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Training Residents in Hospital Medicine: The Hospitalist Elective National Survey
Hospital medicine has become the fastest growing medicine subspecialty, though no standardized hospitalist-focused educational program is required to become a practicing adult medicine hospitalist.1 Historically, adult hospitalists have had little additional training beyond residency, yet, as residency training adapts to duty hour restrictions, patient caps, and increasing attending oversight, it is not clear if traditional rotations and curricula provide adequate preparation for independent practice as an adult hospitalist.2-5 Several types of training and educational programs have emerged to fill this potential gap. These include hospital medicine fellowships, residency pathways, early career faculty development programs (eg, Society of Hospital Medicine/ Society of General Internal Medicine sponsored Academic Hospitalist Academy), and hospitalist-focused resident rotations.6-10 These activities are intended to ensure that residents and early career physicians gain the skills and competencies required to effectively practice hospital medicine.
Hospital medicine fellowships, residency pathways, and faculty development have been described previously.6-8 However, the prevalence and characteristics of hospital medicine-focused resident rotations are unknown, and these rotations are rarely publicized beyond local residency programs. Our study aims to determine the prevalence, purpose, and function of hospitalist-focused rotations within residency programs and explore the role these rotations have in preparing residents for a career in hospital medicine.
METHODS
Study Design, Setting, and Participants
We conducted a cross-sectional study involving the largest 100 Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency programs. We chose the largest programs as we hypothesized that these programs would be most likely to have the infrastructure to support hospital medicine focused rotations compared to smaller programs. The UCSF Committee on Human Research approved this study.
Survey Development
We developed a study-specific survey (the Hospitalist Elective National Survey [HENS]) to assess the prevalence, structure, curricular goals, and perceived benefits of distinct hospitalist rotations as defined by individual resident programs. The survey prompted respondents to consider a “hospitalist-focused” rotation as one that is different from a traditional inpatient “ward” rotation and whose emphasis is on hospitalist-specific training, clinical skills, or career development. The 18-question survey (Appendix 1) included fixed choice, multiple choice, and open-ended responses.
Data Collection
Using publicly available data from the ACGME website (www.acgme.org), we identified the largest 100 medicine programs based on the total number of residents. This included programs with 81 or more residents. An electronic survey was e-mailed to the leadership of each program. In May 2015, surveys were sent to Residency Program Directors (PD), and if they did not respond after 2 attempts, then Associate Program Directors (APD) were contacted twice. If both these leaders did not respond, then the survey was sent to residency program administrators or Hospital Medicine Division Chiefs. Only one survey was completed per site.
Data Analysis
We used descriptive statistics to summarize quantitative data. Responses to open-ended qualitative questions about the goals, strengths, and design of rotations were analyzed using thematic analysis.11 During analysis, we iteratively developed and refined codes that identified important concepts that emerged from the data. Two members of the research team trained in qualitative data analysis coded these data independently (SL & JH).
RESULTS
Eighty-two residency program leaders (53 PD, 19 APD, 10 chiefs/admin) responded to the survey (82% total response rate). Among all responders, the prevalence of hospitalist-focused rotations was 50% (41/82). Of these 41 rotations, 85% (35/41) were elective rotations and 15% (6/41) were mandatory rotations. Hospitalist rotations ranged in existence from 1 to 15 years with a mean duration of 4.78 years (S.D. 3.5).
Of the 41 programs that did not have a hospital medicine-focused rotation, the key barriers identified were a lack of a well-defined model (29%), low faculty interest (15%), low resident interest (12%), and lack of funding (5%). Despite these barriers, 9 of these 41 programs (22%) stated they planned to start a rotation in the future – of which, 3 programs (7%) planned to start a rotation within the year.
Most programs with rotations (39/41, 95%) reported that their hospitalist rotation filled at least one gap in traditional residency curriculum. The most frequently identified gaps the rotation filled included: allowing progressive clinical autonomy (59%, 24/41), learning about quality improvement and high value care (41%, 17/41), and preparing to become a practicing hospitalist (39%, 16/41). Most respondents (66%, 27/41) reported that the rotation helped to prepare trainees for their first year as an attending.
DISCUSSION
The Hospital Elective National Survey provides insight into a growing component of hospitalist-focused training and preparation. Fifty percent of ACGME residency programs surveyed in this study had a hospitalist-focused rotation. Rotation characteristics were heterogeneous, perhaps reflecting both the homegrown nature of their development and the lack of national study or data to guide what constitutes an “ideal” rotation. Common functions of rotations included providing career mentorship and allowing for trainees to get experience “being a hospitalist.” Other key elements of the rotations included providing additional clinical autonomy and teaching material outside of traditional residency curricula such as quality improvement, patient safety, billing, and healthcare finances.
Prior research has explored other training for hospitalists such as fellowships, pathways, and faculty development.6-8 A hospital medicine fellowship provides extensive training but without a practice requirement in adult medicine (as now exists in pediatric hospital medicine), the impact of fellowship training may be limited by its scale.12,13 Longitudinal hospitalist residency pathways provide comprehensive skill development and often require an early career commitment from trainees.7 Faculty development can be another tool to foster career growth, though requires local investment from hospitalist groups that may not have the resources or experience to support this.8 Our study has highlighted that hospitalist-focused rotations within residency programs can train physicians for a career in hospital medicine. Hospitalist and residency leaders should consider that these rotations might be the only hospital medicine-focused training that new hospitalists will have. Given the variable nature of these rotations nationally, developing standards around core hospitalist competencies within these rotations should be a key component to career preparation and a goal for the field at large.14,15
Our study has limitations. The survey focused only on internal medicine as it is the most common training background of hospitalists; however, the field has grown to include other specialties including pediatrics, neurology, family medicine, and surgery. In addition, the survey reviewed the largest ACGME Internal Medicine programs to best evaluate prevalence and content—it may be that some smaller programs have rotations with different characteristics that we have not captured. Lastly, the survey reviewed the rotations through the lens of residency program leadership and not trainees. A future survey of trainees or early career hospitalists who participated in these rotations could provide a better understanding of their achievements and effectiveness.
CONCLUSION
We anticipate that the demand for hospitalist-focused training will continue to grow as more residents in training seek to enter the specialty. Hospitalist and residency program leaders have an opportunity within residency training programs to build new or further develop existing hospital medicine-focused rotations. The HENS survey demonstrates that hospitalist-focused rotations are prevalent in residency education and have the potential to play an important role in hospitalist training.
Disclosure
The authors declare no conflicts of interest in relation to this manuscript.
1. Wachter RM, Goldman L. Zero to 50,000 – The 20th Anniversary of the Hospitalist. N Engl J Med. 2016;375:1009-1011. PubMed
2. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs. J Gen Intern Med. 2008;23:1110-1115. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008; 5:436-441. PubMed
4. Plauth WH 3rd, Pantilat SZ, Wachter RM, Fenton CL. Hospitalists’ perceptions of their residency training needs: results of a national survey. Am J Med. 2001; 15;111:247-254. PubMed
5. Kumar A, Smeraglio A, Witteles R, Harman S, Nallamshetty, S, Rogers A, Harrington R, Ahuja N. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11:646-649. PubMed
6. Ranji, SR, Rosenman, DJ, Amin, AN, Kripalani, S. Hospital medicine fellowships: works in progress. Am J Med. 2006;119(1):72.e1-7. PubMed
7. Sweigart JR, Tad-Y D, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12:173-176. PubMed
8. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6:161-166. PubMed
9. Academic Hospitalist Academy. Course Description, Objectives and Society Sponsorship. Available at: https://academichospitalist.org/. Accessed August 23, 2017.
10. Amin AN. A successful hospitalist rotation for senior medicine residents. Med Educ. 2003;37:1042. PubMed
11. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77-101.
12. American Board of Medical Specialties. ABMS Officially Recognizes Pediatric Hospital Medicine Subspecialty Certification Available at: http://www.abms.org/news-events/abms-officially-recognizes-pediatric-hospital-medicine-subspecialty-certification/. Accessed August 23, 2017. PubMed
13. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008; 23(7):1122-1123. PubMed
14. Dressler DD, Pistoria MJ, Budnitz TL, McKean SC, Amin AN. Core competencies in hospital medicine: development and methodology. J Hosp Med. 2006; 1 Suppl 1:48-56. PubMed
15. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine – 2017 revision: introduction and methodology. J Hosp Med. 2017;4:283-287. PubMed
Hospital medicine has become the fastest growing medicine subspecialty, though no standardized hospitalist-focused educational program is required to become a practicing adult medicine hospitalist.1 Historically, adult hospitalists have had little additional training beyond residency, yet, as residency training adapts to duty hour restrictions, patient caps, and increasing attending oversight, it is not clear if traditional rotations and curricula provide adequate preparation for independent practice as an adult hospitalist.2-5 Several types of training and educational programs have emerged to fill this potential gap. These include hospital medicine fellowships, residency pathways, early career faculty development programs (eg, Society of Hospital Medicine/ Society of General Internal Medicine sponsored Academic Hospitalist Academy), and hospitalist-focused resident rotations.6-10 These activities are intended to ensure that residents and early career physicians gain the skills and competencies required to effectively practice hospital medicine.
Hospital medicine fellowships, residency pathways, and faculty development have been described previously.6-8 However, the prevalence and characteristics of hospital medicine-focused resident rotations are unknown, and these rotations are rarely publicized beyond local residency programs. Our study aims to determine the prevalence, purpose, and function of hospitalist-focused rotations within residency programs and explore the role these rotations have in preparing residents for a career in hospital medicine.
METHODS
Study Design, Setting, and Participants
We conducted a cross-sectional study involving the largest 100 Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency programs. We chose the largest programs as we hypothesized that these programs would be most likely to have the infrastructure to support hospital medicine focused rotations compared to smaller programs. The UCSF Committee on Human Research approved this study.
Survey Development
We developed a study-specific survey (the Hospitalist Elective National Survey [HENS]) to assess the prevalence, structure, curricular goals, and perceived benefits of distinct hospitalist rotations as defined by individual resident programs. The survey prompted respondents to consider a “hospitalist-focused” rotation as one that is different from a traditional inpatient “ward” rotation and whose emphasis is on hospitalist-specific training, clinical skills, or career development. The 18-question survey (Appendix 1) included fixed choice, multiple choice, and open-ended responses.
Data Collection
Using publicly available data from the ACGME website (www.acgme.org), we identified the largest 100 medicine programs based on the total number of residents. This included programs with 81 or more residents. An electronic survey was e-mailed to the leadership of each program. In May 2015, surveys were sent to Residency Program Directors (PD), and if they did not respond after 2 attempts, then Associate Program Directors (APD) were contacted twice. If both these leaders did not respond, then the survey was sent to residency program administrators or Hospital Medicine Division Chiefs. Only one survey was completed per site.
Data Analysis
We used descriptive statistics to summarize quantitative data. Responses to open-ended qualitative questions about the goals, strengths, and design of rotations were analyzed using thematic analysis.11 During analysis, we iteratively developed and refined codes that identified important concepts that emerged from the data. Two members of the research team trained in qualitative data analysis coded these data independently (SL & JH).
RESULTS
Eighty-two residency program leaders (53 PD, 19 APD, 10 chiefs/admin) responded to the survey (82% total response rate). Among all responders, the prevalence of hospitalist-focused rotations was 50% (41/82). Of these 41 rotations, 85% (35/41) were elective rotations and 15% (6/41) were mandatory rotations. Hospitalist rotations ranged in existence from 1 to 15 years with a mean duration of 4.78 years (S.D. 3.5).
Of the 41 programs that did not have a hospital medicine-focused rotation, the key barriers identified were a lack of a well-defined model (29%), low faculty interest (15%), low resident interest (12%), and lack of funding (5%). Despite these barriers, 9 of these 41 programs (22%) stated they planned to start a rotation in the future – of which, 3 programs (7%) planned to start a rotation within the year.
Most programs with rotations (39/41, 95%) reported that their hospitalist rotation filled at least one gap in traditional residency curriculum. The most frequently identified gaps the rotation filled included: allowing progressive clinical autonomy (59%, 24/41), learning about quality improvement and high value care (41%, 17/41), and preparing to become a practicing hospitalist (39%, 16/41). Most respondents (66%, 27/41) reported that the rotation helped to prepare trainees for their first year as an attending.
DISCUSSION
The Hospital Elective National Survey provides insight into a growing component of hospitalist-focused training and preparation. Fifty percent of ACGME residency programs surveyed in this study had a hospitalist-focused rotation. Rotation characteristics were heterogeneous, perhaps reflecting both the homegrown nature of their development and the lack of national study or data to guide what constitutes an “ideal” rotation. Common functions of rotations included providing career mentorship and allowing for trainees to get experience “being a hospitalist.” Other key elements of the rotations included providing additional clinical autonomy and teaching material outside of traditional residency curricula such as quality improvement, patient safety, billing, and healthcare finances.
Prior research has explored other training for hospitalists such as fellowships, pathways, and faculty development.6-8 A hospital medicine fellowship provides extensive training but without a practice requirement in adult medicine (as now exists in pediatric hospital medicine), the impact of fellowship training may be limited by its scale.12,13 Longitudinal hospitalist residency pathways provide comprehensive skill development and often require an early career commitment from trainees.7 Faculty development can be another tool to foster career growth, though requires local investment from hospitalist groups that may not have the resources or experience to support this.8 Our study has highlighted that hospitalist-focused rotations within residency programs can train physicians for a career in hospital medicine. Hospitalist and residency leaders should consider that these rotations might be the only hospital medicine-focused training that new hospitalists will have. Given the variable nature of these rotations nationally, developing standards around core hospitalist competencies within these rotations should be a key component to career preparation and a goal for the field at large.14,15
Our study has limitations. The survey focused only on internal medicine as it is the most common training background of hospitalists; however, the field has grown to include other specialties including pediatrics, neurology, family medicine, and surgery. In addition, the survey reviewed the largest ACGME Internal Medicine programs to best evaluate prevalence and content—it may be that some smaller programs have rotations with different characteristics that we have not captured. Lastly, the survey reviewed the rotations through the lens of residency program leadership and not trainees. A future survey of trainees or early career hospitalists who participated in these rotations could provide a better understanding of their achievements and effectiveness.
CONCLUSION
We anticipate that the demand for hospitalist-focused training will continue to grow as more residents in training seek to enter the specialty. Hospitalist and residency program leaders have an opportunity within residency training programs to build new or further develop existing hospital medicine-focused rotations. The HENS survey demonstrates that hospitalist-focused rotations are prevalent in residency education and have the potential to play an important role in hospitalist training.
Disclosure
The authors declare no conflicts of interest in relation to this manuscript.
Hospital medicine has become the fastest growing medicine subspecialty, though no standardized hospitalist-focused educational program is required to become a practicing adult medicine hospitalist.1 Historically, adult hospitalists have had little additional training beyond residency, yet, as residency training adapts to duty hour restrictions, patient caps, and increasing attending oversight, it is not clear if traditional rotations and curricula provide adequate preparation for independent practice as an adult hospitalist.2-5 Several types of training and educational programs have emerged to fill this potential gap. These include hospital medicine fellowships, residency pathways, early career faculty development programs (eg, Society of Hospital Medicine/ Society of General Internal Medicine sponsored Academic Hospitalist Academy), and hospitalist-focused resident rotations.6-10 These activities are intended to ensure that residents and early career physicians gain the skills and competencies required to effectively practice hospital medicine.
Hospital medicine fellowships, residency pathways, and faculty development have been described previously.6-8 However, the prevalence and characteristics of hospital medicine-focused resident rotations are unknown, and these rotations are rarely publicized beyond local residency programs. Our study aims to determine the prevalence, purpose, and function of hospitalist-focused rotations within residency programs and explore the role these rotations have in preparing residents for a career in hospital medicine.
METHODS
Study Design, Setting, and Participants
We conducted a cross-sectional study involving the largest 100 Accreditation Council for Graduate Medical Education (ACGME) internal medicine residency programs. We chose the largest programs as we hypothesized that these programs would be most likely to have the infrastructure to support hospital medicine focused rotations compared to smaller programs. The UCSF Committee on Human Research approved this study.
Survey Development
We developed a study-specific survey (the Hospitalist Elective National Survey [HENS]) to assess the prevalence, structure, curricular goals, and perceived benefits of distinct hospitalist rotations as defined by individual resident programs. The survey prompted respondents to consider a “hospitalist-focused” rotation as one that is different from a traditional inpatient “ward” rotation and whose emphasis is on hospitalist-specific training, clinical skills, or career development. The 18-question survey (Appendix 1) included fixed choice, multiple choice, and open-ended responses.
