.7326/0003-4819-129-7-199810010-00004. PubMed
3. Burden M, Frank MG, Keniston A, et al. Gender disparities for academic hospitalists. J Hosp Med. 2015;10(8):481-485. doi:10.1002/jhm.2340. PubMed
4. Bristol MN, Abbuhl S, Cappola AR, Sonnad SS. Work-life policies for faculty at the top ten medical schools. J Women’s Health. 2008;17(8):1311-1320. doi: 10.1089/jwh.2007.0682. PubMed
5. Welch JL, Wiehe SE, Palmer-Smith V, Dankoski ME. Flexibility in faculty work-life policies at medical schools in the big ten conference. J Women’s Health. 2011;20(5):725-732. doi: 10.1089/jwh.2010.2553. PubMed
6. Riano NS, Linos E, Accurso EC, et al. Paid family and childbearing leave policies at top US medical schools. JAMA. 2018;319(6):611-614. doi: 10.1001/jama.2017.19519. PubMed
7. Arthur CR, Saenz RB, Replogle WH. The employment-related breastfeeding decisions of physician mothers. J Miss State Med Assoc. 2003;44(12):383-387. PubMed
8. Filtness AJ, MacKenzie J, Armstrong K. Longitudinal change in sleep and daytime sleepiness in postpartum women. PLoS ONE. 2014;9(7):e103513. doi: 10.1371/journal.pone.0103513. PubMed
9. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. doi: 10.1007/s11606-011-1892-5. PubMed
10. Jones J, Nowels CT, Sudore R, Ahluwalia S, Bekelman DB. The future as a series of transitions: qualitative study of heart failure patients and their informal caregivers. J Gen Intern Med. 2015;30(2):176-182. doi: 10.1007/s11606-014-3085-5. PubMed
11. American Academy of Pediatrics. Breastfeeding and the use of human milk. Pediatrics. 2012;129(3):e827-e841. doi: 10.1542/peds.2011-3552. PubMed
12. Houser, L, Vartanian, T. Pay matters: the positive economic impact of paid family Leave for families, businesses and the public. Center for Women and Work at Rutgers. January, 2012. http://go.nationalpartnership.org/site/DocServer/Pay_Matters_Positive_Economic_Impacts_of_Paid_Fam ily_L.pdf?docID=9681. Accessed February 26, 2018.
13. Rossin-Slater M, Ruhm C, Waldfogel J. The effects of California’s paid family leave program on mothers’ leave-taking and subsequent labor market outcomes. J Policy Anal Manage. 2013;32(2):224-2 45. doi: 10.1002/pam.21676. PubMed
1. Association of American Medical Colleges. The State of Women in Academic Medicine: The pipeline and pathways to leadership, 2013-2014. https://www.hopkinsmedicine.org/women_science_medicine/_pdfs/The%20State%20of%20Women%20in%20Academic%20Medicine%202013-2014%20FINAL.pdf. Accessed February 26, 2018.
2. Carr PL, Ash AS, Friedman RH, et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Int Med. 1998;129(7):532-538. doi: 10.7326/0003-4819-129-7-199810010-00004. PubMed
3. Burden M, Frank MG, Keniston A, et al. Gender disparities for academic hospitalists. J Hosp Med. 2015;10(8):481-485. doi:10.1002/jhm.2340. PubMed
4. Bristol MN, Abbuhl S, Cappola AR, Sonnad SS. Work-life policies for faculty at the top ten medical schools. J Women’s Health. 2008;17(8):1311-1320. doi: 10.1089/jwh.2007.0682. PubMed
5. Welch JL, Wiehe SE, Palmer-Smith V, Dankoski ME. Flexibility in faculty work-life policies at medical schools in the big ten conference. J Women’s Health. 2011;20(5):725-732. doi: 10.1089/jwh.2010.2553. PubMed
6. Riano NS, Linos E, Accurso EC, et al. Paid family and childbearing leave policies at top US medical schools. JAMA. 2018;319(6):611-614. doi: 10.1001/jama.2017.19519. PubMed
7. Arthur CR, Saenz RB, Replogle WH. The employment-related breastfeeding decisions of physician mothers. J Miss State Med Assoc. 2003;44(12):383-387. PubMed
8. Filtness AJ, MacKenzie J, Armstrong K. Longitudinal change in sleep and daytime sleepiness in postpartum women. PLoS ONE. 2014;9(7):e103513. doi: 10.1371/journal.pone.0103513. PubMed
9. Reid MB, Misky GJ, Harrison RA, Sharpe B, Auerbach A, Glasheen JJ. Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27(1):23-27. doi: 10.1007/s11606-011-1892-5. PubMed
10. Jones J, Nowels CT, Sudore R, Ahluwalia S, Bekelman DB. The future as a series of transitions: qualitative study of heart failure patients and their informal caregivers. J Gen Intern Med. 2015;30(2):176-182. doi: 10.1007/s11606-014-3085-5. PubMed
11. American Academy of Pediatrics. Breastfeeding and the use of human milk. Pediatrics. 2012;129(3):e827-e841. doi: 10.1542/peds.2011-3552. PubMed
12. Houser, L, Vartanian, T. Pay matters: the positive economic impact of paid family Leave for families, businesses and the public. Center for Women and Work at Rutgers. January, 2012. http://go.nationalpartnership.org/site/DocServer/Pay_Matters_Positive_Economic_Impacts_of_Paid_Fam ily_L.pdf?docID=9681. Accessed February 26, 2018.
13. Rossin-Slater M, Ruhm C, Waldfogel J. The effects of California’s paid family leave program on mothers’ leave-taking and subsequent labor market outcomes. J Policy Anal Manage. 2013;32(2):224-2 45. doi: 10.1002/pam.21676. PubMed
© 2018 Society of Hospital Medicine
Real‐Time Patient Experience Surveys
In 2010, the Centers for Medicare and Medicaid Services implemented value‐based purchasing, a payment model that incentivizes hospitals for reaching certain quality and patient experience thresholds and penalizes those that do not, in part on the basis of patient satisfaction scores.[1] Although low patient satisfaction scores will adversely affect institutions financially, they also reflect patients' perceptions of their care. Some studies suggest that hospitals with higher patient satisfaction scores score higher overall on clinical care processes such as core measures compliance, readmission rates, lower mortality rates, and other quality‐of‐care metrics.[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey assesses patients' experience following their hospital stay.[1] The percent of top box scores (ie, response of always on a four point scale, or scores of 9 or 10 on a 10‐point scale) are utilized to compare hospitals and determine the reimbursement or penalty a hospital will receive. Although these scores are available to the public on the Hospital Compare website,[12] physicians may not know how their hospital is ranked or how they are individually perceived by their patients. Additionally, these surveys are typically conducted 48 hours to 6 weeks after patients are discharged, and the results are distributed back to the hospitals well after the time that care was provided, thereby offering providers no chance of improving patient satisfaction during a given hospital stay.
Institutions across the country are trying to improve their HCAHPS scores, but there is limited research identifying specific measures providers can implement. Some studies have suggested that utilizing etiquette‐based communication and sitting at the bedside[13, 14] may help improve patient experience with their providers, and more recently, it has been suggested that providing real‐time deidentified patient experience survey results with education and a rewards/emncentive system to residents may help as well.[15]
Surveys conducted during a patient's hospitalization can offer real‐time actionable feedback to providers. We performed a quality‐improvement project that was designed to determine if real‐time feedback to hospitalist physicians, followed by coaching, and revisits to the patients' bedside could improve the results recorded on provider‐specific patient surveys and/or patients' HCAHPS scores or percentile rankings.
METHODS
Design
This was a prospective, randomized quality‐improvement initiative that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a 525‐bed university‐affiliated public safety net hospital. The initiative was conducted on both teaching and nonteaching general internal medicine services, which typically have a daily census of between 10 and 15 patients. No protocol changes occurred during the study.
Participants
Participants included all English‐ or Spanish‐speaking patients who were hospitalized on a general internal medicine service, had been admitted within the 2 days prior to enrollment, and had a hospitalist as their attending physician. Patients were excluded if they were enrolled in the study during a previous hospitalization, refused to participate, lacked capacity to participate, had hearing or speech impediments precluding regular conversation, were prisoners, if their clinical condition precluded participation, or their attending was an investigator in the project.
Intervention
Participants were prescreened by investigators by reviewing team sign‐outs to determine if patients had any exclusion criteria. Investigators attempted to survey each patient who met inclusion criteria on a daily basis between 9:00 am and 11:00 am. An investigator administered the survey to each patient verbally using scripted language. Patients were asked to rate how well their doctors were listening to them, explaining what they wanted to know, and whether the doctors were being friendly and helpful, all questions taken from a survey that was available on the US Department of Health and Human Services website (to be referred to as here forward daily survey).[16] We converted the original 5‐point Likert scale used in this survey to a 4‐point scale by removing the option of ok, leaving participants the options of poor, fair, good, or great. Patients were also asked to provide any personalized feedback they had, and these comments were recorded in writing by the investigator.
After being surveyed on day 1, patients were randomized to an intervention or control group using an automated randomization module in Research Electronic Data Capture (REDCap).[17] Patients in both groups who did not provide answers to all 3 questions that qualified as being top box (ie, great) were resurveyed on a daily basis until their responses were all top box or they were discharged, met exclusion criteria, or had been surveyed for a total of 4 consecutive days. In the pilot phase of this study, we found that if patients reported all top box scores on the initial survey their responses typically did not change over time, and the patients became frustrated if asked the same questions again when the patient felt there was not room for improvement. Accordingly, we elected to stop surveying patients when all top box responses were reported.
The attending hospitalist caring for each patient in the intervention group was given feedback about their patients' survey results (both their scores and any specific comments) on a daily basis. Feedback was provided in person by 1 of the investigators. The hospitalist also received an automatically generated electronic mail message with the survey results at 11:00 am on each study day. After informing the hospitalists of the patients' scores, the investigator provided a brief education session that included discussing Denver Health's most recent HCAHPS scores, value‐based purchasing, and the financial consequences of poor patient satisfaction scores. The investigator then coached the hospitalist on etiquette‐based communication,[18, 19] suggested that they sit down when communicating with their patients,[19, 20] and then asked the hospitalist to revisit each patient to discuss how the team could improve in any of the 3 areas where the patient did not give a top box score. These educational sessions were conducted in person and lasted a maximum of 5 minutes. An investigator followed up with each hospitalist the following day to determine whether the revisit occurred. Hospitalists caring for patients who were randomized to the control group were not given real‐time feedback or coaching and were not asked to revisit patients.
A random sample of patients surveyed for this initiative also received HCAHPS surveys 48 hours to 6 weeks following their hospital discharge, according to the standard methodology used to acquire HCAHPS data,[21] by an outside vendor contracted by Denver Health. Our vendor conducted these surveys via telephone in English or Spanish.
Outcomes
The primary outcome was the proportion of patients in each group who reported top box scores on the daily surveys. Secondary outcomes included the percent change for the scores recorded for 3 provider‐specific questions from the daily survey, the median top box HCAHPS scores for the 3 provider related questions and overall hospital rating, and the HCAHPS percentiles of top box scores for these questions.
Sample Size
The sample size for this intervention assumed that the proportion of patients whose treating physicians did not receive real‐time feedback who rated their providers as top box would be 75%, and that the effect of providing real‐time feedback would increase this proportion to 85% on the daily surveys. To have 80% power with a type 1 error of 0.05, we estimated a need to enroll 430 patients, 215 in each group.
Statistics
Data were collected and managed using a secure, Web‐based electronic data capture tool hosted at Denver Health (REDCap), which is designed to support data collection for research studies providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[17]
A 2 test was used to compare the proportion of patients in the 2 groups who reported great scores for each question on the study survey on the first and last day. With the intent of providing a framework for understanding the effect real‐time feedback could have on patient experience, a secondary analysis of HCAHPS results was conducted using several different methods.
First, the proportion of patients in the 2 groups who reported scores of 9 or 10 for the overall hospital rating question or reported always for each doctor communication question on the HCHAPS survey was compared using a 2. Second, to allow for detection of differences in a sample with a smaller N, the median overall hospital rating scores from the HCAHPS survey reported by patients in the 2 groups who completed a survey following discharge were compared using a Wilcoxon rank sum test. Lastly, to place changes in proportion into a larger context (ie, how these changes would relate to value‐based purchasing), HCAHPS scores were converted to percentiles of national performance using the 2014 percentile rankings obtained from the external vendor that conducts the HCAHPS surveys for our hospital and compared between the intervention and control groups using a Wilcoxon rank sum test.
All comments collected from patients during their daily surveys were reviewed, and key words were abstracted from each comment. These key words were sorted and reviewed to categorize recurring key words into themes. Exemplars were then selected for each theme derived from patient comments.
RESULTS
From April 14, 2014 to September 19, 2014, we enrolled 227 patients in the control group and 228 in the intervention group (Figure 1). Patient demographics are summarized in Table 1. Of the 132 patients in the intervention group who reported anything less than top box scores for any of the 3 questions (thus prompting a revisit by their provider), 106 (80%) were revisited by their provider at least once during their hospitalization.
All Patients | HCAHPS Patients | |||
---|---|---|---|---|
Control, N = 227 | Intervention, N = 228 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Age, mean SD | 55 14 | 55 15 | 55 15 | 57 16 |
Gender | ||||
Male | 126 (60) | 121 (55) | 20 (57) | 12 (40) |
Female | 85 (40) | 98 (45) | 15(43) | 18 (60) |
Race/ethnicity | ||||
Hispanic | 84 (40) | 90 (41) | 17 (49) | 12 (40) |
Black | 38 (18) | 28 (13) | 6 (17) | 7 (23) |
White | 87 (41) | 97 (44) | 12 (34) | 10 (33) |
Other | 2 (1) | 4 (2) | 0 (0) | 1 (3) |
Payer | ||||
Medicare | 65 (29) | 82 (36) | 15 (43) | 12 (40) |
Medicaid | 122 (54) | 108 (47) | 17 (49) | 14 (47) |
Commercial | 12 (5) | 15 (7) | 1 (3) | 1 (3) |
Medically indigent | 4 (2) | 7 (3) | 0 (0) | 3 (10) |
Self‐pay | 5 (2) | 4 (2) | 1 (3) | 0 (0) |
Other/unknown | 19 (8) | 12 (5) | 0 (0) | 0 (0) |
Team | ||||
Teaching | 187 (82) | 196 (86) | 27 (77) | 24 (80) |
Nonteaching | 40 (18) | 32 (14) | 8 (23) | 6 (20) |
Top 5 primary discharge diagnoses* | ||||
Septicemia | 26 (11) | 34 (15) | 3 (9) | 5 (17) |
Heart failure | 14 (6) | 13 (6) | 2 (6) | |
Acute pancreatitis | 12 (5) | 9 (4) | 3 (9) | 2 (7) |
Diabetes mellitus | 11 (5) | 8 (4) | 2 (6) | |
Alcohol withdrawal | 9 (4) | |||
Cellulitis | 7 (3) | 2 (7) | ||
Pulmonary embolism | 2 (7) | |||
Chest pain | 2 (7) | |||
Atrial fibrillation | 2 (6) | |||
Length of stay, median (IQR) | 3 (2, 5) | 3 (2, 5) | 3 (2, 5) | 3 (2, 4) |
Charlson Comorbidity Index, median (IQR) | 1 (0, 3) | 2 (0, 3) | 1 (0, 3) | 1.5 (1, 3) |
Daily Surveys
The proportion of patients in both study groups reporting top box scores tended to increase from the first day to the last day of the survey (Figure 2); however, we found no statistically significant differences between the proportion of patients who reported top box scores on first day or last day in the intervention group compared to the control group. The comments made by the patients are summarized in Supporting Table 1 in the online version of this article.
HCAHPS Scores
The proportion of top box scores from the HCAHPS surveys were higher, though not statistically significant, for all 3 provider‐specific questions and for the overall hospital rating for patients whose hospitalists received real‐time feedback (Table 2). The median [interquartile range] score for the overall hospital rating was higher for patients in the intervention group compared with those in the control group, (10 [9, 10] vs 9 [8, 10], P = 0.04]. After converting the HCAHPS scores to percentiles, we found considerably higher rankings for all 3 provider‐related questions and for the overall hospital rating in the intervention group compared to the control group (P = 0.02 for overall differences in percentiles [Table 2]).
HCAHPS Questions | Proportion Top Box* | Percentile Rank | ||
---|---|---|---|---|
Control, N = 35 | Intervention, N = 30 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Overall hospital rating | 61% | 80% | 6 | 87 |
Courtesy/respect | 86% | 93% | 23 | 88 |
Clear communication | 77% | 80% | 39 | 60 |
Listening | 83% | 90% | 57 | 95 |
No adverse events occurred during the course of the study in either group.
DISCUSSION
The important findings of this study were that (1) daily patient satisfaction scores improved from first day to last day regardless of study group, (2) patients whose providers received real‐time feedback had a trend toward higher HCAHPS proportions for the 3 provider‐related questions as well as the overall rating of the hospital but were not statistically significant, (3) the percentile differences in these 3 questions as well as the overall rating of the hospital were significantly higher in the intervention group as was the median score for the overall hospital rating.
Our original sample size calculation was based upon our own preliminary data, indicating that our baseline top box scores for the daily survey was around 75%. The daily survey top box score on the first day was, however, much lower (Figure 2). Accordingly, although we did not find a significant difference in these daily scores, we were underpowered to find such a difference. Additionally, because only a small percentage of patients are selected for the HCAHPS survey, our ability to detect a difference in this secondary outcome was also limited. We felt that it was important to analyze the percentile comparisons in addition to the proportion of top box scores on the HCAHPS, because the metrics for value‐based purchasing are based upon, in part, how a hospital system compares to other systems. Finally, to improve our power to detect a difference given a small sample size, we converted the scoring system for overall hospital ranking to a continuous variable, which again was noted to be significant.
To our knowledge, this is the first randomized investigation designed to assess the effect of real‐time, patient‐specific feedback to physicians. Real‐time feedback is increasingly being incorporated into medical practice, but there is only limited information available describing how this type of feedback affects outcomes.[22, 23, 24] Banka et al.[15] found that HCAHPS scores improved as a result of real‐time feedback given to residents, but the study was not randomized, utilized a pre‐post design that resulted in there being differences between the patients studied before and after the intervention, and did not provide patient‐specific data to the residents. Tabib et al.[25] found that operating costs decreased 17% after instituting real‐time feedback to providers about these costs. Reeves et al.[26] conducted a cluster randomized trial of a patient feedback survey that was designed to improve nursing care, but the results were reviewed by the nurses several months after patients had been discharged.