Data Collection
Using publicly available data from the ACGME website (www.acgme.org), we identified the largest 100 medicine programs based on the total number of residents. This included programs with 81 or more residents. An electronic survey was e-mailed to the leadership of each program. In May 2015, surveys were sent to Residency Program Directors (PD), and if they did not respond after 2 attempts, then Associate Program Directors (APD) were contacted twice. If both these leaders did not respond, then the survey was sent to residency program administrators or Hospital Medicine Division Chiefs. Only one survey was completed per site.
Data Analysis
We used descriptive statistics to summarize quantitative data. Responses to open-ended qualitative questions about the goals, strengths, and design of rotations were analyzed using thematic analysis.11 During analysis, we iteratively developed and refined codes that identified important concepts that emerged from the data. Two members of the research team trained in qualitative data analysis coded these data independently (SL & JH).
RESULTS
Eighty-two residency program leaders (53 PD, 19 APD, 10 chiefs/admin) responded to the survey (82% total response rate). Among all responders, the prevalence of hospitalist-focused rotations was 50% (41/82). Of these 41 rotations, 85% (35/41) were elective rotations and 15% (6/41) were mandatory rotations. Hospitalist rotations ranged in existence from 1 to 15 years with a mean duration of 4.78 years (S.D. 3.5).
Of the 41 programs that did not have a hospital medicine-focused rotation, the key barriers identified were a lack of a well-defined model (29%), low faculty interest (15%), low resident interest (12%), and lack of funding (5%). Despite these barriers, 9 of these 41 programs (22%) stated they planned to start a rotation in the future – of which, 3 programs (7%) planned to start a rotation within the year.
Most programs with rotations (39/41, 95%) reported that their hospitalist rotation filled at least one gap in traditional residency curriculum. The most frequently identified gaps the rotation filled included: allowing progressive clinical autonomy (59%, 24/41), learning about quality improvement and high value care (41%, 17/41), and preparing to become a practicing hospitalist (39%, 16/41). Most respondents (66%, 27/41) reported that the rotation helped to prepare trainees for their first year as an attending.
DISCUSSION
The Hospital Elective National Survey provides insight into a growing component of hospitalist-focused training and preparation. Fifty percent of ACGME residency programs surveyed in this study had a hospitalist-focused rotation. Rotation characteristics were heterogeneous, perhaps reflecting both the homegrown nature of their development and the lack of national study or data to guide what constitutes an “ideal” rotation. Common functions of rotations included providing career mentorship and allowing for trainees to get experience “being a hospitalist.” Other key elements of the rotations included providing additional clinical autonomy and teaching material outside of traditional residency curricula such as quality improvement, patient safety, billing, and healthcare finances.
Prior research has explored other training for hospitalists such as fellowships, pathways, and faculty development.6-8 A hospital medicine fellowship provides extensive training but without a practice requirement in adult medicine (as now exists in pediatric hospital medicine), the impact of fellowship training may be limited by its scale.12,13 Longitudinal hospitalist residency pathways provide comprehensive skill development and often require an early career commitment from trainees.7 Faculty development can be another tool to foster career growth, though requires local investment from hospitalist groups that may not have the resources or experience to support this.8 Our study has highlighted that hospitalist-focused rotations within residency programs can train physicians for a career in hospital medicine. Hospitalist and residency leaders should consider that these rotations might be the only hospital medicine-focused training that new hospitalists will have. Given the variable nature of these rotations nationally, developing standards around core hospitalist competencies within these rotations should be a key component to career preparation and a goal for the field at large.14,15
Our study has limitations. The survey focused only on internal medicine as it is the most common training background of hospitalists; however, the field has grown to include other specialties including pediatrics, neurology, family medicine, and surgery. In addition, the survey reviewed the largest ACGME Internal Medicine programs to best evaluate prevalence and content—it may be that some smaller programs have rotations with different characteristics that we have not captured. Lastly, the survey reviewed the rotations through the lens of residency program leadership and not trainees. A future survey of trainees or early career hospitalists who participated in these rotations could provide a better understanding of their achievements and effectiveness.
CONCLUSION
We anticipate that the demand for hospitalist-focused training will continue to grow as more residents in training seek to enter the specialty. Hospitalist and residency program leaders have an opportunity within residency training programs to build new or further develop existing hospital medicine-focused rotations. The HENS survey demonstrates that hospitalist-focused rotations are prevalent in residency education and have the potential to play an important role in hospitalist training.
Disclosure
The authors declare no conflicts of interest in relation to this manuscript.
1. Wachter RM, Goldman L. Zero to 50,000 – The 20th Anniversary of the Hospitalist. N Engl J Med. 2016;375:1009-1011. PubMed
2. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs. J Gen Intern Med. 2008;23:1110-1115. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008; 5:436-441. PubMed
4. Plauth WH 3rd, Pantilat SZ, Wachter RM, Fenton CL. Hospitalists’ perceptions of their residency training needs: results of a national survey. Am J Med. 2001; 15;111:247-254. PubMed
5. Kumar A, Smeraglio A, Witteles R, Harman S, Nallamshetty, S, Rogers A, Harrington R, Ahuja N. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11:646-649. PubMed
6. Ranji, SR, Rosenman, DJ, Amin, AN, Kripalani, S. Hospital medicine fellowships: works in progress. Am J Med. 2006;119(1):72.e1-7. PubMed
7. Sweigart JR, Tad-Y D, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12:173-176. PubMed
8. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6:161-166. PubMed
9. Academic Hospitalist Academy. Course Description, Objectives and Society Sponsorship. Available at: https://academichospitalist.org/. Accessed August 23, 2017.
10. Amin AN. A successful hospitalist rotation for senior medicine residents. Med Educ. 2003;37:1042. PubMed
11. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77-101.
12. American Board of Medical Specialties. ABMS Officially Recognizes Pediatric Hospital Medicine Subspecialty Certification Available at: http://www.abms.org/news-events/abms-officially-recognizes-pediatric-hospital-medicine-subspecialty-certification/. Accessed August 23, 2017. PubMed
13. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008; 23(7):1122-1123. PubMed
14. Dressler DD, Pistoria MJ, Budnitz TL, McKean SC, Amin AN. Core competencies in hospital medicine: development and methodology. J Hosp Med. 2006; 1 Suppl 1:48-56. PubMed
15. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine – 2017 revision: introduction and methodology. J Hosp Med. 2017;4:283-287. PubMed
1. Wachter RM, Goldman L. Zero to 50,000 – The 20th Anniversary of the Hospitalist. N Engl J Med. 2016;375:1009-1011. PubMed
2. Glasheen JJ, Siegal EM, Epstein K, Kutner J, Prochazka AV. Fulfilling the promise of hospital medicine: tailoring internal medicine training to address hospitalists’ needs. J Gen Intern Med. 2008;23:1110-1115. PubMed
3. Glasheen JJ, Goldenberg J, Nelson JR. Achieving hospital medicine’s promise through internal medicine residency redesign. Mt Sinai J Med. 2008; 5:436-441. PubMed
4. Plauth WH 3rd, Pantilat SZ, Wachter RM, Fenton CL. Hospitalists’ perceptions of their residency training needs: results of a national survey. Am J Med. 2001; 15;111:247-254. PubMed
5. Kumar A, Smeraglio A, Witteles R, Harman S, Nallamshetty, S, Rogers A, Harrington R, Ahuja N. A resident-created hospitalist curriculum for internal medicine housestaff. J Hosp Med. 2016;11:646-649. PubMed
6. Ranji, SR, Rosenman, DJ, Amin, AN, Kripalani, S. Hospital medicine fellowships: works in progress. Am J Med. 2006;119(1):72.e1-7. PubMed
7. Sweigart JR, Tad-Y D, Kneeland P, Williams MV, Glasheen JJ. Hospital medicine resident training tracks: developing the hospital medicine pipeline. J Hosp Med. 2017;12:173-176. PubMed
8. Sehgal NL, Sharpe BA, Auerbach AA, Wachter RM. Investing in the future: building an academic hospitalist faculty development program. J Hosp Med. 2011;6:161-166. PubMed
9. Academic Hospitalist Academy. Course Description, Objectives and Society Sponsorship. Available at: https://academichospitalist.org/. Accessed August 23, 2017.
10. Amin AN. A successful hospitalist rotation for senior medicine residents. Med Educ. 2003;37:1042. PubMed
11. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77-101.
12. American Board of Medical Specialties. ABMS Officially Recognizes Pediatric Hospital Medicine Subspecialty Certification Available at: http://www.abms.org/news-events/abms-officially-recognizes-pediatric-hospital-medicine-subspecialty-certification/. Accessed August 23, 2017. PubMed
13. Wiese J. Residency training: beginning with the end in mind. J Gen Intern Med. 2008; 23(7):1122-1123. PubMed
14. Dressler DD, Pistoria MJ, Budnitz TL, McKean SC, Amin AN. Core competencies in hospital medicine: development and methodology. J Hosp Med. 2006; 1 Suppl 1:48-56. PubMed
15. Nichani S, Crocker J, Fitterman N, Lukela M. Updating the core competencies in hospital medicine – 2017 revision: introduction and methodology. J Hosp Med. 2017;4:283-287. PubMed
© 2018 Society of Hospital Medicine
Standardized attending rounds to improve the patient experience: A pragmatic cluster randomized controlled trial
Patient experience has recently received heightened attention given evidence supporting an association between patient experience and quality of care,1 and the coupling of patient satisfaction to reimbursement rates for Medicare patients.2 Patient experience is often assessed through surveys of patient satisfaction, which correlates with patient perceptions of nurse and physician communication.3 Teaching hospitals introduce variables that may impact communication, including the involvement of multiple levels of care providers and competing patient care vs. educational priorities. Patients admitted to teaching services express decreased satisfaction with coordination and overall care compared with patients on nonteaching services.4
Clinical supervision of trainees on teaching services is primarily achieved through attending rounds (AR), where patients’ clinical presentations and management are discussed with an attending physician. Poor communication during AR may negatively affect the patient experience through inefficient care coordination among the inter-professional care team or through implementation of interventions without patients’ knowledge or input.5-11 Although patient engagement in rounds has been associated with higher patient satisfaction with rounds,12-19 AR and case presentations often occur at a distance from the patient’s bedside.20,21 Furthermore, AR vary in the time allotted per patient and the extent of participation of nurses and other allied health professionals. Standardized bedside rounding processes have been shown to improve efficiency, decrease daily resident work hours,22 and improve nurse-physician teamwork.23
Despite these benefits, recent prospective studies of bedside AR interventions have not improved patient satisfaction with rounds. One involved the implementation of interprofessional patient-centered bedside rounds on a nonteaching service,24 while the other evaluated the impact of integrating athletic principles into multidisciplinary work rounds.25 Work at our institution had sought to develop AR practice recommendations to foster an optimal patient experience, while maintaining provider workflow efficiency, facilitating interdisciplinary communication, and advancing trainee education.26 Using these AR recommendations, we conducted a prospective randomized controlled trial to evaluate the impact of implementing a standardized bedside AR model on patient satisfaction with rounds. We also assessed attending physician and trainee satisfaction with rounds, and perceived and actual AR duration.
METHODS
Setting and Participants
This trial was conducted on the internal medicine teaching service of the University of California San Francisco Medical Center from September 3, 2013 to November 27, 2013. The service is comprised of 8 teams, with a total average daily census of 80 to 90 patients. Teams are comprised of an attending physician, a senior resident (in the second or third year of residency training), 2 interns, and a third- and/or fourth-year medical student.
This trial, which was approved by the University of California, San Francisco Committee on Human Research (UCSF CHR) and was registered with ClinicalTrials.gov (NCT01931553), was classified under Quality Improvement and did not require informed consent of patients or providers.
Intervention Description
We conducted a cluster randomized trial to evaluate the impact of a bundled set of 5 AR practice recommendations, adapted from published work,26 on patient experience, as well as on attending and trainee satisfaction: 1) huddling to establish the rounding schedule and priorities; 2) conducting bedside rounds; 3) integrating bedside nurses; 4) completing real-time order entry using bedside computers; 5) updating the whiteboard in each patient’s room with care plan information.
At the beginning of each month, study investigators (Nader Najafi and Bradley Monash) led a 1.5-hour workshop to train attending physicians and trainees allocated to the intervention arm on the recommended AR practices. Participants also received informational handouts to be referenced during AR. Attending physicians and trainees randomized to the control arm continued usual rounding practices. Control teams were notified that there would be observers on rounds but were not informed of the study aims.
Randomization and Team Assignments
The medicine service was divided into 2 arms, each comprised of 4 teams. Using a coin flip, Cluster 1 (Teams A, B, C and D) was randomized to the intervention, and Cluster 2 (Teams E, F, G and H) was randomized to the control. This design was pragmatically chosen to ensure that 1 team from each arm would admit patients daily. Allocation concealment of attending physicians and trainees was not possible given the nature of the intervention. Patients were blinded to study arm allocation.
MEASURES AND OUTCOMES
Adherence to Practice Recommendations
Thirty premedical students served as volunteer AR auditors. Each auditor received orientation and training in data collection techniques during a single 2-hour workshop. The auditors, blinded to study arm allocation, independently observed morning AR during weekdays and recorded the completion of the following elements as a dichotomous (yes/no) outcome: pre-rounds huddle, participation of nurse in AR, real-time order entry, and whiteboard use. They recorded the duration of AR per day for each team (minutes) and the rounding model for each patient rounding encounter during AR (bedside, hallway, or card flip).23 Bedside rounds were defined as presentation and discussion of the patient care plan in the presence of the patient. Hallway rounds were defined as presentation and discussion of the patient care plan partially outside the patient’s room and partially in the presence of the patient. Card-flip rounds were defined as presentation and discussion of the patient care plan entirely outside of the patient’s room without the team seeing the patient together. Two auditors simultaneously observed a random subset of patient-rounding encounters to evaluate inter-rater reliability, and the concordance between auditor observations was good (Pearson correlation = 0.66).27
Patient-Related Outcomes
The primary outcome was patient satisfaction with AR, assessed using a survey adapted from published work.12,14,28,29 Patients were approached to complete the questionnaire after they had experienced at least 1 AR. Patients were excluded if they were non-English-speaking, unavailable (eg, off the unit for testing or treatment), in isolation, or had impaired mental status. For patients admitted multiple times during the study period, only the first questionnaire was used. Survey questions included patient involvement in decision-making, quality of communication between patient and medicine team, and the perception that the medicine team cared about the patient. Patients were asked to state their level of agreement with each item on a 5-point Likert scale. We obtained data on patient demographics from administrative datasets.
Healthcare Provider Outcomes
Attending physicians and trainees on service for at least 7 consecutive days were sent an electronic survey, adapted from published work.25,30 Questions assessed satisfaction with AR, perceived value of bedside rounds, and extent of patient and nursing involvement.Level of agreement with each item was captured on a continuous scale; 0 = strongly disagree to 100 = strongly agree, or from 0 (far too little) to 100 (far too much), with 50 equating to “about right.” Attending physicians and trainees were also asked to estimate the average duration of AR (in minutes).
Statistical Analyses
Analyses were blinded to study arm allocation and followed intention-to-treat principles. One attending physician crossed over from intervention to control arm; patient surveys associated with this attending (n = 4) were excluded to avoid contamination. No trainees crossed over.
Demographic and clinical characteristics of patients who completed the survey are reported (Appendix). To compare patient satisfaction scores, we used a random-effects regression model to account for correlation among responses within teams within randomized clusters, defining teams by attending physician. As this correlation was negligible and not statistically significant, we did not adjust ordinary linear regression models for clustering. Given observed differences in patient characteristics, we adjusted for a number of covariates (eg, age, gender, insurance payer, race, marital status, trial group arm).
We conducted simple linear regression for attending and trainee satisfaction comparisons between arms, adjusting only for trainee type (eg, resident, intern, and medical student).
We compared the frequency with which intervention and control teams adhered to the 5 recommended AR practices using chi-square tests. We used independent Student’s t tests to compare total duration of AR by teams within each arm, as well as mean time spent per patient.
This trial had a fixed number of arms (n = 2), each of fixed size (n = 600), based on the average monthly inpatient census on the medicine service. This fixed sample size, with 80% power and α = 0.05, will be able to detect a 0.16 difference in patient satisfaction scores between groups.
All analyses were conducted using SAS® v 9.4 (SAS Institute, Inc., Cary, NC).
RESULTS
We observed 241 AR involving 1855 patient rounding encounters in the intervention arm and 264 AR involving 1903 patient rounding encounters in the control arm (response rates shown in Figure 1). 
Patient Satisfaction and Clinical Outcomes
Five hundred ninety-five patients were allocated to the intervention arm and 605 were allocated to the control arm (Figure 1). Mean age, gender, race, marital status, primary language, and insurance provider did not differ between intervention and control arms (Table 1). 
Patients in the intervention arm reported significantly higher satisfaction with AR and felt more cared for by their medicine team (Table 2). 