The differences in median top box scores and percentile rank that we observed could have resulted from the real‐time feedback, the educational coaching, the fact that the providers revisited the majority of the patients, or a combination of all of the above. Gross et al.[27] found that longer visits lead to higher satisfaction, though others have not found this to necessarily be the case.[28, 29] Lin et al.[30] found that patient satisfaction was affected by the perceived duration of the visit as well as whether expectations on visit length were met and/or exceeded. Brown et al.[31] found that training providers in communication skills improved the providers perception of their communication skills, although patient experience scores did not improve. We feel that the results seen are more likely a combination thereof as opposed to any 1 component of the intervention.
The most commonly reported complaints or concerns in patients' undirected comments often related to communication issues. Comments on subsequent surveys suggested that patient satisfaction improved over time in the intervention group, indicating that perhaps physicians did try to improve in areas that were highlighted by the real‐time feedback, and that patients perceived the physician efforts to do so (eg, They're doing better than the last time you asked. They sat down and talked to me and listened better. They came back and explained to me about my care. They listened better. They should do this survey at the clinic. See Supporting Table 1 in the online version of this article).
Our study has several limitations. First, we did not randomize providers, and many of our providers (approximately 65%) participated in both the control group and also in the intervention group, and thus received real‐time feedback at some point during the study, which could have affected their overall practice and limited our ability to find a difference between the 2 groups. In an attempt to control for this possibility, the study was conducted on an intermittent basis during the study time frame. Furthermore, the proportion of patients who reported top box scores at the beginning of the study did not have a clear trend of change by the end of the study, suggesting that overall clinician practices with respect to patient satisfaction did not change during this short time period.
Second, only a small number of our patients were randomly selected for the HCAHPS survey, which limited our ability to detect significant differences in HCAHPS proportions. Third, the HCAHPS percentiles at our institution at that time were low. Accordingly, the improvements that we observed in patient satisfaction scores might not be reproducible at institutions with higher satisfactions scores. Fourth, time and resources were needed to obtain patient feedback to provide to providers during this study. There are, however, other ways to obtain feedback that are less resource intensive (eg, electronic feedback, the utilization of volunteers, or partnering this with manager rounding). Finally, the study was conducted at a single, university‐affiliated public teaching hospital and was a quality‐improvement initiative, and thus our results are not generalizable to other institutions.
In conclusion, real‐time feedback of patient experience to their providers, coupled with provider education, coaching, and revisits, seems to improve satisfaction of patients hospitalized on general internal medicine units who were cared for by hospitalists.
Acknowledgements
The authors thank Kate Fagan, MPH, for her excellent technical assistance.
Disclosure: Nothing to report.
- HCAHPS Fact Sheet. 2015. Available at: http://www.hcahpsonline.org/Files/HCAHPS_Fact_Sheet_June_2015.pdf. Accessed August 25, 2015.
- The relationship between commercial website ratings and traditional hospital performance measures in the USA. BMJ Qual Saf. 2013;22:194–202. , , , .
- Patients' perception of hospital care in the United States. N Engl J Med. 2008;359:1921–1931. , , , .
- The relationship between patients' perception of care and measures of hospital quality and safety. Health Serv Res. 2010;45:1024–1040. , , , .
- Relationship between quality of diabetes care and patient satisfaction. J Natl Med Assoc. 2003;95:64–70. , , , et al.
- Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41–48. , , , , .
- A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1). , , .
- The association between satisfaction with services provided in primary care and outcomes in type 2 diabetes mellitus. Diabet Med. 2003;20:486–490. , .
- Associations between Web‐based patient ratings and objective measures of hospital quality. Arch Intern Med. 2012;172:435–436. , , , et al.
- Patient satisfaction and its relationship with clinical quality and inpatient mortality in acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2010;3:188–195. , , , et al.
- Patients' perceptions of care are associated with quality of hospital care: a survey of 4605 hospitals. Am J Med Qual. 2015;30(4):382–388. , , , , .
- Centers for Medicare 28:908–913.
- Effect of sitting vs. standing on perception of provider time at bedside: a pilot study. Patient Educ Couns. 2012;86:166–171. , , , , , .
- Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10:497–502. , , , et al.
- US Department of Health and Human Services. Patient satisfaction survey. Available at: http://bphc.hrsa.gov/policiesregulations/performancemeasures/patientsurvey/surveyform.html. Accessed November 15, 2013.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- The HCAHPS Handbook. Gulf Breeze, FL: Fire Starter; 2010. .
- Etiquette‐based medicine. N Engl J Med. 2008;358:1988–1989. .
- 5 years after the Kahn's etiquette‐based medicine: a brief checklist proposal for a functional second meeting with the patient. Front Psychol. 2013;4:723. .
- Frequently Asked Questions. Hospital Value‐Based Purchasing Program. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/hospital‐value‐based‐purchasing/Downloads/FY‐2013‐Program‐Frequently‐Asked‐Questions‐about‐Hospital‐VBP‐3‐9‐12.pdf. Accessed February 8, 2014.
- Real‐time patient survey data during routine clinical activities for rapid‐cycle quality improvement. JMIR Med Inform. 2015;3:e13. , , , .
- Mount Sinai launches real‐time patient‐feedback survey tool. Healthcare Informatics website. Available at: http://www.healthcare‐informatics.com/news‐item/mount‐sinai‐launches‐real‐time‐patient‐feedback‐survey‐tool. Accessed August 25, 2015. .
- Hospitals are finally starting to put real‐time data to use. Harvard Business Review website. Available at: https://hbr.org/2014/11/hospitals‐are‐finally‐starting‐to‐put‐real‐time‐data‐to‐use. Published November 12, 2014. Accessed August 25, 2015. , .
- Reducing operating room costs through real‐time cost information feedback: a pilot study. J Endourol. 2015;29:963–968. , , , , .
- Facilitated patient experience feedback can improve nursing care: a pilot study for a phase III cluster randomised controlled trial. BMC Health Serv Res. 2013;13:259. , , .
- Patient satisfaction with time spent with their physician. J Fam Pract. 1998;47:133–137. , , , , .
- The relationship between time spent communicating and communication outcomes on a hospital medicine service. J Gen Intern Med. 2012;27:185–189. , , , , , .
- Cognitive interview techniques reveal specific behaviors and issues that could affect patient satisfaction relative to hospitalists. J Hosp Med. 2009;4:E1–E6. , .
- Is patients' perception of time spent with the physician a determinant of ambulatory patient satisfaction? Arch Intern Med. 2001;161:1437–1442. , , , et al.
- Effect of clinician communication skills training on patient satisfaction. A randomized, controlled trial. Ann Intern Med. 1999;131:822–829. , , , .
In 2010, the Centers for Medicare and Medicaid Services implemented value‐based purchasing, a payment model that incentivizes hospitals for reaching certain quality and patient experience thresholds and penalizes those that do not, in part on the basis of patient satisfaction scores.[1] Although low patient satisfaction scores will adversely affect institutions financially, they also reflect patients' perceptions of their care. Some studies suggest that hospitals with higher patient satisfaction scores score higher overall on clinical care processes such as core measures compliance, readmission rates, lower mortality rates, and other quality‐of‐care metrics.[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey assesses patients' experience following their hospital stay.[1] The percent of top box scores (ie, response of always on a four point scale, or scores of 9 or 10 on a 10‐point scale) are utilized to compare hospitals and determine the reimbursement or penalty a hospital will receive. Although these scores are available to the public on the Hospital Compare website,[12] physicians may not know how their hospital is ranked or how they are individually perceived by their patients. Additionally, these surveys are typically conducted 48 hours to 6 weeks after patients are discharged, and the results are distributed back to the hospitals well after the time that care was provided, thereby offering providers no chance of improving patient satisfaction during a given hospital stay.
Institutions across the country are trying to improve their HCAHPS scores, but there is limited research identifying specific measures providers can implement. Some studies have suggested that utilizing etiquette‐based communication and sitting at the bedside[13, 14] may help improve patient experience with their providers, and more recently, it has been suggested that providing real‐time deidentified patient experience survey results with education and a rewards/emncentive system to residents may help as well.[15]
Surveys conducted during a patient's hospitalization can offer real‐time actionable feedback to providers. We performed a quality‐improvement project that was designed to determine if real‐time feedback to hospitalist physicians, followed by coaching, and revisits to the patients' bedside could improve the results recorded on provider‐specific patient surveys and/or patients' HCAHPS scores or percentile rankings.
METHODS
Design
This was a prospective, randomized quality‐improvement initiative that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a 525‐bed university‐affiliated public safety net hospital. The initiative was conducted on both teaching and nonteaching general internal medicine services, which typically have a daily census of between 10 and 15 patients. No protocol changes occurred during the study.
Participants
Participants included all English‐ or Spanish‐speaking patients who were hospitalized on a general internal medicine service, had been admitted within the 2 days prior to enrollment, and had a hospitalist as their attending physician. Patients were excluded if they were enrolled in the study during a previous hospitalization, refused to participate, lacked capacity to participate, had hearing or speech impediments precluding regular conversation, were prisoners, if their clinical condition precluded participation, or their attending was an investigator in the project.
Intervention
Participants were prescreened by investigators by reviewing team sign‐outs to determine if patients had any exclusion criteria. Investigators attempted to survey each patient who met inclusion criteria on a daily basis between 9:00 am and 11:00 am. An investigator administered the survey to each patient verbally using scripted language. Patients were asked to rate how well their doctors were listening to them, explaining what they wanted to know, and whether the doctors were being friendly and helpful, all questions taken from a survey that was available on the US Department of Health and Human Services website (to be referred to as here forward daily survey).[16] We converted the original 5‐point Likert scale used in this survey to a 4‐point scale by removing the option of ok, leaving participants the options of poor, fair, good, or great. Patients were also asked to provide any personalized feedback they had, and these comments were recorded in writing by the investigator.
After being surveyed on day 1, patients were randomized to an intervention or control group using an automated randomization module in Research Electronic Data Capture (REDCap).[17] Patients in both groups who did not provide answers to all 3 questions that qualified as being top box (ie, great) were resurveyed on a daily basis until their responses were all top box or they were discharged, met exclusion criteria, or had been surveyed for a total of 4 consecutive days. In the pilot phase of this study, we found that if patients reported all top box scores on the initial survey their responses typically did not change over time, and the patients became frustrated if asked the same questions again when the patient felt there was not room for improvement. Accordingly, we elected to stop surveying patients when all top box responses were reported.
The attending hospitalist caring for each patient in the intervention group was given feedback about their patients' survey results (both their scores and any specific comments) on a daily basis. Feedback was provided in person by 1 of the investigators. The hospitalist also received an automatically generated electronic mail message with the survey results at 11:00 am on each study day. After informing the hospitalists of the patients' scores, the investigator provided a brief education session that included discussing Denver Health's most recent HCAHPS scores, value‐based purchasing, and the financial consequences of poor patient satisfaction scores. The investigator then coached the hospitalist on etiquette‐based communication,[18, 19] suggested that they sit down when communicating with their patients,[19, 20] and then asked the hospitalist to revisit each patient to discuss how the team could improve in any of the 3 areas where the patient did not give a top box score. These educational sessions were conducted in person and lasted a maximum of 5 minutes. An investigator followed up with each hospitalist the following day to determine whether the revisit occurred. Hospitalists caring for patients who were randomized to the control group were not given real‐time feedback or coaching and were not asked to revisit patients.
A random sample of patients surveyed for this initiative also received HCAHPS surveys 48 hours to 6 weeks following their hospital discharge, according to the standard methodology used to acquire HCAHPS data,[21] by an outside vendor contracted by Denver Health. Our vendor conducted these surveys via telephone in English or Spanish.
Outcomes
The primary outcome was the proportion of patients in each group who reported top box scores on the daily surveys. Secondary outcomes included the percent change for the scores recorded for 3 provider‐specific questions from the daily survey, the median top box HCAHPS scores for the 3 provider related questions and overall hospital rating, and the HCAHPS percentiles of top box scores for these questions.
Sample Size
The sample size for this intervention assumed that the proportion of patients whose treating physicians did not receive real‐time feedback who rated their providers as top box would be 75%, and that the effect of providing real‐time feedback would increase this proportion to 85% on the daily surveys. To have 80% power with a type 1 error of 0.05, we estimated a need to enroll 430 patients, 215 in each group.
Statistics
Data were collected and managed using a secure, Web‐based electronic data capture tool hosted at Denver Health (REDCap), which is designed to support data collection for research studies providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[17]
A 2 test was used to compare the proportion of patients in the 2 groups who reported great scores for each question on the study survey on the first and last day. With the intent of providing a framework for understanding the effect real‐time feedback could have on patient experience, a secondary analysis of HCAHPS results was conducted using several different methods.
First, the proportion of patients in the 2 groups who reported scores of 9 or 10 for the overall hospital rating question or reported always for each doctor communication question on the HCHAPS survey was compared using a 2. Second, to allow for detection of differences in a sample with a smaller N, the median overall hospital rating scores from the HCAHPS survey reported by patients in the 2 groups who completed a survey following discharge were compared using a Wilcoxon rank sum test. Lastly, to place changes in proportion into a larger context (ie, how these changes would relate to value‐based purchasing), HCAHPS scores were converted to percentiles of national performance using the 2014 percentile rankings obtained from the external vendor that conducts the HCAHPS surveys for our hospital and compared between the intervention and control groups using a Wilcoxon rank sum test.
All comments collected from patients during their daily surveys were reviewed, and key words were abstracted from each comment. These key words were sorted and reviewed to categorize recurring key words into themes. Exemplars were then selected for each theme derived from patient comments.
RESULTS
From April 14, 2014 to September 19, 2014, we enrolled 227 patients in the control group and 228 in the intervention group (Figure 1). Patient demographics are summarized in Table 1. Of the 132 patients in the intervention group who reported anything less than top box scores for any of the 3 questions (thus prompting a revisit by their provider), 106 (80%) were revisited by their provider at least once during their hospitalization.
All Patients | HCAHPS Patients | |||
---|---|---|---|---|
Control, N = 227 | Intervention, N = 228 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Age, mean SD | 55 14 | 55 15 | 55 15 | 57 16 |
Gender | ||||
Male | 126 (60) | 121 (55) | 20 (57) | 12 (40) |
Female | 85 (40) | 98 (45) | 15(43) | 18 (60) |
Race/ethnicity | ||||
Hispanic | 84 (40) | 90 (41) | 17 (49) | 12 (40) |
Black | 38 (18) | 28 (13) | 6 (17) | 7 (23) |
White | 87 (41) | 97 (44) | 12 (34) | 10 (33) |
Other | 2 (1) | 4 (2) | 0 (0) | 1 (3) |
Payer | ||||
Medicare | 65 (29) | 82 (36) | 15 (43) | 12 (40) |
Medicaid | 122 (54) | 108 (47) | 17 (49) | 14 (47) |
Commercial | 12 (5) | 15 (7) | 1 (3) | 1 (3) |
Medically indigent | 4 (2) | 7 (3) | 0 (0) | 3 (10) |
Self‐pay | 5 (2) | 4 (2) | 1 (3) | 0 (0) |
Other/unknown | 19 (8) | 12 (5) | 0 (0) | 0 (0) |
Team | ||||
Teaching | 187 (82) | 196 (86) | 27 (77) | 24 (80) |
Nonteaching | 40 (18) | 32 (14) | 8 (23) | 6 (20) |
Top 5 primary discharge diagnoses* | ||||
Septicemia | 26 (11) | 34 (15) | 3 (9) | 5 (17) |
Heart failure | 14 (6) | 13 (6) | 2 (6) | |
Acute pancreatitis | 12 (5) | 9 (4) | 3 (9) | 2 (7) |
Diabetes mellitus | 11 (5) | 8 (4) | 2 (6) | |
Alcohol withdrawal | 9 (4) | |||
Cellulitis | 7 (3) | 2 (7) | ||
Pulmonary embolism | 2 (7) | |||
Chest pain | 2 (7) | |||
Atrial fibrillation | 2 (6) | |||
Length of stay, median (IQR) | 3 (2, 5) | 3 (2, 5) | 3 (2, 5) | 3 (2, 4) |
Charlson Comorbidity Index, median (IQR) | 1 (0, 3) | 2 (0, 3) | 1 (0, 3) | 1.5 (1, 3) |
Daily Surveys
The proportion of patients in both study groups reporting top box scores tended to increase from the first day to the last day of the survey (Figure 2); however, we found no statistically significant differences between the proportion of patients who reported top box scores on first day or last day in the intervention group compared to the control group. The comments made by the patients are summarized in Supporting Table 1 in the online version of this article.
HCAHPS Scores
The proportion of top box scores from the HCAHPS surveys were higher, though not statistically significant, for all 3 provider‐specific questions and for the overall hospital rating for patients whose hospitalists received real‐time feedback (Table 2). The median [interquartile range] score for the overall hospital rating was higher for patients in the intervention group compared with those in the control group, (10 [9, 10] vs 9 [8, 10], P = 0.04]. After converting the HCAHPS scores to percentiles, we found considerably higher rankings for all 3 provider‐related questions and for the overall hospital rating in the intervention group compared to the control group (P = 0.02 for overall differences in percentiles [Table 2]).
HCAHPS Questions | Proportion Top Box* | Percentile Rank | ||
---|---|---|---|---|
Control, N = 35 | Intervention, N = 30 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Overall hospital rating | 61% | 80% | 6 | 87 |
Courtesy/respect | 86% | 93% | 23 | 88 |
Clear communication | 77% | 80% | 39 | 60 |
Listening | 83% | 90% | 57 | 95 |
No adverse events occurred during the course of the study in either group.
DISCUSSION
The important findings of this study were that (1) daily patient satisfaction scores improved from first day to last day regardless of study group, (2) patients whose providers received real‐time feedback had a trend toward higher HCAHPS proportions for the 3 provider‐related questions as well as the overall rating of the hospital but were not statistically significant, (3) the percentile differences in these 3 questions as well as the overall rating of the hospital were significantly higher in the intervention group as was the median score for the overall hospital rating.
Our original sample size calculation was based upon our own preliminary data, indicating that our baseline top box scores for the daily survey was around 75%. The daily survey top box score on the first day was, however, much lower (Figure 2). Accordingly, although we did not find a significant difference in these daily scores, we were underpowered to find such a difference. Additionally, because only a small percentage of patients are selected for the HCAHPS survey, our ability to detect a difference in this secondary outcome was also limited. We felt that it was important to analyze the percentile comparisons in addition to the proportion of top box scores on the HCAHPS, because the metrics for value‐based purchasing are based upon, in part, how a hospital system compares to other systems. Finally, to improve our power to detect a difference given a small sample size, we converted the scoring system for overall hospital ranking to a continuous variable, which again was noted to be significant.