Actual and Perceived Duration of Attending Rounds
The intervention shortened the total duration of AR by 8 minutes on average (143 vs. 151 minutes, P = 0.052) and the time spent per patient by 4 minutes on average (19 vs. 23 minutes, P < 0.001). Despite this, trainees in the intervention arm perceived AR to last longer (mean estimated time: 167 min vs. 152 min, P < 0.001).
Healthcare Provider Outcomes
We observed 79 attending physicians and trainees in the intervention arm and 78 in the control arm, with survey response rates shown in Figure 1. Attending physicians in the intervention and the control arms reported high levels of satisfaction with the quality of AR (Table 2). Attending physicians in the intervention arm were more likely to report an appropriate level of patient involvement and nurse involvement.
Although trainees in the intervention and control arms reported high levels of satisfaction with the quality of AR, trainees in the intervention arm reported lower satisfaction with AR compared with control arm trainees (Table 2). Trainees in the intervention arm reported that AR involved less autonomy, efficiency, and teaching. Trainees in the intervention arm also scored patient involvement more towards the “far too much” end of the scale compared with “about right” in the control arm. However, trainees in the intervention arm perceived nurse involvement closer to “about right,” as opposed to “far too little” in the control arm.
CONCLUSION/DISCUSSION
Training internal medicine teams to adhere to 5 recommended AR practices increased patient satisfaction with AR and the perception that patients were more cared for by their medicine team. Despite the intervention potentially shortening the duration of AR, attending physicians and trainees perceived AR to last longer, and trainee satisfaction with AR decreased.
Teams in the intervention arm adhered to all recommended rounding practices at higher rates than the control teams. Although intervention teams rounded at the bedside 53% of the time, they were encouraged to bedside round only on patients who desired to participate in rounds, were not altered, and for whom the clinical discussion was not too sensitive to occur at the bedside. Of the recommended rounding behaviors, the lowest adherence was seen with whiteboard use.
A major component of the intervention was to move the clinical presentation to the patient’s bedside. Most patients prefer being included in rounds and partaking in trainee education.12-19,28,29,31-33 Patients may also perceive that more time is spent with them during bedside case presentations,14,28 and exposure to providers conferring on their care may enhance patient confidence in the care being delivered.12 Although a recent study of patient-centered bedside rounding on a nonteaching service did not result in increased patient satisfaction,24 teaching services may offer more opportunities for improvement in care coordination and communication.4
Other aspects of the intervention may have contributed to increased patient satisfaction with AR. The pre-rounds huddle may have helped teams prioritize which patients required more time or would benefit most from bedside rounds. The involvement of nurses in AR may have bolstered communication and team dynamics, enhancing the patient’s perception of interprofessional collaboration. Real-time order entry might have led to more efficient implementation of the care plan, and whiteboard use may have helped to keep patients abreast of the care plan.
Patients in the intervention arm felt more cared for by their medicine teams but did not report improvements in communication or in shared decision-making. Prior work highlights that limited patient engagement, activation, and shared decision-making may occur during AR.24,34 Patient-physician communication during AR is challenged by time pressures and competing priorities, including the “need” for trainees to demonstrate their medical knowledge and clinical skills. Efforts that encourage bedside rounding should include communication training with respect to patient engagement and shared decision-making.
Attending physicians reported positive attitudes toward bedside rounding, consistent with prior studies.13,21,31 However, trainees in the intervention arm expressed decreased satisfaction with AR, estimating that AR took longer and reporting too much patient involvement. Prior studies reflect similar bedside-rounding concerns, including perceived workflow inefficiencies, infringement on teaching opportunities, and time constraints.12,20,35 Trainees are under intense time pressures to complete their work, attend educational conferences, and leave the hospital to attend afternoon clinic or to comply with duty-hour restrictions. Trainees value succinctness,12,35,36 so the perception that intervention AR lasted longer likely contributed to trainee dissatisfaction.
Reduced trainee satisfaction with intervention AR may have also stemmed from the perception of decreased autonomy and less teaching, both valued by trainees.20,35,36 The intervention itself reduced trainee autonomy because usual practice at our hospital involves residents deciding where and how to round. Attending physician presence at the bedside during rounds may have further infringed on trainee autonomy if the patient looked to the attending for answers, or if the attending was seen as the AR leader. Attending physicians may mitigate the risk of compromising trainee autonomy by allowing the trainee to speak first, ensuring the trainee is positioned closer to, and at eye level with, the patient, and redirecting patient questions to the trainee as appropriate. Optimizing trainee experience with bedside AR requires preparation and training of attending physicians, who may feel inadequately prepared to lead bedside rounds and conduct bedside teaching.37 Faculty must learn how to preserve team efficiency, create a safe, nonpunitive bedside environment that fosters the trainee-patient relationship, and ensure rounds remain educational.36,38,39
The intervention reduced the average time spent on AR and time spent per patient. Studies examining the relationship between bedside rounding and duration of rounds have yielded mixed results: some have demonstrated no effect of bedside rounds on rounding time,28,40 while others report longer rounding times.37 The pre-rounds huddle and real-time order writing may have enhanced workflow efficiency.
Our study has several limitations. These results reflect the experience of a single large academic medical center and may not be generalizable to other settings. Although overall patient response to the survey was low and may not be representative of the entire patient population, response rates in the intervention and control arms were equivalent. Non-English speaking patients may have preferences that were not reflected in our survey results, and we did not otherwise quantify individual reasons for survey noncompletion. The presence of auditors on AR may have introduced observer bias. There may have been crossover effect; however, observed prevalence of individual practices remained low in the control arm. The 1.5-hour workshop may have inadequately equipped trainees with the complex skills required to lead and participate in bedside rounding, and more training, experience, and feedback may have yielded different results. For instance, residents with more exposure to bedside rounding express greater appreciation of its role in education and patient care.20 While adherence to some of the recommended practices remained low, we did not employ a full range of change-management techniques. Instead, we opted for a “low intensity” intervention (eg, single workshop, handouts) that relied on voluntary adoption by medicine teams and that we hoped other institutions could reproduce. Finally, we did not assess the relative impact of individual rounding behaviors on the measured outcomes.
In conclusion, training medicine teams to adhere to a standardized bedside AR model increased patient satisfaction with rounds. Concomitant trainee dissatisfaction may require further experience and training of attending physicians and trainees to ensure successful adoption.
Acknowledgements
We would like to thank all patients, providers, and trainees who participated in this study. We would also like to acknowledge the following volunteer auditors who observed teams daily: Arianna Abundo, Elahhe Afkhamnejad, Yolanda Banuelos, Laila Fozoun, Soe Yupar Khin, Tam Thien Le, Wing Sum Li, Yaqiao Li, Mengyao Liu, Tzyy-Harn Lo, Shynh-Herng Lo, David Lowe, Danoush Paborji, Sa Nan Park, Urmila Powale, Redha Fouad Qabazard, Monique Quiroz, John-Luke Marcelo Rivera, Manfred Roy Luna Salvador, Tobias Gowen Squier-Roper, Flora Yan Ting, Francesca Natasha T. Tizon, Emily Claire Trautner, Stephen Weiner, Alice Wilson, Kimberly Woo, Bingling J Wu, Johnny Wu, Brenda Yee. Statistical expertise was provided by Joan Hilton from the UCSF Clinical and Translational Science Institute (CTSI), which is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR000004. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Thanks also to Oralia Schatzman, Andrea Mazzini, and Erika Huie for their administrative support, and John Hillman for data-related support. Special thanks to Kirsten Kangelaris and Andrew Auerbach for their valuable feedback throughout, and to Maria Novelero and Robert Wachter for their divisional support of this project.
Disclosure
The authors report no financial conflicts of interest.
1. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1):1-18. PubMed
2. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Fact Sheet. August 2013. Centers for Medicare and Medicaid Services (CMS). Baltimore, MD.http://www.hcahpsonline.org/files/August_2013_HCAHPS_Fact_Sheet3.pdf. Accessed December 1, 2015.
3. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41-48. PubMed
4. Wray CM, Flores A, Padula WV, Prochaska MT, Meltzer DO, Arora VM. Measuring patient experiences on hospitalist and teaching services: Patient responses to a 30-day postdischarge questionnaire. J Hosp Med. 2016;11(2):99-104. PubMed
5. Bharwani AM, Harris GC, Southwick FS. Perspective: A business school view of medical interprofessional rounds: transforming rounding groups into rounding teams. Acad Med. 2012;87(12):1768-1771. PubMed
6. Chand DV. Observational study using the tools of lean six sigma to improve the efficiency of the resident rounding process. J Grad Med Educ. 2011;3(2):144-150. PubMed
7. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
8. Weber H, Stöckli M, Nübling M, Langewitz WA. Communication during ward rounds in internal medicine. An analysis of patient-nurse-physician interactions using RIAS. Patient Educ Couns. 2007;67(3):343-348. PubMed
9. McMahon GT, Katz JT, Thorndike ME, Levy BD, Loscalzo J. Evaluation of a redesign initiative in an internal-medicine residency. N Engl J Med. 2010;362(14):1304-1311. PubMed
10. Amoss J. Attending rounds: where do we go from here?: comment on “Attending rounds in the current era”. JAMA Intern Med. 2013;173(12):1089-1090. PubMed
11. Curley C, McEachern JE, Speroff T. A firm trial of interdisciplinary rounds on the inpatient medical wards: an intervention designed using continuous quality improvement. Med Care. 1998;36(suppl 8):AS4-A12. PubMed
12. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
13. Chauke, HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):336, 1150-1155. PubMed
15. Simons RJ, Baily RG, Zelis R, Zwillich CW. The physiologic and psychological effects of the bedside presentation. N Engl J Med. 1989;321(18):1273-1275. PubMed
16. Wise TN, Feldheim D, Mann LS, Boyle E, Rustgi VK. Patients’ reactions to house staff work rounds. Psychosomatics. 1985;26(8):669-672. PubMed
17. Linfors EW, Neelon FA. Sounding Boards. The case of bedside rounds. N Engl J Med. 1980;303(21):1230-1233. PubMed
18. Nair BR, Coughlan JL, Hensley MJ. Student and patient perspectives on bedside teaching. Med Educ. 1997;31(5):341-346. PubMed
19. Romano J. Patients’ attitudes and behavior in ward round teaching. JAMA. 1941;117(9):664-667.
20. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
21. Shoeb M, Khanna R, Fang M, et al. Internal medicine rounding practices and the Accreditation Council for Graduate Medical Education core competencies. J Hosp Med. 2014;9(4):239-243. PubMed
22. Calderon AS, Blackmore CC, Williams BL, et al. Transforming ward rounds through rounding-in-flow. J Grad Med Educ. 2014;6(4):750-755. PubMed
23. Henkin S, Chon TY, Christopherson ML, Halvorsen AJ, Worden LM, Ratelle JT. Improving nurse-physician teamwork through interprofessional bedside rounding. J Multidiscip Healthc. 2016;9:201-205. PubMed
24. O’Leary KJ, Killarney A, Hansen LO, et al. Effect of patient-centred bedside rounds on hospitalised patients’ decision control, activation and satisfaction with care. BMJ Qual Saf. 2016;25:921-928. PubMed
25. Southwick F, Lewis M, Treloar D, et al. Applying athletic principles to medical rounds to improve teaching and patient care. Acad Med. 2014;89(7):1018-1023. PubMed
26. Najafi N, Monash B, Mourad M, et al. Improving attending rounds: Qualitative reflections from multidisciplinary providers. Hosp Pract (1995). 2015;43(3):186-190. PubMed
27. Altman DG. Practical Statistics For Medical Research. Boca Raton, FL: Chapman & Hall/CRC; 2006.
28. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
29. Fletcher KE, Rankey DS, Stern DT. Bedside interactions from the other side of the bedrail. J Gen Intern Med. 2005;20(1):58-61. PubMed
30. Gatorounds: Applying Championship Athletic Principles to Healthcare. University of Florida Health. http://gatorounds.med.ufl.edu/surveys/. Accessed March 1, 2013.
31. Gonzalo JD, Heist BS, Duffy BL, et al. The value of bedside rounds: a multicenter qualitative study. Teach Learn Med. 2013;25(4):326-333. PubMed
32. Rogers HD, Carline JD, Paauw DS. Examination room presentations in general internal medicine clinic: patients’ and students’ perceptions. Acad Med. 2003;78(9):945-949. PubMed
33. Fletcher KE, Furney SL, Stern DT. Patients speak: what’s really important about bedside interactions with physician teams. Teach Learn Med. 2007;19(2):120-127. PubMed
34. Satterfield JM, Bereknyei S, Hilton JF, et al. The prevalence of social and behavioral topics and related educational opportunities during attending rounds. Acad Med. 2014; 89(11):1548-1557. PubMed
35. Kroenke K, Simmons JO, Copley JB, Smith C. Attending rounds: a survey of physician attitudes. J Gen Intern Med. 1990;5(3):229-233. PubMed
36. Castiglioni A, Shewchuk RM, Willett LL, Heudebert GR, Centor RM. A pilot study using nominal group technique to assess residents’ perceptions of successful attending rounds. J Gen Intern Med. 2008;23(7):1060-1065. PubMed
37. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
38. Gonzalo JD, Wolpaw DR, Lehman E, Chuang CH. Patient-centered interprofessional collaborative care: factors associated with bedside interprofessional rounds. J Gen Intern Med. 2014;29(7):1040-1047. PubMed
39. Roy B, Castiglioni A, Kraemer RR, et al. Using cognitive mapping to define key domains for successful attending rounds. J Gen Intern Med. 2012;27(11):1492-1498. PubMed
40. Bhansali P, Birch S, Campbell JK, et al. A time-motion study of inpatient rounds using a family-centered rounds model. Hosp Pediatr. 2013;3(1):31-38. PubMed
Patient experience has recently received heightened attention given evidence supporting an association between patient experience and quality of care,1 and the coupling of patient satisfaction to reimbursement rates for Medicare patients.2 Patient experience is often assessed through surveys of patient satisfaction, which correlates with patient perceptions of nurse and physician communication.3 Teaching hospitals introduce variables that may impact communication, including the involvement of multiple levels of care providers and competing patient care vs. educational priorities. Patients admitted to teaching services express decreased satisfaction with coordination and overall care compared with patients on nonteaching services.4
Clinical supervision of trainees on teaching services is primarily achieved through attending rounds (AR), where patients’ clinical presentations and management are discussed with an attending physician. Poor communication during AR may negatively affect the patient experience through inefficient care coordination among the inter-professional care team or through implementation of interventions without patients’ knowledge or input.5-11 Although patient engagement in rounds has been associated with higher patient satisfaction with rounds,12-19 AR and case presentations often occur at a distance from the patient’s bedside.20,21 Furthermore, AR vary in the time allotted per patient and the extent of participation of nurses and other allied health professionals. Standardized bedside rounding processes have been shown to improve efficiency, decrease daily resident work hours,22 and improve nurse-physician teamwork.23
Despite these benefits, recent prospective studies of bedside AR interventions have not improved patient satisfaction with rounds. One involved the implementation of interprofessional patient-centered bedside rounds on a nonteaching service,24 while the other evaluated the impact of integrating athletic principles into multidisciplinary work rounds.25 Work at our institution had sought to develop AR practice recommendations to foster an optimal patient experience, while maintaining provider workflow efficiency, facilitating interdisciplinary communication, and advancing trainee education.26 Using these AR recommendations, we conducted a prospective randomized controlled trial to evaluate the impact of implementing a standardized bedside AR model on patient satisfaction with rounds. We also assessed attending physician and trainee satisfaction with rounds, and perceived and actual AR duration.
METHODS
Setting and Participants
This trial was conducted on the internal medicine teaching service of the University of California San Francisco Medical Center from September 3, 2013 to November 27, 2013. The service is comprised of 8 teams, with a total average daily census of 80 to 90 patients. Teams are comprised of an attending physician, a senior resident (in the second or third year of residency training), 2 interns, and a third- and/or fourth-year medical student.
This trial, which was approved by the University of California, San Francisco Committee on Human Research (UCSF CHR) and was registered with ClinicalTrials.gov (NCT01931553), was classified under Quality Improvement and did not require informed consent of patients or providers.
Intervention Description
We conducted a cluster randomized trial to evaluate the impact of a bundled set of 5 AR practice recommendations, adapted from published work,26 on patient experience, as well as on attending and trainee satisfaction: 1) huddling to establish the rounding schedule and priorities; 2) conducting bedside rounds; 3) integrating bedside nurses; 4) completing real-time order entry using bedside computers; 5) updating the whiteboard in each patient’s room with care plan information.
At the beginning of each month, study investigators (Nader Najafi and Bradley Monash) led a 1.5-hour workshop to train attending physicians and trainees allocated to the intervention arm on the recommended AR practices. Participants also received informational handouts to be referenced during AR. Attending physicians and trainees randomized to the control arm continued usual rounding practices. Control teams were notified that there would be observers on rounds but were not informed of the study aims.