To our knowledge, this is the first randomized investigation designed to assess the effect of real‐time, patient‐specific feedback to physicians. Real‐time feedback is increasingly being incorporated into medical practice, but there is only limited information available describing how this type of feedback affects outcomes.[22, 23, 24] Banka et al.[15] found that HCAHPS scores improved as a result of real‐time feedback given to residents, but the study was not randomized, utilized a pre‐post design that resulted in there being differences between the patients studied before and after the intervention, and did not provide patient‐specific data to the residents. Tabib et al.[25] found that operating costs decreased 17% after instituting real‐time feedback to providers about these costs. Reeves et al.[26] conducted a cluster randomized trial of a patient feedback survey that was designed to improve nursing care, but the results were reviewed by the nurses several months after patients had been discharged.
The differences in median top box scores and percentile rank that we observed could have resulted from the real‐time feedback, the educational coaching, the fact that the providers revisited the majority of the patients, or a combination of all of the above. Gross et al.[27] found that longer visits lead to higher satisfaction, though others have not found this to necessarily be the case.[28, 29] Lin et al.[30] found that patient satisfaction was affected by the perceived duration of the visit as well as whether expectations on visit length were met and/or exceeded. Brown et al.[31] found that training providers in communication skills improved the providers perception of their communication skills, although patient experience scores did not improve. We feel that the results seen are more likely a combination thereof as opposed to any 1 component of the intervention.
The most commonly reported complaints or concerns in patients' undirected comments often related to communication issues. Comments on subsequent surveys suggested that patient satisfaction improved over time in the intervention group, indicating that perhaps physicians did try to improve in areas that were highlighted by the real‐time feedback, and that patients perceived the physician efforts to do so (eg, They're doing better than the last time you asked. They sat down and talked to me and listened better. They came back and explained to me about my care. They listened better. They should do this survey at the clinic. See Supporting Table 1 in the online version of this article).
Our study has several limitations. First, we did not randomize providers, and many of our providers (approximately 65%) participated in both the control group and also in the intervention group, and thus received real‐time feedback at some point during the study, which could have affected their overall practice and limited our ability to find a difference between the 2 groups. In an attempt to control for this possibility, the study was conducted on an intermittent basis during the study time frame. Furthermore, the proportion of patients who reported top box scores at the beginning of the study did not have a clear trend of change by the end of the study, suggesting that overall clinician practices with respect to patient satisfaction did not change during this short time period.
Second, only a small number of our patients were randomly selected for the HCAHPS survey, which limited our ability to detect significant differences in HCAHPS proportions. Third, the HCAHPS percentiles at our institution at that time were low. Accordingly, the improvements that we observed in patient satisfaction scores might not be reproducible at institutions with higher satisfactions scores. Fourth, time and resources were needed to obtain patient feedback to provide to providers during this study. There are, however, other ways to obtain feedback that are less resource intensive (eg, electronic feedback, the utilization of volunteers, or partnering this with manager rounding). Finally, the study was conducted at a single, university‐affiliated public teaching hospital and was a quality‐improvement initiative, and thus our results are not generalizable to other institutions.
In conclusion, real‐time feedback of patient experience to their providers, coupled with provider education, coaching, and revisits, seems to improve satisfaction of patients hospitalized on general internal medicine units who were cared for by hospitalists.
Acknowledgements
The authors thank Kate Fagan, MPH, for her excellent technical assistance.
Disclosure: Nothing to report.
In 2010, the Centers for Medicare and Medicaid Services implemented value‐based purchasing, a payment model that incentivizes hospitals for reaching certain quality and patient experience thresholds and penalizes those that do not, in part on the basis of patient satisfaction scores.[1] Although low patient satisfaction scores will adversely affect institutions financially, they also reflect patients' perceptions of their care. Some studies suggest that hospitals with higher patient satisfaction scores score higher overall on clinical care processes such as core measures compliance, readmission rates, lower mortality rates, and other quality‐of‐care metrics.[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey assesses patients' experience following their hospital stay.[1] The percent of top box scores (ie, response of always on a four point scale, or scores of 9 or 10 on a 10‐point scale) are utilized to compare hospitals and determine the reimbursement or penalty a hospital will receive. Although these scores are available to the public on the Hospital Compare website,[12] physicians may not know how their hospital is ranked or how they are individually perceived by their patients. Additionally, these surveys are typically conducted 48 hours to 6 weeks after patients are discharged, and the results are distributed back to the hospitals well after the time that care was provided, thereby offering providers no chance of improving patient satisfaction during a given hospital stay.
Institutions across the country are trying to improve their HCAHPS scores, but there is limited research identifying specific measures providers can implement. Some studies have suggested that utilizing etiquette‐based communication and sitting at the bedside[13, 14] may help improve patient experience with their providers, and more recently, it has been suggested that providing real‐time deidentified patient experience survey results with education and a rewards/emncentive system to residents may help as well.[15]
Surveys conducted during a patient's hospitalization can offer real‐time actionable feedback to providers. We performed a quality‐improvement project that was designed to determine if real‐time feedback to hospitalist physicians, followed by coaching, and revisits to the patients' bedside could improve the results recorded on provider‐specific patient surveys and/or patients' HCAHPS scores or percentile rankings.
METHODS
Design
This was a prospective, randomized quality‐improvement initiative that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a 525‐bed university‐affiliated public safety net hospital. The initiative was conducted on both teaching and nonteaching general internal medicine services, which typically have a daily census of between 10 and 15 patients. No protocol changes occurred during the study.
Participants
Participants included all English‐ or Spanish‐speaking patients who were hospitalized on a general internal medicine service, had been admitted within the 2 days prior to enrollment, and had a hospitalist as their attending physician. Patients were excluded if they were enrolled in the study during a previous hospitalization, refused to participate, lacked capacity to participate, had hearing or speech impediments precluding regular conversation, were prisoners, if their clinical condition precluded participation, or their attending was an investigator in the project.
Intervention
Participants were prescreened by investigators by reviewing team sign‐outs to determine if patients had any exclusion criteria. Investigators attempted to survey each patient who met inclusion criteria on a daily basis between 9:00 am and 11:00 am. An investigator administered the survey to each patient verbally using scripted language. Patients were asked to rate how well their doctors were listening to them, explaining what they wanted to know, and whether the doctors were being friendly and helpful, all questions taken from a survey that was available on the US Department of Health and Human Services website (to be referred to as here forward daily survey).[16] We converted the original 5‐point Likert scale used in this survey to a 4‐point scale by removing the option of ok, leaving participants the options of poor, fair, good, or great. Patients were also asked to provide any personalized feedback they had, and these comments were recorded in writing by the investigator.
After being surveyed on day 1, patients were randomized to an intervention or control group using an automated randomization module in Research Electronic Data Capture (REDCap).[17] Patients in both groups who did not provide answers to all 3 questions that qualified as being top box (ie, great) were resurveyed on a daily basis until their responses were all top box or they were discharged, met exclusion criteria, or had been surveyed for a total of 4 consecutive days. In the pilot phase of this study, we found that if patients reported all top box scores on the initial survey their responses typically did not change over time, and the patients became frustrated if asked the same questions again when the patient felt there was not room for improvement. Accordingly, we elected to stop surveying patients when all top box responses were reported.
The attending hospitalist caring for each patient in the intervention group was given feedback about their patients' survey results (both their scores and any specific comments) on a daily basis. Feedback was provided in person by 1 of the investigators. The hospitalist also received an automatically generated electronic mail message with the survey results at 11:00 am on each study day. After informing the hospitalists of the patients' scores, the investigator provided a brief education session that included discussing Denver Health's most recent HCAHPS scores, value‐based purchasing, and the financial consequences of poor patient satisfaction scores. The investigator then coached the hospitalist on etiquette‐based communication,[18, 19] suggested that they sit down when communicating with their patients,[19, 20] and then asked the hospitalist to revisit each patient to discuss how the team could improve in any of the 3 areas where the patient did not give a top box score. These educational sessions were conducted in person and lasted a maximum of 5 minutes. An investigator followed up with each hospitalist the following day to determine whether the revisit occurred. Hospitalists caring for patients who were randomized to the control group were not given real‐time feedback or coaching and were not asked to revisit patients.
A random sample of patients surveyed for this initiative also received HCAHPS surveys 48 hours to 6 weeks following their hospital discharge, according to the standard methodology used to acquire HCAHPS data,[21] by an outside vendor contracted by Denver Health. Our vendor conducted these surveys via telephone in English or Spanish.
Outcomes
The primary outcome was the proportion of patients in each group who reported top box scores on the daily surveys. Secondary outcomes included the percent change for the scores recorded for 3 provider‐specific questions from the daily survey, the median top box HCAHPS scores for the 3 provider related questions and overall hospital rating, and the HCAHPS percentiles of top box scores for these questions.
Sample Size
The sample size for this intervention assumed that the proportion of patients whose treating physicians did not receive real‐time feedback who rated their providers as top box would be 75%, and that the effect of providing real‐time feedback would increase this proportion to 85% on the daily surveys. To have 80% power with a type 1 error of 0.05, we estimated a need to enroll 430 patients, 215 in each group.
Statistics
Data were collected and managed using a secure, Web‐based electronic data capture tool hosted at Denver Health (REDCap), which is designed to support data collection for research studies providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[17]
A 2 test was used to compare the proportion of patients in the 2 groups who reported great scores for each question on the study survey on the first and last day. With the intent of providing a framework for understanding the effect real‐time feedback could have on patient experience, a secondary analysis of HCAHPS results was conducted using several different methods.