Randomization and Team Assignments
The medicine service was divided into 2 arms, each comprised of 4 teams. Using a coin flip, Cluster 1 (Teams A, B, C and D) was randomized to the intervention, and Cluster 2 (Teams E, F, G and H) was randomized to the control. This design was pragmatically chosen to ensure that 1 team from each arm would admit patients daily. Allocation concealment of attending physicians and trainees was not possible given the nature of the intervention. Patients were blinded to study arm allocation.
MEASURES AND OUTCOMES
Adherence to Practice Recommendations
Thirty premedical students served as volunteer AR auditors. Each auditor received orientation and training in data collection techniques during a single 2-hour workshop. The auditors, blinded to study arm allocation, independently observed morning AR during weekdays and recorded the completion of the following elements as a dichotomous (yes/no) outcome: pre-rounds huddle, participation of nurse in AR, real-time order entry, and whiteboard use. They recorded the duration of AR per day for each team (minutes) and the rounding model for each patient rounding encounter during AR (bedside, hallway, or card flip).23 Bedside rounds were defined as presentation and discussion of the patient care plan in the presence of the patient. Hallway rounds were defined as presentation and discussion of the patient care plan partially outside the patient’s room and partially in the presence of the patient. Card-flip rounds were defined as presentation and discussion of the patient care plan entirely outside of the patient’s room without the team seeing the patient together. Two auditors simultaneously observed a random subset of patient-rounding encounters to evaluate inter-rater reliability, and the concordance between auditor observations was good (Pearson correlation = 0.66).27
Patient-Related Outcomes
The primary outcome was patient satisfaction with AR, assessed using a survey adapted from published work.12,14,28,29 Patients were approached to complete the questionnaire after they had experienced at least 1 AR. Patients were excluded if they were non-English-speaking, unavailable (eg, off the unit for testing or treatment), in isolation, or had impaired mental status. For patients admitted multiple times during the study period, only the first questionnaire was used. Survey questions included patient involvement in decision-making, quality of communication between patient and medicine team, and the perception that the medicine team cared about the patient. Patients were asked to state their level of agreement with each item on a 5-point Likert scale. We obtained data on patient demographics from administrative datasets.
Healthcare Provider Outcomes
Attending physicians and trainees on service for at least 7 consecutive days were sent an electronic survey, adapted from published work.25,30 Questions assessed satisfaction with AR, perceived value of bedside rounds, and extent of patient and nursing involvement.Level of agreement with each item was captured on a continuous scale; 0 = strongly disagree to 100 = strongly agree, or from 0 (far too little) to 100 (far too much), with 50 equating to “about right.” Attending physicians and trainees were also asked to estimate the average duration of AR (in minutes).
Statistical Analyses
Analyses were blinded to study arm allocation and followed intention-to-treat principles. One attending physician crossed over from intervention to control arm; patient surveys associated with this attending (n = 4) were excluded to avoid contamination. No trainees crossed over.
Demographic and clinical characteristics of patients who completed the survey are reported (Appendix). To compare patient satisfaction scores, we used a random-effects regression model to account for correlation among responses within teams within randomized clusters, defining teams by attending physician. As this correlation was negligible and not statistically significant, we did not adjust ordinary linear regression models for clustering. Given observed differences in patient characteristics, we adjusted for a number of covariates (eg, age, gender, insurance payer, race, marital status, trial group arm).
We conducted simple linear regression for attending and trainee satisfaction comparisons between arms, adjusting only for trainee type (eg, resident, intern, and medical student).
We compared the frequency with which intervention and control teams adhered to the 5 recommended AR practices using chi-square tests. We used independent Student’s t tests to compare total duration of AR by teams within each arm, as well as mean time spent per patient.
This trial had a fixed number of arms (n = 2), each of fixed size (n = 600), based on the average monthly inpatient census on the medicine service. This fixed sample size, with 80% power and α = 0.05, will be able to detect a 0.16 difference in patient satisfaction scores between groups.
All analyses were conducted using SAS® v 9.4 (SAS Institute, Inc., Cary, NC).
RESULTS
We observed 241 AR involving 1855 patient rounding encounters in the intervention arm and 264 AR involving 1903 patient rounding encounters in the control arm (response rates shown in Figure 1).
Patient Satisfaction and Clinical Outcomes
Five hundred ninety-five patients were allocated to the intervention arm and 605 were allocated to the control arm (Figure 1). Mean age, gender, race, marital status, primary language, and insurance provider did not differ between intervention and control arms (Table 1).
Patients in the intervention arm reported significantly higher satisfaction with AR and felt more cared for by their medicine team (Table 2).
Actual and Perceived Duration of Attending Rounds
The intervention shortened the total duration of AR by 8 minutes on average (143 vs. 151 minutes, P = 0.052) and the time spent per patient by 4 minutes on average (19 vs. 23 minutes, P < 0.001). Despite this, trainees in the intervention arm perceived AR to last longer (mean estimated time: 167 min vs. 152 min, P < 0.001).
Healthcare Provider Outcomes
We observed 79 attending physicians and trainees in the intervention arm and 78 in the control arm, with survey response rates shown in Figure 1. Attending physicians in the intervention and the control arms reported high levels of satisfaction with the quality of AR (Table 2). Attending physicians in the intervention arm were more likely to report an appropriate level of patient involvement and nurse involvement.
Although trainees in the intervention and control arms reported high levels of satisfaction with the quality of AR, trainees in the intervention arm reported lower satisfaction with AR compared with control arm trainees (Table 2). Trainees in the intervention arm reported that AR involved less autonomy, efficiency, and teaching. Trainees in the intervention arm also scored patient involvement more towards the “far too much” end of the scale compared with “about right” in the control arm. However, trainees in the intervention arm perceived nurse involvement closer to “about right,” as opposed to “far too little” in the control arm.
CONCLUSION/DISCUSSION
Training internal medicine teams to adhere to 5 recommended AR practices increased patient satisfaction with AR and the perception that patients were more cared for by their medicine team. Despite the intervention potentially shortening the duration of AR, attending physicians and trainees perceived AR to last longer, and trainee satisfaction with AR decreased.
Teams in the intervention arm adhered to all recommended rounding practices at higher rates than the control teams. Although intervention teams rounded at the bedside 53% of the time, they were encouraged to bedside round only on patients who desired to participate in rounds, were not altered, and for whom the clinical discussion was not too sensitive to occur at the bedside. Of the recommended rounding behaviors, the lowest adherence was seen with whiteboard use.
A major component of the intervention was to move the clinical presentation to the patient’s bedside. Most patients prefer being included in rounds and partaking in trainee education.12-19,28,29,31-33 Patients may also perceive that more time is spent with them during bedside case presentations,14,28 and exposure to providers conferring on their care may enhance patient confidence in the care being delivered.12 Although a recent study of patient-centered bedside rounding on a nonteaching service did not result in increased patient satisfaction,24 teaching services may offer more opportunities for improvement in care coordination and communication.4
Other aspects of the intervention may have contributed to increased patient satisfaction with AR. The pre-rounds huddle may have helped teams prioritize which patients required more time or would benefit most from bedside rounds. The involvement of nurses in AR may have bolstered communication and team dynamics, enhancing the patient’s perception of interprofessional collaboration. Real-time order entry might have led to more efficient implementation of the care plan, and whiteboard use may have helped to keep patients abreast of the care plan.
Patients in the intervention arm felt more cared for by their medicine teams but did not report improvements in communication or in shared decision-making. Prior work highlights that limited patient engagement, activation, and shared decision-making may occur during AR.24,34 Patient-physician communication during AR is challenged by time pressures and competing priorities, including the “need” for trainees to demonstrate their medical knowledge and clinical skills. Efforts that encourage bedside rounding should include communication training with respect to patient engagement and shared decision-making.
Attending physicians reported positive attitudes toward bedside rounding, consistent with prior studies.13,21,31 However, trainees in the intervention arm expressed decreased satisfaction with AR, estimating that AR took longer and reporting too much patient involvement. Prior studies reflect similar bedside-rounding concerns, including perceived workflow inefficiencies, infringement on teaching opportunities, and time constraints.12,20,35 Trainees are under intense time pressures to complete their work, attend educational conferences, and leave the hospital to attend afternoon clinic or to comply with duty-hour restrictions. Trainees value succinctness,12,35,36 so the perception that intervention AR lasted longer likely contributed to trainee dissatisfaction.
Reduced trainee satisfaction with intervention AR may have also stemmed from the perception of decreased autonomy and less teaching, both valued by trainees.20,35,36 The intervention itself reduced trainee autonomy because usual practice at our hospital involves residents deciding where and how to round. Attending physician presence at the bedside during rounds may have further infringed on trainee autonomy if the patient looked to the attending for answers, or if the attending was seen as the AR leader. Attending physicians may mitigate the risk of compromising trainee autonomy by allowing the trainee to speak first, ensuring the trainee is positioned closer to, and at eye level with, the patient, and redirecting patient questions to the trainee as appropriate. Optimizing trainee experience with bedside AR requires preparation and training of attending physicians, who may feel inadequately prepared to lead bedside rounds and conduct bedside teaching.37 Faculty must learn how to preserve team efficiency, create a safe, nonpunitive bedside environment that fosters the trainee-patient relationship, and ensure rounds remain educational.36,38,39
The intervention reduced the average time spent on AR and time spent per patient. Studies examining the relationship between bedside rounding and duration of rounds have yielded mixed results: some have demonstrated no effect of bedside rounds on rounding time,28,40 while others report longer rounding times.37 The pre-rounds huddle and real-time order writing may have enhanced workflow efficiency.
Our study has several limitations. These results reflect the experience of a single large academic medical center and may not be generalizable to other settings. Although overall patient response to the survey was low and may not be representative of the entire patient population, response rates in the intervention and control arms were equivalent. Non-English speaking patients may have preferences that were not reflected in our survey results, and we did not otherwise quantify individual reasons for survey noncompletion. The presence of auditors on AR may have introduced observer bias. There may have been crossover effect; however, observed prevalence of individual practices remained low in the control arm. The 1.5-hour workshop may have inadequately equipped trainees with the complex skills required to lead and participate in bedside rounding, and more training, experience, and feedback may have yielded different results. For instance, residents with more exposure to bedside rounding express greater appreciation of its role in education and patient care.20 While adherence to some of the recommended practices remained low, we did not employ a full range of change-management techniques. Instead, we opted for a “low intensity” intervention (eg, single workshop, handouts) that relied on voluntary adoption by medicine teams and that we hoped other institutions could reproduce. Finally, we did not assess the relative impact of individual rounding behaviors on the measured outcomes.
In conclusion, training medicine teams to adhere to a standardized bedside AR model increased patient satisfaction with rounds. Concomitant trainee dissatisfaction may require further experience and training of attending physicians and trainees to ensure successful adoption.
Acknowledgements
We would like to thank all patients, providers, and trainees who participated in this study. We would also like to acknowledge the following volunteer auditors who observed teams daily: Arianna Abundo, Elahhe Afkhamnejad, Yolanda Banuelos, Laila Fozoun, Soe Yupar Khin, Tam Thien Le, Wing Sum Li, Yaqiao Li, Mengyao Liu, Tzyy-Harn Lo, Shynh-Herng Lo, David Lowe, Danoush Paborji, Sa Nan Park, Urmila Powale, Redha Fouad Qabazard, Monique Quiroz, John-Luke Marcelo Rivera, Manfred Roy Luna Salvador, Tobias Gowen Squier-Roper, Flora Yan Ting, Francesca Natasha T. Tizon, Emily Claire Trautner, Stephen Weiner, Alice Wilson, Kimberly Woo, Bingling J Wu, Johnny Wu, Brenda Yee. Statistical expertise was provided by Joan Hilton from the UCSF Clinical and Translational Science Institute (CTSI), which is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR000004. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Thanks also to Oralia Schatzman, Andrea Mazzini, and Erika Huie for their administrative support, and John Hillman for data-related support. Special thanks to Kirsten Kangelaris and Andrew Auerbach for their valuable feedback throughout, and to Maria Novelero and Robert Wachter for their divisional support of this project.
Disclosure
The authors report no financial conflicts of interest.
Patient experience has recently received heightened attention given evidence supporting an association between patient experience and quality of care,1 and the coupling of patient satisfaction to reimbursement rates for Medicare patients.2 Patient experience is often assessed through surveys of patient satisfaction, which correlates with patient perceptions of nurse and physician communication.3 Teaching hospitals introduce variables that may impact communication, including the involvement of multiple levels of care providers and competing patient care vs. educational priorities. Patients admitted to teaching services express decreased satisfaction with coordination and overall care compared with patients on nonteaching services.4
Clinical supervision of trainees on teaching services is primarily achieved through attending rounds (AR), where patients’ clinical presentations and management are discussed with an attending physician. Poor communication during AR may negatively affect the patient experience through inefficient care coordination among the inter-professional care team or through implementation of interventions without patients’ knowledge or input.5-11 Although patient engagement in rounds has been associated with higher patient satisfaction with rounds,12-19 AR and case presentations often occur at a distance from the patient’s bedside.20,21 Furthermore, AR vary in the time allotted per patient and the extent of participation of nurses and other allied health professionals. Standardized bedside rounding processes have been shown to improve efficiency, decrease daily resident work hours,22 and improve nurse-physician teamwork.23
Despite these benefits, recent prospective studies of bedside AR interventions have not improved patient satisfaction with rounds. One involved the implementation of interprofessional patient-centered bedside rounds on a nonteaching service,24 while the other evaluated the impact of integrating athletic principles into multidisciplinary work rounds.25 Work at our institution had sought to develop AR practice recommendations to foster an optimal patient experience, while maintaining provider workflow efficiency, facilitating interdisciplinary communication, and advancing trainee education.26 Using these AR recommendations, we conducted a prospective randomized controlled trial to evaluate the impact of implementing a standardized bedside AR model on patient satisfaction with rounds. We also assessed attending physician and trainee satisfaction with rounds, and perceived and actual AR duration.
METHODS
Setting and Participants
This trial was conducted on the internal medicine teaching service of the University of California San Francisco Medical Center from September 3, 2013 to November 27, 2013. The service is comprised of 8 teams, with a total average daily census of 80 to 90 patients. Teams are comprised of an attending physician, a senior resident (in the second or third year of residency training), 2 interns, and a third- and/or fourth-year medical student.
This trial, which was approved by the University of California, San Francisco Committee on Human Research (UCSF CHR) and was registered with ClinicalTrials.gov (NCT01931553), was classified under Quality Improvement and did not require informed consent of patients or providers.
Intervention Description
We conducted a cluster randomized trial to evaluate the impact of a bundled set of 5 AR practice recommendations, adapted from published work,26 on patient experience, as well as on attending and trainee satisfaction: 1) huddling to establish the rounding schedule and priorities; 2) conducting bedside rounds; 3) integrating bedside nurses; 4) completing real-time order entry using bedside computers; 5) updating the whiteboard in each patient’s room with care plan information.
At the beginning of each month, study investigators (Nader Najafi and Bradley Monash) led a 1.5-hour workshop to train attending physicians and trainees allocated to the intervention arm on the recommended AR practices. Participants also received informational handouts to be referenced during AR. Attending physicians and trainees randomized to the control arm continued usual rounding practices. Control teams were notified that there would be observers on rounds but were not informed of the study aims.
Randomization and Team Assignments
The medicine service was divided into 2 arms, each comprised of 4 teams. Using a coin flip, Cluster 1 (Teams A, B, C and D) was randomized to the intervention, and Cluster 2 (Teams E, F, G and H) was randomized to the control. This design was pragmatically chosen to ensure that 1 team from each arm would admit patients daily. Allocation concealment of attending physicians and trainees was not possible given the nature of the intervention. Patients were blinded to study arm allocation.
MEASURES AND OUTCOMES
Adherence to Practice Recommendations
Thirty premedical students served as volunteer AR auditors. Each auditor received orientation and training in data collection techniques during a single 2-hour workshop. The auditors, blinded to study arm allocation, independently observed morning AR during weekdays and recorded the completion of the following elements as a dichotomous (yes/no) outcome: pre-rounds huddle, participation of nurse in AR, real-time order entry, and whiteboard use. They recorded the duration of AR per day for each team (minutes) and the rounding model for each patient rounding encounter during AR (bedside, hallway, or card flip).23 Bedside rounds were defined as presentation and discussion of the patient care plan in the presence of the patient. Hallway rounds were defined as presentation and discussion of the patient care plan partially outside the patient’s room and partially in the presence of the patient. Card-flip rounds were defined as presentation and discussion of the patient care plan entirely outside of the patient’s room without the team seeing the patient together. Two auditors simultaneously observed a random subset of patient-rounding encounters to evaluate inter-rater reliability, and the concordance between auditor observations was good (Pearson correlation = 0.66).27
Patient-Related Outcomes
The primary outcome was patient satisfaction with AR, assessed using a survey adapted from published work.12,14,28,29 Patients were approached to complete the questionnaire after they had experienced at least 1 AR. Patients were excluded if they were non-English-speaking, unavailable (eg, off the unit for testing or treatment), in isolation, or had impaired mental status. For patients admitted multiple times during the study period, only the first questionnaire was used. Survey questions included patient involvement in decision-making, quality of communication between patient and medicine team, and the perception that the medicine team cared about the patient. Patients were asked to state their level of agreement with each item on a 5-point Likert scale. We obtained data on patient demographics from administrative datasets.