First, the proportion of patients in the 2 groups who reported scores of 9 or 10 for the overall hospital rating question or reported always for each doctor communication question on the HCHAPS survey was compared using a 2. Second, to allow for detection of differences in a sample with a smaller N, the median overall hospital rating scores from the HCAHPS survey reported by patients in the 2 groups who completed a survey following discharge were compared using a Wilcoxon rank sum test. Lastly, to place changes in proportion into a larger context (ie, how these changes would relate to value‐based purchasing), HCAHPS scores were converted to percentiles of national performance using the 2014 percentile rankings obtained from the external vendor that conducts the HCAHPS surveys for our hospital and compared between the intervention and control groups using a Wilcoxon rank sum test.
All comments collected from patients during their daily surveys were reviewed, and key words were abstracted from each comment. These key words were sorted and reviewed to categorize recurring key words into themes. Exemplars were then selected for each theme derived from patient comments.
RESULTS
From April 14, 2014 to September 19, 2014, we enrolled 227 patients in the control group and 228 in the intervention group (Figure 1). Patient demographics are summarized in Table 1. Of the 132 patients in the intervention group who reported anything less than top box scores for any of the 3 questions (thus prompting a revisit by their provider), 106 (80%) were revisited by their provider at least once during their hospitalization.
All Patients | HCAHPS Patients | |||
---|---|---|---|---|
Control, N = 227 | Intervention, N = 228 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Age, mean SD | 55 14 | 55 15 | 55 15 | 57 16 |
Gender | ||||
Male | 126 (60) | 121 (55) | 20 (57) | 12 (40) |
Female | 85 (40) | 98 (45) | 15(43) | 18 (60) |
Race/ethnicity | ||||
Hispanic | 84 (40) | 90 (41) | 17 (49) | 12 (40) |
Black | 38 (18) | 28 (13) | 6 (17) | 7 (23) |
White | 87 (41) | 97 (44) | 12 (34) | 10 (33) |
Other | 2 (1) | 4 (2) | 0 (0) | 1 (3) |
Payer | ||||
Medicare | 65 (29) | 82 (36) | 15 (43) | 12 (40) |
Medicaid | 122 (54) | 108 (47) | 17 (49) | 14 (47) |
Commercial | 12 (5) | 15 (7) | 1 (3) | 1 (3) |
Medically indigent | 4 (2) | 7 (3) | 0 (0) | 3 (10) |
Self‐pay | 5 (2) | 4 (2) | 1 (3) | 0 (0) |
Other/unknown | 19 (8) | 12 (5) | 0 (0) | 0 (0) |
Team | ||||
Teaching | 187 (82) | 196 (86) | 27 (77) | 24 (80) |
Nonteaching | 40 (18) | 32 (14) | 8 (23) | 6 (20) |
Top 5 primary discharge diagnoses* | ||||
Septicemia | 26 (11) | 34 (15) | 3 (9) | 5 (17) |
Heart failure | 14 (6) | 13 (6) | 2 (6) | |
Acute pancreatitis | 12 (5) | 9 (4) | 3 (9) | 2 (7) |
Diabetes mellitus | 11 (5) | 8 (4) | 2 (6) | |
Alcohol withdrawal | 9 (4) | |||
Cellulitis | 7 (3) | 2 (7) | ||
Pulmonary embolism | 2 (7) | |||
Chest pain | 2 (7) | |||
Atrial fibrillation | 2 (6) | |||
Length of stay, median (IQR) | 3 (2, 5) | 3 (2, 5) | 3 (2, 5) | 3 (2, 4) |
Charlson Comorbidity Index, median (IQR) | 1 (0, 3) | 2 (0, 3) | 1 (0, 3) | 1.5 (1, 3) |
Daily Surveys
The proportion of patients in both study groups reporting top box scores tended to increase from the first day to the last day of the survey (Figure 2); however, we found no statistically significant differences between the proportion of patients who reported top box scores on first day or last day in the intervention group compared to the control group. The comments made by the patients are summarized in Supporting Table 1 in the online version of this article.
HCAHPS Scores
The proportion of top box scores from the HCAHPS surveys were higher, though not statistically significant, for all 3 provider‐specific questions and for the overall hospital rating for patients whose hospitalists received real‐time feedback (Table 2). The median [interquartile range] score for the overall hospital rating was higher for patients in the intervention group compared with those in the control group, (10 [9, 10] vs 9 [8, 10], P = 0.04]. After converting the HCAHPS scores to percentiles, we found considerably higher rankings for all 3 provider‐related questions and for the overall hospital rating in the intervention group compared to the control group (P = 0.02 for overall differences in percentiles [Table 2]).
HCAHPS Questions | Proportion Top Box* | Percentile Rank | ||
---|---|---|---|---|
Control, N = 35 | Intervention, N = 30 | Control, N = 35 | Intervention, N = 30 | |
| ||||
Overall hospital rating | 61% | 80% | 6 | 87 |
Courtesy/respect | 86% | 93% | 23 | 88 |
Clear communication | 77% | 80% | 39 | 60 |
Listening | 83% | 90% | 57 | 95 |
No adverse events occurred during the course of the study in either group.
DISCUSSION
The important findings of this study were that (1) daily patient satisfaction scores improved from first day to last day regardless of study group, (2) patients whose providers received real‐time feedback had a trend toward higher HCAHPS proportions for the 3 provider‐related questions as well as the overall rating of the hospital but were not statistically significant, (3) the percentile differences in these 3 questions as well as the overall rating of the hospital were significantly higher in the intervention group as was the median score for the overall hospital rating.
Our original sample size calculation was based upon our own preliminary data, indicating that our baseline top box scores for the daily survey was around 75%. The daily survey top box score on the first day was, however, much lower (Figure 2). Accordingly, although we did not find a significant difference in these daily scores, we were underpowered to find such a difference. Additionally, because only a small percentage of patients are selected for the HCAHPS survey, our ability to detect a difference in this secondary outcome was also limited. We felt that it was important to analyze the percentile comparisons in addition to the proportion of top box scores on the HCAHPS, because the metrics for value‐based purchasing are based upon, in part, how a hospital system compares to other systems. Finally, to improve our power to detect a difference given a small sample size, we converted the scoring system for overall hospital ranking to a continuous variable, which again was noted to be significant.
To our knowledge, this is the first randomized investigation designed to assess the effect of real‐time, patient‐specific feedback to physicians. Real‐time feedback is increasingly being incorporated into medical practice, but there is only limited information available describing how this type of feedback affects outcomes.[22, 23, 24] Banka et al.[15] found that HCAHPS scores improved as a result of real‐time feedback given to residents, but the study was not randomized, utilized a pre‐post design that resulted in there being differences between the patients studied before and after the intervention, and did not provide patient‐specific data to the residents. Tabib et al.[25] found that operating costs decreased 17% after instituting real‐time feedback to providers about these costs. Reeves et al.[26] conducted a cluster randomized trial of a patient feedback survey that was designed to improve nursing care, but the results were reviewed by the nurses several months after patients had been discharged.
The differences in median top box scores and percentile rank that we observed could have resulted from the real‐time feedback, the educational coaching, the fact that the providers revisited the majority of the patients, or a combination of all of the above. Gross et al.[27] found that longer visits lead to higher satisfaction, though others have not found this to necessarily be the case.[28, 29] Lin et al.[30] found that patient satisfaction was affected by the perceived duration of the visit as well as whether expectations on visit length were met and/or exceeded. Brown et al.[31] found that training providers in communication skills improved the providers perception of their communication skills, although patient experience scores did not improve. We feel that the results seen are more likely a combination thereof as opposed to any 1 component of the intervention.
The most commonly reported complaints or concerns in patients' undirected comments often related to communication issues. Comments on subsequent surveys suggested that patient satisfaction improved over time in the intervention group, indicating that perhaps physicians did try to improve in areas that were highlighted by the real‐time feedback, and that patients perceived the physician efforts to do so (eg, They're doing better than the last time you asked. They sat down and talked to me and listened better. They came back and explained to me about my care. They listened better. They should do this survey at the clinic. See Supporting Table 1 in the online version of this article).
Our study has several limitations. First, we did not randomize providers, and many of our providers (approximately 65%) participated in both the control group and also in the intervention group, and thus received real‐time feedback at some point during the study, which could have affected their overall practice and limited our ability to find a difference between the 2 groups. In an attempt to control for this possibility, the study was conducted on an intermittent basis during the study time frame. Furthermore, the proportion of patients who reported top box scores at the beginning of the study did not have a clear trend of change by the end of the study, suggesting that overall clinician practices with respect to patient satisfaction did not change during this short time period.
Second, only a small number of our patients were randomly selected for the HCAHPS survey, which limited our ability to detect significant differences in HCAHPS proportions. Third, the HCAHPS percentiles at our institution at that time were low. Accordingly, the improvements that we observed in patient satisfaction scores might not be reproducible at institutions with higher satisfactions scores. Fourth, time and resources were needed to obtain patient feedback to provide to providers during this study. There are, however, other ways to obtain feedback that are less resource intensive (eg, electronic feedback, the utilization of volunteers, or partnering this with manager rounding). Finally, the study was conducted at a single, university‐affiliated public teaching hospital and was a quality‐improvement initiative, and thus our results are not generalizable to other institutions.
In conclusion, real‐time feedback of patient experience to their providers, coupled with provider education, coaching, and revisits, seems to improve satisfaction of patients hospitalized on general internal medicine units who were cared for by hospitalists.