Healthcare Provider Outcomes
Attending physicians and trainees on service for at least 7 consecutive days were sent an electronic survey, adapted from published work.25,30 Questions assessed satisfaction with AR, perceived value of bedside rounds, and extent of patient and nursing involvement.Level of agreement with each item was captured on a continuous scale; 0 = strongly disagree to 100 = strongly agree, or from 0 (far too little) to 100 (far too much), with 50 equating to “about right.” Attending physicians and trainees were also asked to estimate the average duration of AR (in minutes).
Statistical Analyses
Analyses were blinded to study arm allocation and followed intention-to-treat principles. One attending physician crossed over from intervention to control arm; patient surveys associated with this attending (n = 4) were excluded to avoid contamination. No trainees crossed over.
Demographic and clinical characteristics of patients who completed the survey are reported (Appendix). To compare patient satisfaction scores, we used a random-effects regression model to account for correlation among responses within teams within randomized clusters, defining teams by attending physician. As this correlation was negligible and not statistically significant, we did not adjust ordinary linear regression models for clustering. Given observed differences in patient characteristics, we adjusted for a number of covariates (eg, age, gender, insurance payer, race, marital status, trial group arm).
We conducted simple linear regression for attending and trainee satisfaction comparisons between arms, adjusting only for trainee type (eg, resident, intern, and medical student).
We compared the frequency with which intervention and control teams adhered to the 5 recommended AR practices using chi-square tests. We used independent Student’s t tests to compare total duration of AR by teams within each arm, as well as mean time spent per patient.
This trial had a fixed number of arms (n = 2), each of fixed size (n = 600), based on the average monthly inpatient census on the medicine service. This fixed sample size, with 80% power and α = 0.05, will be able to detect a 0.16 difference in patient satisfaction scores between groups.
All analyses were conducted using SAS® v 9.4 (SAS Institute, Inc., Cary, NC).
RESULTS
We observed 241 AR involving 1855 patient rounding encounters in the intervention arm and 264 AR involving 1903 patient rounding encounters in the control arm (response rates shown in Figure 1).
Patient Satisfaction and Clinical Outcomes
Five hundred ninety-five patients were allocated to the intervention arm and 605 were allocated to the control arm (Figure 1). Mean age, gender, race, marital status, primary language, and insurance provider did not differ between intervention and control arms (Table 1).
Patients in the intervention arm reported significantly higher satisfaction with AR and felt more cared for by their medicine team (Table 2).
Actual and Perceived Duration of Attending Rounds
The intervention shortened the total duration of AR by 8 minutes on average (143 vs. 151 minutes, P = 0.052) and the time spent per patient by 4 minutes on average (19 vs. 23 minutes, P < 0.001). Despite this, trainees in the intervention arm perceived AR to last longer (mean estimated time: 167 min vs. 152 min, P < 0.001).
Healthcare Provider Outcomes
We observed 79 attending physicians and trainees in the intervention arm and 78 in the control arm, with survey response rates shown in Figure 1. Attending physicians in the intervention and the control arms reported high levels of satisfaction with the quality of AR (Table 2). Attending physicians in the intervention arm were more likely to report an appropriate level of patient involvement and nurse involvement.
Although trainees in the intervention and control arms reported high levels of satisfaction with the quality of AR, trainees in the intervention arm reported lower satisfaction with AR compared with control arm trainees (Table 2). Trainees in the intervention arm reported that AR involved less autonomy, efficiency, and teaching. Trainees in the intervention arm also scored patient involvement more towards the “far too much” end of the scale compared with “about right” in the control arm. However, trainees in the intervention arm perceived nurse involvement closer to “about right,” as opposed to “far too little” in the control arm.
CONCLUSION/DISCUSSION
Training internal medicine teams to adhere to 5 recommended AR practices increased patient satisfaction with AR and the perception that patients were more cared for by their medicine team. Despite the intervention potentially shortening the duration of AR, attending physicians and trainees perceived AR to last longer, and trainee satisfaction with AR decreased.
Teams in the intervention arm adhered to all recommended rounding practices at higher rates than the control teams. Although intervention teams rounded at the bedside 53% of the time, they were encouraged to bedside round only on patients who desired to participate in rounds, were not altered, and for whom the clinical discussion was not too sensitive to occur at the bedside. Of the recommended rounding behaviors, the lowest adherence was seen with whiteboard use.
A major component of the intervention was to move the clinical presentation to the patient’s bedside. Most patients prefer being included in rounds and partaking in trainee education.12-19,28,29,31-33 Patients may also perceive that more time is spent with them during bedside case presentations,14,28 and exposure to providers conferring on their care may enhance patient confidence in the care being delivered.12 Although a recent study of patient-centered bedside rounding on a nonteaching service did not result in increased patient satisfaction,24 teaching services may offer more opportunities for improvement in care coordination and communication.4
Other aspects of the intervention may have contributed to increased patient satisfaction with AR. The pre-rounds huddle may have helped teams prioritize which patients required more time or would benefit most from bedside rounds. The involvement of nurses in AR may have bolstered communication and team dynamics, enhancing the patient’s perception of interprofessional collaboration. Real-time order entry might have led to more efficient implementation of the care plan, and whiteboard use may have helped to keep patients abreast of the care plan.
Patients in the intervention arm felt more cared for by their medicine teams but did not report improvements in communication or in shared decision-making. Prior work highlights that limited patient engagement, activation, and shared decision-making may occur during AR.24,34 Patient-physician communication during AR is challenged by time pressures and competing priorities, including the “need” for trainees to demonstrate their medical knowledge and clinical skills. Efforts that encourage bedside rounding should include communication training with respect to patient engagement and shared decision-making.
Attending physicians reported positive attitudes toward bedside rounding, consistent with prior studies.13,21,31 However, trainees in the intervention arm expressed decreased satisfaction with AR, estimating that AR took longer and reporting too much patient involvement. Prior studies reflect similar bedside-rounding concerns, including perceived workflow inefficiencies, infringement on teaching opportunities, and time constraints.12,20,35 Trainees are under intense time pressures to complete their work, attend educational conferences, and leave the hospital to attend afternoon clinic or to comply with duty-hour restrictions. Trainees value succinctness,12,35,36 so the perception that intervention AR lasted longer likely contributed to trainee dissatisfaction.
Reduced trainee satisfaction with intervention AR may have also stemmed from the perception of decreased autonomy and less teaching, both valued by trainees.20,35,36 The intervention itself reduced trainee autonomy because usual practice at our hospital involves residents deciding where and how to round. Attending physician presence at the bedside during rounds may have further infringed on trainee autonomy if the patient looked to the attending for answers, or if the attending was seen as the AR leader. Attending physicians may mitigate the risk of compromising trainee autonomy by allowing the trainee to speak first, ensuring the trainee is positioned closer to, and at eye level with, the patient, and redirecting patient questions to the trainee as appropriate. Optimizing trainee experience with bedside AR requires preparation and training of attending physicians, who may feel inadequately prepared to lead bedside rounds and conduct bedside teaching.37 Faculty must learn how to preserve team efficiency, create a safe, nonpunitive bedside environment that fosters the trainee-patient relationship, and ensure rounds remain educational.36,38,39
The intervention reduced the average time spent on AR and time spent per patient. Studies examining the relationship between bedside rounding and duration of rounds have yielded mixed results: some have demonstrated no effect of bedside rounds on rounding time,28,40 while others report longer rounding times.37 The pre-rounds huddle and real-time order writing may have enhanced workflow efficiency.
Our study has several limitations. These results reflect the experience of a single large academic medical center and may not be generalizable to other settings. Although overall patient response to the survey was low and may not be representative of the entire patient population, response rates in the intervention and control arms were equivalent. Non-English speaking patients may have preferences that were not reflected in our survey results, and we did not otherwise quantify individual reasons for survey noncompletion. The presence of auditors on AR may have introduced observer bias. There may have been crossover effect; however, observed prevalence of individual practices remained low in the control arm. The 1.5-hour workshop may have inadequately equipped trainees with the complex skills required to lead and participate in bedside rounding, and more training, experience, and feedback may have yielded different results. For instance, residents with more exposure to bedside rounding express greater appreciation of its role in education and patient care.20 While adherence to some of the recommended practices remained low, we did not employ a full range of change-management techniques. Instead, we opted for a “low intensity” intervention (eg, single workshop, handouts) that relied on voluntary adoption by medicine teams and that we hoped other institutions could reproduce. Finally, we did not assess the relative impact of individual rounding behaviors on the measured outcomes.
In conclusion, training medicine teams to adhere to a standardized bedside AR model increased patient satisfaction with rounds. Concomitant trainee dissatisfaction may require further experience and training of attending physicians and trainees to ensure successful adoption.
Acknowledgements
We would like to thank all patients, providers, and trainees who participated in this study. We would also like to acknowledge the following volunteer auditors who observed teams daily: Arianna Abundo, Elahhe Afkhamnejad, Yolanda Banuelos, Laila Fozoun, Soe Yupar Khin, Tam Thien Le, Wing Sum Li, Yaqiao Li, Mengyao Liu, Tzyy-Harn Lo, Shynh-Herng Lo, David Lowe, Danoush Paborji, Sa Nan Park, Urmila Powale, Redha Fouad Qabazard, Monique Quiroz, John-Luke Marcelo Rivera, Manfred Roy Luna Salvador, Tobias Gowen Squier-Roper, Flora Yan Ting, Francesca Natasha T. Tizon, Emily Claire Trautner, Stephen Weiner, Alice Wilson, Kimberly Woo, Bingling J Wu, Johnny Wu, Brenda Yee. Statistical expertise was provided by Joan Hilton from the UCSF Clinical and Translational Science Institute (CTSI), which is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR000004. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Thanks also to Oralia Schatzman, Andrea Mazzini, and Erika Huie for their administrative support, and John Hillman for data-related support. Special thanks to Kirsten Kangelaris and Andrew Auerbach for their valuable feedback throughout, and to Maria Novelero and Robert Wachter for their divisional support of this project.
Disclosure
The authors report no financial conflicts of interest.
1. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1):1-18. PubMed
2. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Fact Sheet. August 2013. Centers for Medicare and Medicaid Services (CMS). Baltimore, MD.http://www.hcahpsonline.org/files/August_2013_HCAHPS_Fact_Sheet3.pdf. Accessed December 1, 2015.
3. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41-48. PubMed
4. Wray CM, Flores A, Padula WV, Prochaska MT, Meltzer DO, Arora VM. Measuring patient experiences on hospitalist and teaching services: Patient responses to a 30-day postdischarge questionnaire. J Hosp Med. 2016;11(2):99-104. PubMed
5. Bharwani AM, Harris GC, Southwick FS. Perspective: A business school view of medical interprofessional rounds: transforming rounding groups into rounding teams. Acad Med. 2012;87(12):1768-1771. PubMed
6. Chand DV. Observational study using the tools of lean six sigma to improve the efficiency of the resident rounding process. J Grad Med Educ. 2011;3(2):144-150. PubMed
7. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
8. Weber H, Stöckli M, Nübling M, Langewitz WA. Communication during ward rounds in internal medicine. An analysis of patient-nurse-physician interactions using RIAS. Patient Educ Couns. 2007;67(3):343-348. PubMed
9. McMahon GT, Katz JT, Thorndike ME, Levy BD, Loscalzo J. Evaluation of a redesign initiative in an internal-medicine residency. N Engl J Med. 2010;362(14):1304-1311. PubMed
10. Amoss J. Attending rounds: where do we go from here?: comment on “Attending rounds in the current era”. JAMA Intern Med. 2013;173(12):1089-1090. PubMed
11. Curley C, McEachern JE, Speroff T. A firm trial of interdisciplinary rounds on the inpatient medical wards: an intervention designed using continuous quality improvement. Med Care. 1998;36(suppl 8):AS4-A12. PubMed
12. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
13. Chauke, HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):336, 1150-1155. PubMed
15. Simons RJ, Baily RG, Zelis R, Zwillich CW. The physiologic and psychological effects of the bedside presentation. N Engl J Med. 1989;321(18):1273-1275. PubMed
16. Wise TN, Feldheim D, Mann LS, Boyle E, Rustgi VK. Patients’ reactions to house staff work rounds. Psychosomatics. 1985;26(8):669-672. PubMed
17. Linfors EW, Neelon FA. Sounding Boards. The case of bedside rounds. N Engl J Med. 1980;303(21):1230-1233. PubMed
18. Nair BR, Coughlan JL, Hensley MJ. Student and patient perspectives on bedside teaching. Med Educ. 1997;31(5):341-346. PubMed
19. Romano J. Patients’ attitudes and behavior in ward round teaching. JAMA. 1941;117(9):664-667.
20. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
21. Shoeb M, Khanna R, Fang M, et al. Internal medicine rounding practices and the Accreditation Council for Graduate Medical Education core competencies. J Hosp Med. 2014;9(4):239-243. PubMed
22. Calderon AS, Blackmore CC, Williams BL, et al. Transforming ward rounds through rounding-in-flow. J Grad Med Educ. 2014;6(4):750-755. PubMed
23. Henkin S, Chon TY, Christopherson ML, Halvorsen AJ, Worden LM, Ratelle JT. Improving nurse-physician teamwork through interprofessional bedside rounding. J Multidiscip Healthc. 2016;9:201-205. PubMed
24. O’Leary KJ, Killarney A, Hansen LO, et al. Effect of patient-centred bedside rounds on hospitalised patients’ decision control, activation and satisfaction with care. BMJ Qual Saf. 2016;25:921-928. PubMed
25. Southwick F, Lewis M, Treloar D, et al. Applying athletic principles to medical rounds to improve teaching and patient care. Acad Med. 2014;89(7):1018-1023. PubMed
26. Najafi N, Monash B, Mourad M, et al. Improving attending rounds: Qualitative reflections from multidisciplinary providers. Hosp Pract (1995). 2015;43(3):186-190. PubMed
27. Altman DG. Practical Statistics For Medical Research. Boca Raton, FL: Chapman & Hall/CRC; 2006.
28. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
29. Fletcher KE, Rankey DS, Stern DT. Bedside interactions from the other side of the bedrail. J Gen Intern Med. 2005;20(1):58-61. PubMed
30. Gatorounds: Applying Championship Athletic Principles to Healthcare. University of Florida Health. http://gatorounds.med.ufl.edu/surveys/. Accessed March 1, 2013.
31. Gonzalo JD, Heist BS, Duffy BL, et al. The value of bedside rounds: a multicenter qualitative study. Teach Learn Med. 2013;25(4):326-333. PubMed
32. Rogers HD, Carline JD, Paauw DS. Examination room presentations in general internal medicine clinic: patients’ and students’ perceptions. Acad Med. 2003;78(9):945-949. PubMed
33. Fletcher KE, Furney SL, Stern DT. Patients speak: what’s really important about bedside interactions with physician teams. Teach Learn Med. 2007;19(2):120-127. PubMed
34. Satterfield JM, Bereknyei S, Hilton JF, et al. The prevalence of social and behavioral topics and related educational opportunities during attending rounds. Acad Med. 2014; 89(11):1548-1557. PubMed
35. Kroenke K, Simmons JO, Copley JB, Smith C. Attending rounds: a survey of physician attitudes. J Gen Intern Med. 1990;5(3):229-233. PubMed
36. Castiglioni A, Shewchuk RM, Willett LL, Heudebert GR, Centor RM. A pilot study using nominal group technique to assess residents’ perceptions of successful attending rounds. J Gen Intern Med. 2008;23(7):1060-1065. PubMed
37. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
38. Gonzalo JD, Wolpaw DR, Lehman E, Chuang CH. Patient-centered interprofessional collaborative care: factors associated with bedside interprofessional rounds. J Gen Intern Med. 2014;29(7):1040-1047. PubMed
39. Roy B, Castiglioni A, Kraemer RR, et al. Using cognitive mapping to define key domains for successful attending rounds. J Gen Intern Med. 2012;27(11):1492-1498. PubMed
40. Bhansali P, Birch S, Campbell JK, et al. A time-motion study of inpatient rounds using a family-centered rounds model. Hosp Pediatr. 2013;3(1):31-38. PubMed
1. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3(1):1-18. PubMed
2. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Fact Sheet. August 2013. Centers for Medicare and Medicaid Services (CMS). Baltimore, MD.http://www.hcahpsonline.org/files/August_2013_HCAHPS_Fact_Sheet3.pdf. Accessed December 1, 2015.