Acknowledgements
The authors thank Kate Fagan, MPH, for her excellent technical assistance.
Disclosure: Nothing to report.
- HCAHPS Fact Sheet. 2015. Available at: http://www.hcahpsonline.org/Files/HCAHPS_Fact_Sheet_June_2015.pdf. Accessed August 25, 2015.
- The relationship between commercial website ratings and traditional hospital performance measures in the USA. BMJ Qual Saf. 2013;22:194–202. , , , .
- Patients' perception of hospital care in the United States. N Engl J Med. 2008;359:1921–1931. , , , .
- The relationship between patients' perception of care and measures of hospital quality and safety. Health Serv Res. 2010;45:1024–1040. , , , .
- Relationship between quality of diabetes care and patient satisfaction. J Natl Med Assoc. 2003;95:64–70. , , , et al.
- Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41–48. , , , , .
- A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1). , , .
- The association between satisfaction with services provided in primary care and outcomes in type 2 diabetes mellitus. Diabet Med. 2003;20:486–490. , .
- Associations between Web‐based patient ratings and objective measures of hospital quality. Arch Intern Med. 2012;172:435–436. , , , et al.
- Patient satisfaction and its relationship with clinical quality and inpatient mortality in acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2010;3:188–195. , , , et al.
- Patients' perceptions of care are associated with quality of hospital care: a survey of 4605 hospitals. Am J Med Qual. 2015;30(4):382–388. , , , , .
- Centers for Medicare 28:908–913.
- Effect of sitting vs. standing on perception of provider time at bedside: a pilot study. Patient Educ Couns. 2012;86:166–171. , , , , , .
- Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10:497–502. , , , et al.
- US Department of Health and Human Services. Patient satisfaction survey. Available at: http://bphc.hrsa.gov/policiesregulations/performancemeasures/patientsurvey/surveyform.html. Accessed November 15, 2013.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- The HCAHPS Handbook. Gulf Breeze, FL: Fire Starter; 2010. .
- Etiquette‐based medicine. N Engl J Med. 2008;358:1988–1989. .
- 5 years after the Kahn's etiquette‐based medicine: a brief checklist proposal for a functional second meeting with the patient. Front Psychol. 2013;4:723. .
- Frequently Asked Questions. Hospital Value‐Based Purchasing Program. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/hospital‐value‐based‐purchasing/Downloads/FY‐2013‐Program‐Frequently‐Asked‐Questions‐about‐Hospital‐VBP‐3‐9‐12.pdf. Accessed February 8, 2014.
- Real‐time patient survey data during routine clinical activities for rapid‐cycle quality improvement. JMIR Med Inform. 2015;3:e13. , , , .
- Mount Sinai launches real‐time patient‐feedback survey tool. Healthcare Informatics website. Available at: http://www.healthcare‐informatics.com/news‐item/mount‐sinai‐launches‐real‐time‐patient‐feedback‐survey‐tool. Accessed August 25, 2015. .
- Hospitals are finally starting to put real‐time data to use. Harvard Business Review website. Available at: https://hbr.org/2014/11/hospitals‐are‐finally‐starting‐to‐put‐real‐time‐data‐to‐use. Published November 12, 2014. Accessed August 25, 2015. , .
- Reducing operating room costs through real‐time cost information feedback: a pilot study. J Endourol. 2015;29:963–968. , , , , .
- Facilitated patient experience feedback can improve nursing care: a pilot study for a phase III cluster randomised controlled trial. BMC Health Serv Res. 2013;13:259. , , .
- Patient satisfaction with time spent with their physician. J Fam Pract. 1998;47:133–137. , , , , .
- The relationship between time spent communicating and communication outcomes on a hospital medicine service. J Gen Intern Med. 2012;27:185–189. , , , , , .
- Cognitive interview techniques reveal specific behaviors and issues that could affect patient satisfaction relative to hospitalists. J Hosp Med. 2009;4:E1–E6. , .
- Is patients' perception of time spent with the physician a determinant of ambulatory patient satisfaction? Arch Intern Med. 2001;161:1437–1442. , , , et al.
- Effect of clinician communication skills training on patient satisfaction. A randomized, controlled trial. Ann Intern Med. 1999;131:822–829. , , , .
- HCAHPS Fact Sheet. 2015. Available at: http://www.hcahpsonline.org/Files/HCAHPS_Fact_Sheet_June_2015.pdf. Accessed August 25, 2015.
- The relationship between commercial website ratings and traditional hospital performance measures in the USA. BMJ Qual Saf. 2013;22:194–202. , , , .
- Patients' perception of hospital care in the United States. N Engl J Med. 2008;359:1921–1931. , , , .
- The relationship between patients' perception of care and measures of hospital quality and safety. Health Serv Res. 2010;45:1024–1040. , , , .
- Relationship between quality of diabetes care and patient satisfaction. J Natl Med Assoc. 2003;95:64–70. , , , et al.
- Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41–48. , , , , .
- A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1). , , .
- The association between satisfaction with services provided in primary care and outcomes in type 2 diabetes mellitus. Diabet Med. 2003;20:486–490. , .
- Associations between Web‐based patient ratings and objective measures of hospital quality. Arch Intern Med. 2012;172:435–436. , , , et al.
- Patient satisfaction and its relationship with clinical quality and inpatient mortality in acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2010;3:188–195. , , , et al.
- Patients' perceptions of care are associated with quality of hospital care: a survey of 4605 hospitals. Am J Med Qual. 2015;30(4):382–388. , , , , .
- Centers for Medicare 28:908–913.
- Effect of sitting vs. standing on perception of provider time at bedside: a pilot study. Patient Educ Couns. 2012;86:166–171. , , , , , .
- Improving patient satisfaction through physician education, feedback, and incentives. J Hosp Med. 2015;10:497–502. , , , et al.
- US Department of Health and Human Services. Patient satisfaction survey. Available at: http://bphc.hrsa.gov/policiesregulations/performancemeasures/patientsurvey/surveyform.html. Accessed November 15, 2013.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- The HCAHPS Handbook. Gulf Breeze, FL: Fire Starter; 2010. .
- Etiquette‐based medicine. N Engl J Med. 2008;358:1988–1989. .
- 5 years after the Kahn's etiquette‐based medicine: a brief checklist proposal for a functional second meeting with the patient. Front Psychol. 2013;4:723. .
- Frequently Asked Questions. Hospital Value‐Based Purchasing Program. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/hospital‐value‐based‐purchasing/Downloads/FY‐2013‐Program‐Frequently‐Asked‐Questions‐about‐Hospital‐VBP‐3‐9‐12.pdf. Accessed February 8, 2014.
- Real‐time patient survey data during routine clinical activities for rapid‐cycle quality improvement. JMIR Med Inform. 2015;3:e13. , , , .
- Mount Sinai launches real‐time patient‐feedback survey tool. Healthcare Informatics website. Available at: http://www.healthcare‐informatics.com/news‐item/mount‐sinai‐launches‐real‐time‐patient‐feedback‐survey‐tool. Accessed August 25, 2015. .
- Hospitals are finally starting to put real‐time data to use. Harvard Business Review website. Available at: https://hbr.org/2014/11/hospitals‐are‐finally‐starting‐to‐put‐real‐time‐data‐to‐use. Published November 12, 2014. Accessed August 25, 2015. , .
- Reducing operating room costs through real‐time cost information feedback: a pilot study. J Endourol. 2015;29:963–968. , , , , .
- Facilitated patient experience feedback can improve nursing care: a pilot study for a phase III cluster randomised controlled trial. BMC Health Serv Res. 2013;13:259. , , .
- Patient satisfaction with time spent with their physician. J Fam Pract. 1998;47:133–137. , , , , .
- The relationship between time spent communicating and communication outcomes on a hospital medicine service. J Gen Intern Med. 2012;27:185–189. , , , , , .
- Cognitive interview techniques reveal specific behaviors and issues that could affect patient satisfaction relative to hospitalists. J Hosp Med. 2009;4:E1–E6. , .
- Is patients' perception of time spent with the physician a determinant of ambulatory patient satisfaction? Arch Intern Med. 2001;161:1437–1442. , , , et al.
- Effect of clinician communication skills training on patient satisfaction. A randomized, controlled trial. Ann Intern Med. 1999;131:822–829. , , , .
© 2016 Society of Hospital Medicine
Gender Disparities for Academic Hospitalists
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
Male, N (%) | Female, N (%) | |
---|---|---|
| ||
Hospitalists | ||
All presentations | 411 (74) | 146 (26)* |
Featured or plenary presentations | 49 (91) | 5 (9)* |
General internists | ||
All presentations | 289 (50) | 291 (50) |
Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
First Author | Last Author | |||
---|---|---|---|---|
Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
Hospitalists | ||||
All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
General internists | ||||
All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538. , , , et al.
- The “gender gap” in authorship of academic medical literature—a 35‐year perspective. N Engl J Med. 2006;355:281–287. , , , et al.
- Sex differences in academic advancement. Results of a national study of pediatricians. N Engl J Med. 1996;335:1282–1289. , , , , , .
- Women physicians in academic medicine: new insights from cohort studies. N Engl J Med. 2000;342:399–405. .
- Gender differences in academic productivity and leadership appointments of physicians throughout academic careers. Acad Med. 2011;86:43–47. , , , , .
- Promotion of women physicians in academic medicine. Glass ceiling or sticky floor? JAMA. 1995;273:1022–1025. , , , .
- Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141:205–212. , , , .