3. Boulding W, Glickman SW, Manary MP, Schulman KA, Staelin R. Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care. 2011;17:41-48. PubMed
4. Wray CM, Flores A, Padula WV, Prochaska MT, Meltzer DO, Arora VM. Measuring patient experiences on hospitalist and teaching services: Patient responses to a 30-day postdischarge questionnaire. J Hosp Med. 2016;11(2):99-104. PubMed
5. Bharwani AM, Harris GC, Southwick FS. Perspective: A business school view of medical interprofessional rounds: transforming rounding groups into rounding teams. Acad Med. 2012;87(12):1768-1771. PubMed
6. Chand DV. Observational study using the tools of lean six sigma to improve the efficiency of the resident rounding process. J Grad Med Educ. 2011;3(2):144-150. PubMed
7. Stickrath C, Noble M, Prochazka A, et al. Attending rounds in the current era: what is and is not happening. JAMA Intern Med. 2013;173(12):1084-1089. PubMed
8. Weber H, Stöckli M, Nübling M, Langewitz WA. Communication during ward rounds in internal medicine. An analysis of patient-nurse-physician interactions using RIAS. Patient Educ Couns. 2007;67(3):343-348. PubMed
9. McMahon GT, Katz JT, Thorndike ME, Levy BD, Loscalzo J. Evaluation of a redesign initiative in an internal-medicine residency. N Engl J Med. 2010;362(14):1304-1311. PubMed
10. Amoss J. Attending rounds: where do we go from here?: comment on “Attending rounds in the current era”. JAMA Intern Med. 2013;173(12):1089-1090. PubMed
11. Curley C, McEachern JE, Speroff T. A firm trial of interdisciplinary rounds on the inpatient medical wards: an intervention designed using continuous quality improvement. Med Care. 1998;36(suppl 8):AS4-A12. PubMed
12. Wang-Cheng RM, Barnas GP, Sigmann P, Riendl PA, Young MJ. Bedside case presentations: why patients like them but learners don’t. J Gen Intern Med. 1989;4(4):284-287. PubMed
13. Chauke, HL, Pattinson RC. Ward rounds—bedside or conference room? S Afr Med J. 2006;96(5):398-400. PubMed
14. Lehmann LS, Brancati FL, Chen MC, Roter D, Dobs AS. The effect of bedside case presentations on patients’ perceptions of their medical care. N Engl J Med. 1997;336(16):336, 1150-1155. PubMed
15. Simons RJ, Baily RG, Zelis R, Zwillich CW. The physiologic and psychological effects of the bedside presentation. N Engl J Med. 1989;321(18):1273-1275. PubMed
16. Wise TN, Feldheim D, Mann LS, Boyle E, Rustgi VK. Patients’ reactions to house staff work rounds. Psychosomatics. 1985;26(8):669-672. PubMed
17. Linfors EW, Neelon FA. Sounding Boards. The case of bedside rounds. N Engl J Med. 1980;303(21):1230-1233. PubMed
18. Nair BR, Coughlan JL, Hensley MJ. Student and patient perspectives on bedside teaching. Med Educ. 1997;31(5):341-346. PubMed
19. Romano J. Patients’ attitudes and behavior in ward round teaching. JAMA. 1941;117(9):664-667.
20. Gonzalo JD, Masters PA, Simons RJ, Chuang CH. Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes. Teach Learn Med. 2009;21(2):105-110. PubMed
21. Shoeb M, Khanna R, Fang M, et al. Internal medicine rounding practices and the Accreditation Council for Graduate Medical Education core competencies. J Hosp Med. 2014;9(4):239-243. PubMed
22. Calderon AS, Blackmore CC, Williams BL, et al. Transforming ward rounds through rounding-in-flow. J Grad Med Educ. 2014;6(4):750-755. PubMed
23. Henkin S, Chon TY, Christopherson ML, Halvorsen AJ, Worden LM, Ratelle JT. Improving nurse-physician teamwork through interprofessional bedside rounding. J Multidiscip Healthc. 2016;9:201-205. PubMed
24. O’Leary KJ, Killarney A, Hansen LO, et al. Effect of patient-centred bedside rounds on hospitalised patients’ decision control, activation and satisfaction with care. BMJ Qual Saf. 2016;25:921-928. PubMed
25. Southwick F, Lewis M, Treloar D, et al. Applying athletic principles to medical rounds to improve teaching and patient care. Acad Med. 2014;89(7):1018-1023. PubMed
26. Najafi N, Monash B, Mourad M, et al. Improving attending rounds: Qualitative reflections from multidisciplinary providers. Hosp Pract (1995). 2015;43(3):186-190. PubMed
27. Altman DG. Practical Statistics For Medical Research. Boca Raton, FL: Chapman & Hall/CRC; 2006.
28. Gonzalo JD, Chuang CH, Huang G, Smith C. The return of bedside rounds: an educational intervention. J Gen Intern Med. 2010;25(8):792-798. PubMed
29. Fletcher KE, Rankey DS, Stern DT. Bedside interactions from the other side of the bedrail. J Gen Intern Med. 2005;20(1):58-61. PubMed
30. Gatorounds: Applying Championship Athletic Principles to Healthcare. University of Florida Health. http://gatorounds.med.ufl.edu/surveys/. Accessed March 1, 2013.
31. Gonzalo JD, Heist BS, Duffy BL, et al. The value of bedside rounds: a multicenter qualitative study. Teach Learn Med. 2013;25(4):326-333. PubMed
32. Rogers HD, Carline JD, Paauw DS. Examination room presentations in general internal medicine clinic: patients’ and students’ perceptions. Acad Med. 2003;78(9):945-949. PubMed
33. Fletcher KE, Furney SL, Stern DT. Patients speak: what’s really important about bedside interactions with physician teams. Teach Learn Med. 2007;19(2):120-127. PubMed
34. Satterfield JM, Bereknyei S, Hilton JF, et al. The prevalence of social and behavioral topics and related educational opportunities during attending rounds. Acad Med. 2014; 89(11):1548-1557. PubMed
35. Kroenke K, Simmons JO, Copley JB, Smith C. Attending rounds: a survey of physician attitudes. J Gen Intern Med. 1990;5(3):229-233. PubMed
36. Castiglioni A, Shewchuk RM, Willett LL, Heudebert GR, Centor RM. A pilot study using nominal group technique to assess residents’ perceptions of successful attending rounds. J Gen Intern Med. 2008;23(7):1060-1065. PubMed
37. Crumlish CM, Yialamas MA, McMahon GT. Quantification of bedside teaching by an academic hospitalist group. J Hosp Med. 2009;4(5):304-307. PubMed
38. Gonzalo JD, Wolpaw DR, Lehman E, Chuang CH. Patient-centered interprofessional collaborative care: factors associated with bedside interprofessional rounds. J Gen Intern Med. 2014;29(7):1040-1047. PubMed
39. Roy B, Castiglioni A, Kraemer RR, et al. Using cognitive mapping to define key domains for successful attending rounds. J Gen Intern Med. 2012;27(11):1492-1498. PubMed
40. Bhansali P, Birch S, Campbell JK, et al. A time-motion study of inpatient rounds using a family-centered rounds model. Hosp Pediatr. 2013;3(1):31-38. PubMed
© 2017 Society of Hospital Medicine
Patient‐Reported Barriers to Discharge
Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]
Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]
Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]
To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.
METHODS
Study Design, Setting, and Participants
We carried out a prospective observational study at the University of California San Francisco (UCSF) Medical Center, a 600‐bed tertiary care academic hospital in San Francisco, California. The UCSF Committee on Human Research approved this study. We recruited patients between November 2013 and April 2014. Patients were eligible to participate if they were admitted to the General Medicine Service; over 18 years old; English speaking; cognitively able to provide informed consent; and not under contact, droplet, airborne, or radiation isolation. Patients were eligible to participate regardless of where they were admitted from or expected to be discharged (eg, home, skilled nursing facility). Patients were excluded if they were acutely unwell or symptomatic resulting in them being unable to complete the surveys. Caregivers were not able to participate in the study on behalf of patients. We screened daily admission charts for eligibility and approached consecutive patients to consent them into the study on their first or second day of hospitalization. An enrollment tracker was used to documented reasons for patients' exclusion or refusal.
Survey Development
We adapted an existing and validated Readiness for Hospital Discharge Survey (RHDS) previously used in obstetric, surgical, and medicine patients for our study.[10, 11, 12] This initial list was culled from 23 to 12 items, based on input from patients and physicians. This feedback step also prompted a change in the response scale from a 0 to 10 scale to a simpler yes, no, or I would like to talk with someone about this scale intended to encourage discussion between patients and providers. After this revision step, we further pretested the survey among physicians and a small set of general medical patients to assess comprehension. Thus, our final question set included 12 items in 4 domains; personal status (ie, pain, mobility), knowledge (ie, medications, problems to watch for, recovery plan), coping ability (ie, emotional support, who to call with problems), and expected support (ie, related to activities and instrumental activities of daily living).
Data Collection
We collected data from interviews of patients as well as chart abstraction. Trained research assistants approached patients to complete our revised RHDS at admission, which was either on their first or second day of hospitalization. We collected data via an intake admission survey, which asked patients about their readiness for discharge, followed by a daily readiness for discharge survey until the day of discharge. A research assistant read the survey items to patients and recorded responses on a paper version of the survey. We abstracted demographic, clinical, and 30‐day readmission information from each participant's electronic medical record.
Analytic Approach
A barrier to discharge readiness was confirmed when a patient responded no' to an item (except for presence of catheter and pain or discomfort where yes was used) and/or they stated they wanted to talk to someone about the issue. We then used descriptive statistics to summarize patients' responses by survey administration number. Multilevel mixed effect regression was used to investigate any patterns in barriers to discharge over the course of hospitalization. We described the frequency of identified barriers to discharge on the intake admission and final (48 hours of discharge) surveys. McNemar's tests compared the proportion of patients reporting each barrier, and paired t tests the mean number of barriers at these 2 survey time points. We also assessed whether persistent barriers to discharge readiness on the final survey were associated with readmission to our hospital within 30‐days using t tests, 2, or Fisher exact test. Analysis was conducted in SPSS 22.0 (IBM Corp., Armonk, NY) and Stata (StataCorp, College Station, TX).
RESULTS
Patients
There were 2045 patients admitted to the general medicine service during the study period. Medical record screening resulted in 1350 exclusions. Of the remaining 695 patients, 113 refused and 419 were further found to be unable to participate. After all exclusions were applied and following direct screening, 163 patients agreed to participate in our study (Table 1). Mean length of stay among our cohort was 5.42 days (standard deviation [SD], 11.49) and the majority of patients were admitted from and discharged to home (Table 1).
| |
| Mean age, y (SD) | 56.4 (17) |
| Female gender, no. (%) | 86 (53) |
| Race, no. (%) | |
| Asian | 13 (8) |
| African American | 27 (16) |
| White | 96 (59) |
| Other | 24 (25) |
| Declined to say | 3 (1) |
| Married, no. (%) | 78 (48) |
| Insurance, no. (%) | |
| Medicare | 59 (36) |
| Medicaid | 22 (14) |
| Private | 73 (45) |
| Self‐pay | 2 (1) |
| Other | 7 (4) |
| Patient admitted from, no. (%) | |
| Home | 118 (72) |
| Outpatient clinic | 17 (10) |
| Procedural area | 6 (4) |
| Another facility | 12 (7) |
| Other | 9 (6) |
| Patient discharged to, no. (%) | |
| Home without services | 107 (66) |
| Home with services | 40 (25) |
| Home hospice | 2 (1) |
| Skilled nursing facility | 8 (5) |
| Patient deceased | 3 (2) |
| Other | 3 (2) |
Barriers to Discharge Readiness
Patients completed on average 1.82 surveys (SD 1.10; range, 18), and in total 296 surveys were administered. Only 5% of patients were captured on their admission day, whereas 77% of patients were surveyed on their second hospital day (Table 2). Between the first and second survey administration, 51% of patients were lost to follow‐up, and then by the third survey administration a further 37% were lost to follow‐up (Table 3). Patients were unable to be reinterviewed most often because they had been (1) discharged, (2) were unavailable or having a procedure at time of recruitment, or (3) became too sick and symptomatic.
| Hospital Day | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| No. of eligible patients hospitalized | 163 | 161 | 138 | 102 | 70 | 50 | 35 | 24 | 19 | 17 |
| No. of patients surveyed | 8 | 124 | 70 | 30 | 22 | 13 | 7 | 6 | 2 | 0 |
| % of eligible patients surveyed | 4.9 | 77.0 | 50.7 | 29.4 | 31.4 | 26.0 | 20.0 | 25.0 | 10.5 | 0 |
| Survey No. | ||||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6+ | |
| ||||||
| No. of patients surveyed | 163 | 83 | 31 | 11 | 3 | 5 |
| Total barriers (all patients) | 533 | 235 | 84 | 22 | 7 | 8 |
| No. of barriers per patient, mean (SD) | 3.27(2.35) | 2.83 (2.11) | 2.71 (2.49) | 2.00 (1.73) | 2.33 (2.51) | 1.60 (2.30) |
| Median no. of barriers per patient | 3.0 | 3.0 | 2.0 | 1.0 | 2.0 | 0 |
| Median hospital day of survey administration | 2.0 | 3.0 | 5.0 | 6.0 | 8.0 | 13.0 |
| Initial admission survey, no. (%) | 163 (100.0) | 0 | 0 | 0 | 0 | 0 |
| Follow‐up survey, no. (%) | 0 | 38 (45.8) | 16 (51.6) | 4 (36.4) | 0 | 1 (20.0) |
| Survey 48 hours before discharge, no. (%) | 59 (36.2) | 45 (54.2) | 15 (48.4) | 7 (63.6) | 3 (100.0) | 4 (80.0) |
In total, over 889 individual barriers to discharge readiness were reported across all surveys. The total and mean numbers of barriers were highest on the admission intake survey, and numbers continued to decrease until the fourth survey. On average, the total number of barriers to discharge patients reported decreased by 0.15 (95% confidence interval: 0.01‐0.30) per day (P = 0.047).
Change in Barriers to Discharge
Sixty‐eight patients (42%) completed an admission intake survey as well as final survey 48 hours before discharge (Table 4). We observed a significant reduction in mean number of barriers reported between admission and discharge surveys (3.19 vs 2.53, P = 0.01). Sixty‐one patients (90%) left the hospital with 1 or more persistent barrier to a safe discharge. However, the 3 most common barriers to discharge readiness on the admission and final survey remained the same: unresolved pain, lack of understanding of plan for recovery, and daily living activities (eg, cooking, cleaning, and shopping). The number of patients with unresolved pain appeared to increase slightly, though this rise was not statistically significant. In contrast, there were significant reductions in patients reporting they were unaware of problems to watch out for postdischarge (28% vs 16%; P = 0.04) or did not understand their recovery plan (52% vs 40%; P = 0.03).
| Barrier to Discharge | Survey | |
|---|---|---|
| Admission, No. (%) | Final Survey, No. (%) | |
| ||
| Catheter is present? | 6 (7.2) | 6 (7.2) |
| Not out of bed, sitting in a chair, or walking? | 17 (20.5) | 13 (15.7) |
| Pain or discomfort? | 50 (60.2) | 52 (62.7) |
| Unable to get to the bathroom for toilet or to shower? | 15 (18.1) | 12 (14.5) |
| Unable to self‐care without help from others? | 27 (32.5) | 23 (27.7) |
| Unable to get your own medications? | 11 (13.3) | 14 (16.9) |
| Know what problems to watch for?* | 23 (27.7) | 13 (15.7) |
| Know where to call if you had problems? | 10 (12.0) | 8 (9.6) |
| Inability for personal care such as bathing, toileting, and eating? | 8 (9.6) | 11 (13.3) |
| Lack of support for emotional needs? | 16 (19.3) | 9 (10.8) |
| Unable to cook, clean, or do shopping? | 33 (39.8) | 25 (30.1) |
| Do not understand the overall plan for your recovery?* | 43 (51.8) | 33 (39.8) |
DISCUSSION
Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.
Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.
Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]
Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.
Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.
Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.
However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.
Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.
Acknowledgements
The authors thank all of the patients who participated in this project, and Yimdriuska Magan Gigi for her assistance with chart abstractions. The authors also acknowledge and thank John Boscardin for his statistical and analytic support.