- Women physicians: choosing a career in academic medicine. Acad Med. 2012;87:105–114. , , .
- The status of women at one academic medical center. Breaking through the glass ceiling. JAMA. 1990;264:1813–1817. , , , .
- Status of women in academic anesthesiology. Anesthesiology. 1986;64:496–500. , .
- The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians. BMC Health Serv Res. 2006;6:55. , , .
- Pew Research Center. On pay gap, millenial women near parity—for now. December 2013. Available at: http://www.pewsocialtrends.org/files/2013/12/gender-and-work_final.pdf. Published December 11, 2013. Accessed February 5, 2015.
- The emerging role of "hospitalists" in the American health care system. N Engl J Med. 1996;335:514–517. , .
- Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27:23–27. , , , , , .
- The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014. .
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9. , , , .
- State of Hospital Medicine: 2011 Report Based on 2010 Data. Medical Group Management Association and Society of Hospital Medicine. www.mgma.com, www.hospitalmedicine.org.
- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
- American Medical Association. Graduate Medical Education Directory 2012–2013. Chicago, IL: American Medical Association; 2012:182–203.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- Association of American Medical Colleges. 2012 Physician Specialty Data Book. Center for Workforce Studies. Available at: https://www.aamc.org/download/313228/data/2012physicianspecialtydatabook.pdf. Published November 2012. Accessed September 1, 2014.
- Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635. , , , .
- Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186. , , , et al.
- Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896. , , , et al.
- Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629. , , , .
- A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018. , , , , .
- Perceived obstacles to career success for women in academic surgery. Arch Surg. 2000;135:972–977. , , .
- Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426. , , , .
- Doing the same and earning less: male and female physicians in a new medical specialty. Inquiry. 2004;41:301–315. .
- Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353. , , , , , .
- Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758. , , , , .
- The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201. , , , .
- Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207. , , , , .
- Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014. .
- Mentoring in academic medicine: a systematic review. JAMA. 2006;296:1103–1115. , , .
- Inequality quantified: mind the gender gap. Nature. 2013;495:22–24. .
- Gender differences in academic advancement: patterns, causes, and potential solutions in one US College of Medicine. Acad Med. 2003;78:500–508. , , , et al.
- Why aren't there more women leaders in academic medicine? The views of clinical department chairs. Acad Med. 2001;76:453–465. , .
- Gender factors in reviewer recommendations for manuscript publication. J Appl Behav Anal. 1990;23:539–543. .
- Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16. , , , .
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
Male, N (%) | Female, N (%) | |
---|---|---|
| ||
Hospitalists | ||
All presentations | 411 (74) | 146 (26)* |
Featured or plenary presentations | 49 (91) | 5 (9)* |
General internists | ||
All presentations | 289 (50) | 291 (50) |
Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
First Author | Last Author | |||
---|---|---|---|---|
Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
Hospitalists | ||||
All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
General internists | ||||
All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
Male, N (%) | Female, N (%) | |
---|---|---|
| ||
Hospitalists | ||
All presentations | 411 (74) | 146 (26)* |
Featured or plenary presentations | 49 (91) | 5 (9)* |
General internists | ||
All presentations | 289 (50) | 291 (50) |
Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
First Author | Last Author | |||
---|---|---|---|---|
Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
Hospitalists | ||||
All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
General internists | ||||
All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538. , , , et al.
- The “gender gap” in authorship of academic medical literature—a 35‐year perspective. N Engl J Med. 2006;355:281–287. , , , et al.
- Sex differences in academic advancement. Results of a national study of pediatricians. N Engl J Med. 1996;335:1282–1289. , , , , , .
- Women physicians in academic medicine: new insights from cohort studies. N Engl J Med. 2000;342:399–405. .
- Gender differences in academic productivity and leadership appointments of physicians throughout academic careers. Acad Med. 2011;86:43–47. , , , , .
- Promotion of women physicians in academic medicine. Glass ceiling or sticky floor? JAMA. 1995;273:1022–1025. , , , .
- Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141:205–212. , , , .
- Women physicians: choosing a career in academic medicine. Acad Med. 2012;87:105–114. , , .
- The status of women at one academic medical center. Breaking through the glass ceiling. JAMA. 1990;264:1813–1817. , , , .
- Status of women in academic anesthesiology. Anesthesiology. 1986;64:496–500. , .
- The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians. BMC Health Serv Res. 2006;6:55. , , .
- Pew Research Center. On pay gap, millenial women near parity—for now. December 2013. Available at: http://www.pewsocialtrends.org/files/2013/12/gender-and-work_final.pdf. Published December 11, 2013. Accessed February 5, 2015.
- The emerging role of "hospitalists" in the American health care system. N Engl J Med. 1996;335:514–517. , .
- Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27:23–27. , , , , , .
- The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014. .
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9. , , , .
- State of Hospital Medicine: 2011 Report Based on 2010 Data. Medical Group Management Association and Society of Hospital Medicine. www.mgma.com, www.hospitalmedicine.org.
- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
- American Medical Association. Graduate Medical Education Directory 2012–2013. Chicago, IL: American Medical Association; 2012:182–203.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- Association of American Medical Colleges. 2012 Physician Specialty Data Book. Center for Workforce Studies. Available at: https://www.aamc.org/download/313228/data/2012physicianspecialtydatabook.pdf. Published November 2012. Accessed September 1, 2014.
- Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635. , , , .
- Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186. , , , et al.
- Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896. , , , et al.
- Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629. , , , .
- A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018. , , , , .
- Perceived obstacles to career success for women in academic surgery. Arch Surg. 2000;135:972–977. , , .
- Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426. , , , .
- Doing the same and earning less: male and female physicians in a new medical specialty. Inquiry. 2004;41:301–315. .
- Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353. , , , , , .
- Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758. , , , , .
- The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201. , , , .
- Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207. , , , , .
- Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014. .
- Mentoring in academic medicine: a systematic review. JAMA. 2006;296:1103–1115. , , .
- Inequality quantified: mind the gender gap. Nature. 2013;495:22–24. .
- Gender differences in academic advancement: patterns, causes, and potential solutions in one US College of Medicine. Acad Med. 2003;78:500–508. , , , et al.
- Why aren't there more women leaders in academic medicine? The views of clinical department chairs. Acad Med. 2001;76:453–465. , .
- Gender factors in reviewer recommendations for manuscript publication. J Appl Behav Anal. 1990;23:539–543. .
- Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16. , , , .
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538. , , , et al.
- The “gender gap” in authorship of academic medical literature—a 35‐year perspective. N Engl J Med. 2006;355:281–287. , , , et al.
- Sex differences in academic advancement. Results of a national study of pediatricians. N Engl J Med. 1996;335:1282–1289. , , , , , .
- Women physicians in academic medicine: new insights from cohort studies. N Engl J Med. 2000;342:399–405. .
- Gender differences in academic productivity and leadership appointments of physicians throughout academic careers. Acad Med. 2011;86:43–47. , , , , .
- Promotion of women physicians in academic medicine. Glass ceiling or sticky floor? JAMA. 1995;273:1022–1025. , , , .
- Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141:205–212. , , , .
- Women physicians: choosing a career in academic medicine. Acad Med. 2012;87:105–114. , , .
- The status of women at one academic medical center. Breaking through the glass ceiling. JAMA. 1990;264:1813–1817. , , , .
- Status of women in academic anesthesiology. Anesthesiology. 1986;64:496–500. , .
- The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians. BMC Health Serv Res. 2006;6:55. , , .
- Pew Research Center. On pay gap, millenial women near parity—for now. December 2013. Available at: http://www.pewsocialtrends.org/files/2013/12/gender-and-work_final.pdf. Published December 11, 2013. Accessed February 5, 2015.
- The emerging role of "hospitalists" in the American health care system. N Engl J Med. 1996;335:514–517. , .
- Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27:23–27. , , , , , .
- The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014. .
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9. , , , .
- State of Hospital Medicine: 2011 Report Based on 2010 Data. Medical Group Management Association and Society of Hospital Medicine. www.mgma.com, www.hospitalmedicine.org.
- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
- American Medical Association. Graduate Medical Education Directory 2012–2013. Chicago, IL: American Medical Association; 2012:182–203.
- Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. , , , , , .
- Association of American Medical Colleges. 2012 Physician Specialty Data Book. Center for Workforce Studies. Available at: https://www.aamc.org/download/313228/data/2012physicianspecialtydatabook.pdf. Published November 2012. Accessed September 1, 2014.
- Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635. , , , .
- Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186. , , , et al.
- Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896. , , , et al.
- Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629. , , , .
- A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018. , , , , .
- Perceived obstacles to career success for women in academic surgery. Arch Surg. 2000;135:972–977. , , .
- Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426. , , , .
- Doing the same and earning less: male and female physicians in a new medical specialty. Inquiry. 2004;41:301–315. .
- Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353. , , , , , .
- Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758. , , , , .
- The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201. , , , .
- Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207. , , , , .
- Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014. .
- Mentoring in academic medicine: a systematic review. JAMA. 2006;296:1103–1115. , , .
- Inequality quantified: mind the gender gap. Nature. 2013;495:22–24. .
- Gender differences in academic advancement: patterns, causes, and potential solutions in one US College of Medicine. Acad Med. 2003;78:500–508. , , , et al.
- Why aren't there more women leaders in academic medicine? The views of clinical department chairs. Acad Med. 2001;76:453–465. , .
- Gender factors in reviewer recommendations for manuscript publication. J Appl Behav Anal. 1990;23:539–543. .
- Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16. , , , .
© 2015 Society of Hospital Medicine