Disclosures: James D. Harrison, and Drs. Ryan S. Greysen and Andrew D. Auerbach contributed to the concept, design, analysis, interpretation of data, drafting of the manuscript, critical revisions to the manuscript, and final approval of manuscript. Ronald Jacolbia and Alice Nguyen contributed to the acquisition of data, drafting and final approval of manuscript and project, and administrative and technical support. Dr. Auerbach was supported by National Heart, Lung, and Blood Institute grant K24 K24HL098372. Dr. Greysen is supported by the National Institutes of Health (NIH), National Institute of Aging (NIA) through the Claude D. Pepper Older Americans Independence Center (P30AG021342 NIH/NIA and K23AG045338‐01). The authors have no financial or other conflicts of interest to declare.
- , , . Trends and projections in inpatient hospital costs and utilization 2003–2013. HCUP statistical brief #175. July 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- , . Overview of hospital stays in the United States 2012. HCUP statistical brief #180. October 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- Joint Commision. The Joint Commission Comprehensive Accreditation Manual for Hospitals. Oak Brook, IL: The Joint Commission; 2015.
- , , . Hospital discharge and readmission. In: Post TW, ed. UpToDate website: Available at: http://www.uptodate.com/contents/hospital‐discharge‐and‐readmission. Accessed August 14, 2015.
- , . A patient centered model of care for hospital discharge. Clin Nurse Res. 2004;13:117–136.
- , , , , , . Which reasons do doctors, nurses and patients have for hospital discharge? A mixed methods study. PLoS One. 2014;9:e91333.
- , , . Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Med Care. 2010;48:482–486.
- , . Older people's perception of their readiness for discharge and postdischarge use of community support and services. Int J Older People Nurs. 2013;8:104–115.
- , , , . The care transitions intervention: Results of a randomized controlled trial. Arch Intern Med. 2006;166:1822–1828.
- , . Psychometric properties of the Readiness for Hospital Discharge Scale. J Nurs Meas. 2006;14:163–180.
- , , , et al. Perceived readiness for hospital discharge in adult medical‐surgical patients. Clin Nurse Spec. 2007;21:31–42.
- , , , . Validation of patient and nurse short forms of the Readiness for Hospital Discharge Scale and their relationship to return to the hospital. Health Serv Res. 2014;49:304–317.
- , , , et al. “Missing Pieces”—functional, social and environmental barriers to recovery for vulnerable older adults transitioning from hospital to home. J Am Geriatr Soc. 2014;62:1556–1561.
- , , , et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med. 2013;173:1715–1722.
- , , . Brief scale measuring patient prepardeness for hospital discharge to home: Psychometric properties. J Hosp Med. 2008;3:446–454.
- , , , et al. Improving patient discharge and reducing hospital readmission by using intervention mapping. BMC Health Serv Res. 2014;14:389.
- , , , , , . Development of a discharge readiness report within the electronic health record: a discharge planning tool. J Hosp Med. 2014;9:533–539.
- , , , . Incorporating Patient and Carer Concerns in Discharge Plans: The Development of a Practical Patient‐Centred Checklist. The Internet Journal of Allied Health Sciences and Practice. 2006;4: Article 5.
- , , , . Identifying keys to success in reducing readmissions using the ideal transitions in care framework. BMC Health Serv Res. 2014;14:423.
Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]
Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]
Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]
To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.
METHODS
Study Design, Setting, and Participants
We carried out a prospective observational study at the University of California San Francisco (UCSF) Medical Center, a 600‐bed tertiary care academic hospital in San Francisco, California. The UCSF Committee on Human Research approved this study. We recruited patients between November 2013 and April 2014. Patients were eligible to participate if they were admitted to the General Medicine Service; over 18 years old; English speaking; cognitively able to provide informed consent; and not under contact, droplet, airborne, or radiation isolation. Patients were eligible to participate regardless of where they were admitted from or expected to be discharged (eg, home, skilled nursing facility). Patients were excluded if they were acutely unwell or symptomatic resulting in them being unable to complete the surveys. Caregivers were not able to participate in the study on behalf of patients. We screened daily admission charts for eligibility and approached consecutive patients to consent them into the study on their first or second day of hospitalization. An enrollment tracker was used to documented reasons for patients' exclusion or refusal.
Survey Development
We adapted an existing and validated Readiness for Hospital Discharge Survey (RHDS) previously used in obstetric, surgical, and medicine patients for our study.[10, 11, 12] This initial list was culled from 23 to 12 items, based on input from patients and physicians. This feedback step also prompted a change in the response scale from a 0 to 10 scale to a simpler yes, no, or I would like to talk with someone about this scale intended to encourage discussion between patients and providers. After this revision step, we further pretested the survey among physicians and a small set of general medical patients to assess comprehension. Thus, our final question set included 12 items in 4 domains; personal status (ie, pain, mobility), knowledge (ie, medications, problems to watch for, recovery plan), coping ability (ie, emotional support, who to call with problems), and expected support (ie, related to activities and instrumental activities of daily living).
Data Collection
We collected data from interviews of patients as well as chart abstraction. Trained research assistants approached patients to complete our revised RHDS at admission, which was either on their first or second day of hospitalization. We collected data via an intake admission survey, which asked patients about their readiness for discharge, followed by a daily readiness for discharge survey until the day of discharge. A research assistant read the survey items to patients and recorded responses on a paper version of the survey. We abstracted demographic, clinical, and 30‐day readmission information from each participant's electronic medical record.
Analytic Approach
A barrier to discharge readiness was confirmed when a patient responded no' to an item (except for presence of catheter and pain or discomfort where yes was used) and/or they stated they wanted to talk to someone about the issue. We then used descriptive statistics to summarize patients' responses by survey administration number. Multilevel mixed effect regression was used to investigate any patterns in barriers to discharge over the course of hospitalization. We described the frequency of identified barriers to discharge on the intake admission and final (48 hours of discharge) surveys. McNemar's tests compared the proportion of patients reporting each barrier, and paired t tests the mean number of barriers at these 2 survey time points. We also assessed whether persistent barriers to discharge readiness on the final survey were associated with readmission to our hospital within 30‐days using t tests, 2, or Fisher exact test. Analysis was conducted in SPSS 22.0 (IBM Corp., Armonk, NY) and Stata (StataCorp, College Station, TX).
RESULTS
Patients
There were 2045 patients admitted to the general medicine service during the study period. Medical record screening resulted in 1350 exclusions. Of the remaining 695 patients, 113 refused and 419 were further found to be unable to participate. After all exclusions were applied and following direct screening, 163 patients agreed to participate in our study (Table 1). Mean length of stay among our cohort was 5.42 days (standard deviation [SD], 11.49) and the majority of patients were admitted from and discharged to home (Table 1).
| |
| Mean age, y (SD) | 56.4 (17) |
| Female gender, no. (%) | 86 (53) |
| Race, no. (%) | |
| Asian | 13 (8) |
| African American | 27 (16) |
| White | 96 (59) |
| Other | 24 (25) |
| Declined to say | 3 (1) |
| Married, no. (%) | 78 (48) |
| Insurance, no. (%) | |
| Medicare | 59 (36) |
| Medicaid | 22 (14) |
| Private | 73 (45) |
| Self‐pay | 2 (1) |
| Other | 7 (4) |
| Patient admitted from, no. (%) | |
| Home | 118 (72) |
| Outpatient clinic | 17 (10) |
| Procedural area | 6 (4) |
| Another facility | 12 (7) |
| Other | 9 (6) |
| Patient discharged to, no. (%) | |
| Home without services | 107 (66) |
| Home with services | 40 (25) |
| Home hospice | 2 (1) |
| Skilled nursing facility | 8 (5) |
| Patient deceased | 3 (2) |
| Other | 3 (2) |
Barriers to Discharge Readiness
Patients completed on average 1.82 surveys (SD 1.10; range, 18), and in total 296 surveys were administered. Only 5% of patients were captured on their admission day, whereas 77% of patients were surveyed on their second hospital day (Table 2). Between the first and second survey administration, 51% of patients were lost to follow‐up, and then by the third survey administration a further 37% were lost to follow‐up (Table 3). Patients were unable to be reinterviewed most often because they had been (1) discharged, (2) were unavailable or having a procedure at time of recruitment, or (3) became too sick and symptomatic.
| Hospital Day | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| No. of eligible patients hospitalized | 163 | 161 | 138 | 102 | 70 | 50 | 35 | 24 | 19 | 17 |
| No. of patients surveyed | 8 | 124 | 70 | 30 | 22 | 13 | 7 | 6 | 2 | 0 |
| % of eligible patients surveyed | 4.9 | 77.0 | 50.7 | 29.4 | 31.4 | 26.0 | 20.0 | 25.0 | 10.5 | 0 |
| Survey No. | ||||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6+ | |
| ||||||
| No. of patients surveyed | 163 | 83 | 31 | 11 | 3 | 5 |
| Total barriers (all patients) | 533 | 235 | 84 | 22 | 7 | 8 |
| No. of barriers per patient, mean (SD) | 3.27(2.35) | 2.83 (2.11) | 2.71 (2.49) | 2.00 (1.73) | 2.33 (2.51) | 1.60 (2.30) |
| Median no. of barriers per patient | 3.0 | 3.0 | 2.0 | 1.0 | 2.0 | 0 |
| Median hospital day of survey administration | 2.0 | 3.0 | 5.0 | 6.0 | 8.0 | 13.0 |
| Initial admission survey, no. (%) | 163 (100.0) | 0 | 0 | 0 | 0 | 0 |
| Follow‐up survey, no. (%) | 0 | 38 (45.8) | 16 (51.6) | 4 (36.4) | 0 | 1 (20.0) |
| Survey 48 hours before discharge, no. (%) | 59 (36.2) | 45 (54.2) | 15 (48.4) | 7 (63.6) | 3 (100.0) | 4 (80.0) |
In total, over 889 individual barriers to discharge readiness were reported across all surveys. The total and mean numbers of barriers were highest on the admission intake survey, and numbers continued to decrease until the fourth survey. On average, the total number of barriers to discharge patients reported decreased by 0.15 (95% confidence interval: 0.01‐0.30) per day (P = 0.047).
Change in Barriers to Discharge
Sixty‐eight patients (42%) completed an admission intake survey as well as final survey 48 hours before discharge (Table 4). We observed a significant reduction in mean number of barriers reported between admission and discharge surveys (3.19 vs 2.53, P = 0.01). Sixty‐one patients (90%) left the hospital with 1 or more persistent barrier to a safe discharge. However, the 3 most common barriers to discharge readiness on the admission and final survey remained the same: unresolved pain, lack of understanding of plan for recovery, and daily living activities (eg, cooking, cleaning, and shopping). The number of patients with unresolved pain appeared to increase slightly, though this rise was not statistically significant. In contrast, there were significant reductions in patients reporting they were unaware of problems to watch out for postdischarge (28% vs 16%; P = 0.04) or did not understand their recovery plan (52% vs 40%; P = 0.03).
| Barrier to Discharge | Survey | |
|---|---|---|
| Admission, No. (%) | Final Survey, No. (%) | |
| ||
| Catheter is present? | 6 (7.2) | 6 (7.2) |
| Not out of bed, sitting in a chair, or walking? | 17 (20.5) | 13 (15.7) |
| Pain or discomfort? | 50 (60.2) | 52 (62.7) |
| Unable to get to the bathroom for toilet or to shower? | 15 (18.1) | 12 (14.5) |
| Unable to self‐care without help from others? | 27 (32.5) | 23 (27.7) |
| Unable to get your own medications? | 11 (13.3) | 14 (16.9) |
| Know what problems to watch for?* | 23 (27.7) | 13 (15.7) |
| Know where to call if you had problems? | 10 (12.0) | 8 (9.6) |
| Inability for personal care such as bathing, toileting, and eating? | 8 (9.6) | 11 (13.3) |
| Lack of support for emotional needs? | 16 (19.3) | 9 (10.8) |
| Unable to cook, clean, or do shopping? | 33 (39.8) | 25 (30.1) |
| Do not understand the overall plan for your recovery?* | 43 (51.8) | 33 (39.8) |
DISCUSSION
Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.
Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.
Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]
Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.
Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.
Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.
However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.
Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.
Acknowledgements
The authors thank all of the patients who participated in this project, and Yimdriuska Magan Gigi for her assistance with chart abstractions. The authors also acknowledge and thank John Boscardin for his statistical and analytic support.
Disclosures: James D. Harrison, and Drs. Ryan S. Greysen and Andrew D. Auerbach contributed to the concept, design, analysis, interpretation of data, drafting of the manuscript, critical revisions to the manuscript, and final approval of manuscript. Ronald Jacolbia and Alice Nguyen contributed to the acquisition of data, drafting and final approval of manuscript and project, and administrative and technical support. Dr. Auerbach was supported by National Heart, Lung, and Blood Institute grant K24 K24HL098372. Dr. Greysen is supported by the National Institutes of Health (NIH), National Institute of Aging (NIA) through the Claude D. Pepper Older Americans Independence Center (P30AG021342 NIH/NIA and K23AG045338‐01). The authors have no financial or other conflicts of interest to declare.
Thirty‐six million adults were discharged from US hospitals in 2012, with approximately 45% from medicine service lines.[1, 2] Discharge planning, a key aspect of care for hospitalized patients,[3] should involve the development of a plan to enable the patient to be discharged at the appropriate time and with provision of sufficient postdischarge support and services.[4]
Central to the discharge planning process is an assessment of a patient's readiness for discharge. Readiness is often a provider‐driven process, based on specific clinical and health system benchmarks.[5] However, providers' perception of readiness for discharge does not always correlate with patients' self‐assessments or objective measures of understanding.[6] For example, nurses overestimate patients' readiness for discharge compared to patients' own self‐report.[7] As a result, the need to include the patient perspective is increasingly recognized as an important contributing factor in the discharge planning process.[8, 9]
Current approaches to assessing discharge readiness are typically single assessments. However, these assessments do not take into account the complexity of discharge planning or patients' understanding, or their ability to carry out postacute care tasks.[8] In addition, few models have included assessments of physical stability and functional ability along with measures such as ability to manage self‐care activities at home, coping and social support, or access to health system and community resources.[10, 11]
To address these gaps in the existing literature, we carried out a prospective observational study of daily, patient‐reported, assessments of discharge readiness to better understand patients' perspectives on issues that could impede the transition to home. Using these data, we then sought to determine the prevalence of patient‐reported discharge barriers and the frequency with which they were resolved prior to the day of discharge. We also explored whether problems identified at discharge were associated with 30‐day readmission.
METHODS
Study Design, Setting, and Participants
We carried out a prospective observational study at the University of California San Francisco (UCSF) Medical Center, a 600‐bed tertiary care academic hospital in San Francisco, California. The UCSF Committee on Human Research approved this study. We recruited patients between November 2013 and April 2014. Patients were eligible to participate if they were admitted to the General Medicine Service; over 18 years old; English speaking; cognitively able to provide informed consent; and not under contact, droplet, airborne, or radiation isolation. Patients were eligible to participate regardless of where they were admitted from or expected to be discharged (eg, home, skilled nursing facility). Patients were excluded if they were acutely unwell or symptomatic resulting in them being unable to complete the surveys. Caregivers were not able to participate in the study on behalf of patients. We screened daily admission charts for eligibility and approached consecutive patients to consent them into the study on their first or second day of hospitalization. An enrollment tracker was used to documented reasons for patients' exclusion or refusal.
Survey Development
We adapted an existing and validated Readiness for Hospital Discharge Survey (RHDS) previously used in obstetric, surgical, and medicine patients for our study.[10, 11, 12] This initial list was culled from 23 to 12 items, based on input from patients and physicians. This feedback step also prompted a change in the response scale from a 0 to 10 scale to a simpler yes, no, or I would like to talk with someone about this scale intended to encourage discussion between patients and providers. After this revision step, we further pretested the survey among physicians and a small set of general medical patients to assess comprehension. Thus, our final question set included 12 items in 4 domains; personal status (ie, pain, mobility), knowledge (ie, medications, problems to watch for, recovery plan), coping ability (ie, emotional support, who to call with problems), and expected support (ie, related to activities and instrumental activities of daily living).
Data Collection
We collected data from interviews of patients as well as chart abstraction. Trained research assistants approached patients to complete our revised RHDS at admission, which was either on their first or second day of hospitalization. We collected data via an intake admission survey, which asked patients about their readiness for discharge, followed by a daily readiness for discharge survey until the day of discharge. A research assistant read the survey items to patients and recorded responses on a paper version of the survey. We abstracted demographic, clinical, and 30‐day readmission information from each participant's electronic medical record.
Analytic Approach
A barrier to discharge readiness was confirmed when a patient responded no' to an item (except for presence of catheter and pain or discomfort where yes was used) and/or they stated they wanted to talk to someone about the issue. We then used descriptive statistics to summarize patients' responses by survey administration number. Multilevel mixed effect regression was used to investigate any patterns in barriers to discharge over the course of hospitalization. We described the frequency of identified barriers to discharge on the intake admission and final (48 hours of discharge) surveys. McNemar's tests compared the proportion of patients reporting each barrier, and paired t tests the mean number of barriers at these 2 survey time points. We also assessed whether persistent barriers to discharge readiness on the final survey were associated with readmission to our hospital within 30‐days using t tests, 2, or Fisher exact test. Analysis was conducted in SPSS 22.0 (IBM Corp., Armonk, NY) and Stata (StataCorp, College Station, TX).
RESULTS
Patients
There were 2045 patients admitted to the general medicine service during the study period. Medical record screening resulted in 1350 exclusions. Of the remaining 695 patients, 113 refused and 419 were further found to be unable to participate. After all exclusions were applied and following direct screening, 163 patients agreed to participate in our study (Table 1). Mean length of stay among our cohort was 5.42 days (standard deviation [SD], 11.49) and the majority of patients were admitted from and discharged to home (Table 1).
| |
| Mean age, y (SD) | 56.4 (17) |
| Female gender, no. (%) | 86 (53) |
| Race, no. (%) | |
| Asian | 13 (8) |
| African American | 27 (16) |
| White | 96 (59) |
| Other | 24 (25) |
| Declined to say | 3 (1) |
| Married, no. (%) | 78 (48) |
| Insurance, no. (%) | |
| Medicare | 59 (36) |
| Medicaid | 22 (14) |
| Private | 73 (45) |
| Self‐pay | 2 (1) |
| Other | 7 (4) |
| Patient admitted from, no. (%) | |
| Home | 118 (72) |
| Outpatient clinic | 17 (10) |
| Procedural area | 6 (4) |
| Another facility | 12 (7) |
| Other | 9 (6) |
| Patient discharged to, no. (%) | |
| Home without services | 107 (66) |
| Home with services | 40 (25) |
| Home hospice | 2 (1) |
| Skilled nursing facility | 8 (5) |
| Patient deceased | 3 (2) |
| Other | 3 (2) |
Barriers to Discharge Readiness
Patients completed on average 1.82 surveys (SD 1.10; range, 18), and in total 296 surveys were administered. Only 5% of patients were captured on their admission day, whereas 77% of patients were surveyed on their second hospital day (Table 2). Between the first and second survey administration, 51% of patients were lost to follow‐up, and then by the third survey administration a further 37% were lost to follow‐up (Table 3). Patients were unable to be reinterviewed most often because they had been (1) discharged, (2) were unavailable or having a procedure at time of recruitment, or (3) became too sick and symptomatic.
| Hospital Day | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| No. of eligible patients hospitalized | 163 | 161 | 138 | 102 | 70 | 50 | 35 | 24 | 19 | 17 |
| No. of patients surveyed | 8 | 124 | 70 | 30 | 22 | 13 | 7 | 6 | 2 | 0 |
| % of eligible patients surveyed | 4.9 | 77.0 | 50.7 | 29.4 | 31.4 | 26.0 | 20.0 | 25.0 | 10.5 | 0 |
| Survey No. | ||||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6+ | |
| ||||||
| No. of patients surveyed | 163 | 83 | 31 | 11 | 3 | 5 |
| Total barriers (all patients) | 533 | 235 | 84 | 22 | 7 | 8 |
| No. of barriers per patient, mean (SD) | 3.27(2.35) | 2.83 (2.11) | 2.71 (2.49) | 2.00 (1.73) | 2.33 (2.51) | 1.60 (2.30) |
| Median no. of barriers per patient | 3.0 | 3.0 | 2.0 | 1.0 | 2.0 | 0 |
| Median hospital day of survey administration | 2.0 | 3.0 | 5.0 | 6.0 | 8.0 | 13.0 |
| Initial admission survey, no. (%) | 163 (100.0) | 0 | 0 | 0 | 0 | 0 |
| Follow‐up survey, no. (%) | 0 | 38 (45.8) | 16 (51.6) | 4 (36.4) | 0 | 1 (20.0) |
| Survey 48 hours before discharge, no. (%) | 59 (36.2) | 45 (54.2) | 15 (48.4) | 7 (63.6) | 3 (100.0) | 4 (80.0) |
In total, over 889 individual barriers to discharge readiness were reported across all surveys. The total and mean numbers of barriers were highest on the admission intake survey, and numbers continued to decrease until the fourth survey. On average, the total number of barriers to discharge patients reported decreased by 0.15 (95% confidence interval: 0.01‐0.30) per day (P = 0.047).
Change in Barriers to Discharge
Sixty‐eight patients (42%) completed an admission intake survey as well as final survey 48 hours before discharge (Table 4). We observed a significant reduction in mean number of barriers reported between admission and discharge surveys (3.19 vs 2.53, P = 0.01). Sixty‐one patients (90%) left the hospital with 1 or more persistent barrier to a safe discharge. However, the 3 most common barriers to discharge readiness on the admission and final survey remained the same: unresolved pain, lack of understanding of plan for recovery, and daily living activities (eg, cooking, cleaning, and shopping). The number of patients with unresolved pain appeared to increase slightly, though this rise was not statistically significant. In contrast, there were significant reductions in patients reporting they were unaware of problems to watch out for postdischarge (28% vs 16%; P = 0.04) or did not understand their recovery plan (52% vs 40%; P = 0.03).
| Barrier to Discharge | Survey | |
|---|---|---|
| Admission, No. (%) | Final Survey, No. (%) | |
| ||
| Catheter is present? | 6 (7.2) | 6 (7.2) |
| Not out of bed, sitting in a chair, or walking? | 17 (20.5) | 13 (15.7) |
| Pain or discomfort? | 50 (60.2) | 52 (62.7) |
| Unable to get to the bathroom for toilet or to shower? | 15 (18.1) | 12 (14.5) |
| Unable to self‐care without help from others? | 27 (32.5) | 23 (27.7) |
| Unable to get your own medications? | 11 (13.3) | 14 (16.9) |
| Know what problems to watch for?* | 23 (27.7) | 13 (15.7) |
| Know where to call if you had problems? | 10 (12.0) | 8 (9.6) |
| Inability for personal care such as bathing, toileting, and eating? | 8 (9.6) | 11 (13.3) |
| Lack of support for emotional needs? | 16 (19.3) | 9 (10.8) |
| Unable to cook, clean, or do shopping? | 33 (39.8) | 25 (30.1) |
| Do not understand the overall plan for your recovery?* | 43 (51.8) | 33 (39.8) |
DISCUSSION
Assessing discharge readiness highlights an opportunity to engage patients directly in their discharge planning process. However, our prospective study of 163 hospitalized adults revealed that unresolved discharge barriers were common; 90% of patients were discharged with at least 1 issue that might inhibit an effective transition home. The majority of these patients were also discharged home without any support services. In addition, many of the major barriers patients reportedpain, lack of understanding around plans, and ability to provide self‐carewere consistent from admission to discharge, suggesting a missed opportunity to address problems present early in a patient's stay.
Some of the issues our patients described, such as pain; lack of understanding of a recovery plan; and functional, social, and environmental vulnerabilities that impede recovery, have been described in studies using data collected in the postacute time period.[13, 14, 15] Focus on postacute barriers is likely to be of limited clinical utility to assist in any real‐time discharge planning, particularly planning that assesses individual patients' needs and tailors programs and education appropriately. Having said this, consistency between our results and data collected from postdischarge patients again supports broad areas of improvement for health systems.
Persistent gaps in care at discharge may be a result of limited standardization of discharge processes and a lack of engagement in obtaining patient‐reported concerns. Lack of a framework for preparing individual patients for discharge has been recognized as a significant obstacle to effective discharge planning. For example, Hesselink et al.'s qualitative study with almost 200 patients and providers across multiple institutions described how lack of a standard approach to providing discharge planning resulted in gaps in information provision.[16] Similarly, Horwitz et al. described wide variation in discharge practices at a US academic medical center, suggesting lack of a standard approach to identifying patient needs.[14]
Although many transitions of care programs have supported implementation of specific care interventions at a hospital or health system level, there have been surprisingly few studies describing efforts to standardize the assessment of discharge barriers and prospectively engage individual patients.[17] One emblematic study used stakeholder interviews and process mapping to develop a readiness report within their electronic medical record (EMR).[17] Aggregate data from the EMR including orders and discharge plans were coded, extracted, and summarized into a report. The overall goal of the report was to identify progress toward completion of discharge tasks; however, a limitation was that it did not explicitly include patient self‐assessments. Another study by Grimmer et al. describes the development of a patient‐centered discharge checklist that incorporated patients and care concerns.[18] The themes incorporated into this checklist cover many transitional issues; however, outside of the checklist's development, few publications or Web resources describe it in actual use.
Our approach may represent an advance in approaches to engaging patients in discharge planning and preparing patients for leaving the hospital. Although our data do not support efficacy of our daily surveys in terms of improving discharge planning, this initial evaluation provides the framework upon which providers can develop discharge plans that are both standardized in terms of using a structured multidomain communication tool to elicit barriers, as well as patient‐centered and patient‐directed, by using the information collected in the survey tool to initiate tailored discharge planning earlier in the hospital stay. However, our program points out an important limitation of an entirely patient‐initiated program, which is difficulty obtaining truly daily assessments. During this study, we had a single research assistant visit patients as frequently as possible during hospitalization, but even daily visits did not yield complete information on all patients. Although this limitation may in part be due to the fact that our study was a focused pilot of an approach we hope to expand, it also represents the complexity of patient experience in the hospital, where patients are often out of their room for tests, are unable to complete a survey because of problematic symptoms, or simply are unwilling or unable to participate in regular surveys.
Our study has a number of limitations. First, the number of patients in our study overall, and the number who completed at least 2 surveys, was relatively small, limiting the generalizability of the study and our ability to determine the true prevalence of unresolved barriers at discharge. In addition, our selection criteria and response rates have limited our sample in that our final group may not be representative of all patients admitted to our medicine service. The broad exclusion of patients who had physical or psychosocial barriers, and those who were acutely unwell and symptomatic, has the potential to introduce selection bias given the excluded populations are those most at risk of readmission. We also acknowledge that some of the issues that patients' are reporting may be chronic ones. However, given the fact that patients feel these issues, even if chronic, are unaddressed or that they want to talk with their doctor about them, is still a very large potential gap in care and patient engagement.
However, despite these limitations, which seem most likely to produce a cohort that is more likely to be able to participate in our survey, and in turn more likely to participate in their care more broadly, we still observed disappointing resolution of discharge barriers. In addition, our adapted survey instrument, though based on well‐supported conceptual frameworks,[19] has not been extensively tested outside of our hospital setting. Finally, as a single‐center study, our results cannot be generalized to other settings.
Assessing discharge readiness highlights an opportunity to obtain patient self‐reported barriers to discharge. This can facilitate discharge planning that targets individual patient needs. This information also emphasizes potentially fruitful opportunities for improved communication and education activities, potentially if these data are fed back to providers in real time, potentially as part of team‐based dashboards or the context of interdisciplinary team models.
Acknowledgements
The authors thank all of the patients who participated in this project, and Yimdriuska Magan Gigi for her assistance with chart abstractions. The authors also acknowledge and thank John Boscardin for his statistical and analytic support.
Disclosures: James D. Harrison, and Drs. Ryan S. Greysen and Andrew D. Auerbach contributed to the concept, design, analysis, interpretation of data, drafting of the manuscript, critical revisions to the manuscript, and final approval of manuscript. Ronald Jacolbia and Alice Nguyen contributed to the acquisition of data, drafting and final approval of manuscript and project, and administrative and technical support. Dr. Auerbach was supported by National Heart, Lung, and Blood Institute grant K24 K24HL098372. Dr. Greysen is supported by the National Institutes of Health (NIH), National Institute of Aging (NIA) through the Claude D. Pepper Older Americans Independence Center (P30AG021342 NIH/NIA and K23AG045338‐01). The authors have no financial or other conflicts of interest to declare.
- , , . Trends and projections in inpatient hospital costs and utilization 2003–2013. HCUP statistical brief #175. July 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- , . Overview of hospital stays in the United States 2012. HCUP statistical brief #180. October 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- Joint Commision. The Joint Commission Comprehensive Accreditation Manual for Hospitals. Oak Brook, IL: The Joint Commission; 2015.
- , , . Hospital discharge and readmission. In: Post TW, ed. UpToDate website: Available at: http://www.uptodate.com/contents/hospital‐discharge‐and‐readmission. Accessed August 14, 2015.
- , . A patient centered model of care for hospital discharge. Clin Nurse Res. 2004;13:117–136.
- , , , , , . Which reasons do doctors, nurses and patients have for hospital discharge? A mixed methods study. PLoS One. 2014;9:e91333.
- , , . Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Med Care. 2010;48:482–486.
- , . Older people's perception of their readiness for discharge and postdischarge use of community support and services. Int J Older People Nurs. 2013;8:104–115.
- , , , . The care transitions intervention: Results of a randomized controlled trial. Arch Intern Med. 2006;166:1822–1828.
- , . Psychometric properties of the Readiness for Hospital Discharge Scale. J Nurs Meas. 2006;14:163–180.
- , , , et al. Perceived readiness for hospital discharge in adult medical‐surgical patients. Clin Nurse Spec. 2007;21:31–42.
- , , , . Validation of patient and nurse short forms of the Readiness for Hospital Discharge Scale and their relationship to return to the hospital. Health Serv Res. 2014;49:304–317.
- , , , et al. “Missing Pieces”—functional, social and environmental barriers to recovery for vulnerable older adults transitioning from hospital to home. J Am Geriatr Soc. 2014;62:1556–1561.
- , , , et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med. 2013;173:1715–1722.
- , , . Brief scale measuring patient prepardeness for hospital discharge to home: Psychometric properties. J Hosp Med. 2008;3:446–454.
- , , , et al. Improving patient discharge and reducing hospital readmission by using intervention mapping. BMC Health Serv Res. 2014;14:389.
- , , , , , . Development of a discharge readiness report within the electronic health record: a discharge planning tool. J Hosp Med. 2014;9:533–539.
- , , , . Incorporating Patient and Carer Concerns in Discharge Plans: The Development of a Practical Patient‐Centred Checklist. The Internet Journal of Allied Health Sciences and Practice. 2006;4: Article 5.
- , , , . Identifying keys to success in reducing readmissions using the ideal transitions in care framework. BMC Health Serv Res. 2014;14:423.
- , , . Trends and projections in inpatient hospital costs and utilization 2003–2013. HCUP statistical brief #175. July 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- , . Overview of hospital stays in the United States 2012. HCUP statistical brief #180. October 2014. Rockville, MD: Agency for Healthcare Research and Quality; 2014.
- Joint Commision. The Joint Commission Comprehensive Accreditation Manual for Hospitals. Oak Brook, IL: The Joint Commission; 2015.
- , , . Hospital discharge and readmission. In: Post TW, ed. UpToDate website: Available at: http://www.uptodate.com/contents/hospital‐discharge‐and‐readmission. Accessed August 14, 2015.
- , . A patient centered model of care for hospital discharge. Clin Nurse Res. 2004;13:117–136.
- , , , , , . Which reasons do doctors, nurses and patients have for hospital discharge? A mixed methods study. PLoS One. 2014;9:e91333.
- , , . Nurse and patient perceptions of discharge readiness in relation to postdischarge utilization. Med Care. 2010;48:482–486.
- , . Older people's perception of their readiness for discharge and postdischarge use of community support and services. Int J Older People Nurs. 2013;8:104–115.
- , , , . The care transitions intervention: Results of a randomized controlled trial. Arch Intern Med. 2006;166:1822–1828.
- , . Psychometric properties of the Readiness for Hospital Discharge Scale. J Nurs Meas. 2006;14:163–180.
- , , , et al. Perceived readiness for hospital discharge in adult medical‐surgical patients. Clin Nurse Spec. 2007;21:31–42.
- , , , . Validation of patient and nurse short forms of the Readiness for Hospital Discharge Scale and their relationship to return to the hospital. Health Serv Res. 2014;49:304–317.
- , , , et al. “Missing Pieces”—functional, social and environmental barriers to recovery for vulnerable older adults transitioning from hospital to home. J Am Geriatr Soc. 2014;62:1556–1561.
- , , , et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med. 2013;173:1715–1722.
- , , . Brief scale measuring patient prepardeness for hospital discharge to home: Psychometric properties. J Hosp Med. 2008;3:446–454.
- , , , et al. Improving patient discharge and reducing hospital readmission by using intervention mapping. BMC Health Serv Res. 2014;14:389.
- , , , , , . Development of a discharge readiness report within the electronic health record: a discharge planning tool. J Hosp Med. 2014;9:533–539.
- , , , . Incorporating Patient and Carer Concerns in Discharge Plans: The Development of a Practical Patient‐Centred Checklist. The Internet Journal of Allied Health Sciences and Practice. 2006;4: Article 5.
- , , , . Identifying keys to success in reducing readmissions using the ideal transitions in care framework. BMC Health Serv Res. 2014;14:423.