With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

With this issue, I officially assume the role of Editor-in-Chief of the Journal of Hospital Medicine. I am honored and humbled to serve as the third editor for this journal and thankful to my predecessors, Drs. Mark V. Williams and Andrew D. Auerbach, for establishing it as the premier forum for publication of research in hospital medicine.

The journal has always taken a broad view of its mission. Our focus on improving value and quality of healthcare for children and adults will continue. We are also well-positioned to expand our scope and publish the highest quality research and commentary on the evolving healthcare system, including adoption of new technology, population health management, and regionalization in healthcare, and our role within it. There is also increasing recognition that these trends have implications for patient experience and outcomes, healthcare professional well-being, and the learning environment. We welcome qualitative and quantitative research that provides insight into understanding and addressing these new challenges. We also seek your Perspectives in Hospital Medicine to highlight innovations or controversies in healthcare delivery or policy.

The journal landscape has evolved. We consume medical information in many different formats with a rapidly diminishing reliance on paper and ink. Rather than perusing a journal at the end of a busy workday, we now capitalize on small increments of time in between meetings or other activities. The journal has taken a leading role in engaging readers through social media (@JHospMedicine) with Twitter-based features such as journal clubs (#JHMChat) to discuss recently published research as well as visual abstracts to efficiently share scientific advances.¹ We will extend these efforts to include “tweetorials,” video abstracts, and a redesigned web presence, allowing us to transcend the constraints of traditional written articles. Our goals are to increase the visibility of authors and accessibility of their research, allow readers to engage with the journal in formats that best meet their needs, and enhance knowledge retention and knowledge translation to improve healthcare systems and patient outcomes.

The Journal of Hospital Medicine also strives to remain relevant to clinical practice through columns that seek to improve diagnostic reasoning (Clinical Care Conundrums), value and innovation in healthcare (Choosing Wisely: Things We Do For No Reason, Choosing Wisely: Next Steps in Improving Healthcare Value), and, through our long-form reviews, core medical knowledge. While in-depth reviews provide an important synthesis of a topic, our work environment and schedules are not always conducive to reading in this manner; busy clinicians may benefit from focused updates. We will introduce new shorter format reviews addressing clinical content, including practice guidelines, and research methodology.

Finally, we are invested in developing a leadership pipeline for academic medicine. Our Editorial Fellowship will provide educational experiences, professional development, and academic and networking opportunities for a cadre of young physicians.² A new column will highlight leadership and professional development lessons from renowned leaders from a broad range of disciplines. We also value diversity and inclusion. Disparities in academic medical leadership, though well-recognized, persist. For example, women now comprise more than half of all incoming medical students³ and 41% of faculty, yet only 24% of full professors, 18% of department chairs, and 17% of deans.⁴ This journal will play an important role in creating a diverse pipeline of academic leaders. We will lead by example and, in the coming year, develop approaches to create equity in all facets of journal leadership and authorship.

I am grateful to Dr. Auerbach for his visionary stewardship of the journal. As I take the helm, the journal will continue to evolve with the changing landscape of healthcare. I am fortunate to work with an exceptionally talented team, and I look forward to serving the journal and the field together to accomplish these goals.

Disclosures

The author has no financial conflicts of interest to disclose

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The peer review and editorial processes are integral activities in academic medicine that provide ethical, independent, and unbiased critical assessment of submitted manuscripts to academic journals. Recognizing that few trainees or junior faculty are formally exposed to these processes,¹ the Journal of Hospital Medicine aims to fill this opportunity gap through the launch of a one-year Editorial Fellowship.

The Fellowship is open to chief residents, hospital medicine fellows, and junior faculty (eg, Assistant Professor or Clinical Instructor). Starting in July of each year, a group of four to six applicants are paired with editorial mentors who are current JHM Deputy or Associate Editors. Structured as a distance-learning program, this program aims to allow Fellows the ability to continue in their full time professional roles while also allowing the opportunity to engage with national leaders in hospital medicine. Regular communication and interactions take place through both synchronous and asynchronous means. Fellows’ responsibilities during the 12-month experience include: completion of six guided peer reviews, preparation of one or two editorials, participation in monthly editorial meetings, and quarterly educational videoconferences. Interested Fellows may also have an opportunity to co-lead the journal’s online journal club, #JHMChat.² Fellows are expected to attend the editorial staff meeting at the annual Society of Hospital Medicine Conference.

With this program, JHM aims to accomplish several tasks. First, we hope to offer a unique educational experience that allows for further growth, development, inspiration, and experience in academic medicine—specifically around the manuscript review and editorial processes. Second, recognizing that a journal’s quality is frequently a product of its reviewers, JHM hopes to build a cadre of well-trained and experienced reviewers and, hopefully, future members of the JHM editorial leadership team. Third, the program hopes to act as a networking experience, allowing editorial Fellows to learn from, collaborate with, and become academic leaders in the field. Finally, we hope to provide an opportunity for Fellows to be academically productive in their composition of editorial content—an output that will help catalyze their professional development.

We believe that in working closely with the JHM editorial staff, this program will help develop the next generation of leaders in academic hospital medicine. We strongly encourage applications from physicians who have been historically under-represented in leadership in academic medicine. Further details and the application can be found in the appendix and on the JHM website (www.journalofhospitalmedicine.com). It will be announced annually through the @JHospMedicine twitter handle.

Disclosures

The authors have nothing to disclose.

The Centers of Medicare and Medicaid Services (CMS) has introduced new payment models that tie quality and value incentives to 90% of fee-for-service payments and provide 50% of Medicare payments through alternative payment models.¹ The push toward value comes after productivity-based physician reimbursement (ie, fee for service) has been associated with poor quality care, including delayed diagnoses, complications, readmissions, increased length of stay, and high costs of care.^2-5 The method of physician payment is widely believed to affect clinical behavior by incentivizing doing more, coding for more, and billing for more.^6-7 Although payment systems may be used to achieve policy objectives,⁸ little is known about the association of different payment systems with the culture of delivering value-based care among frontline clinicians.

Culture is defined as a system of shared assumptions, values, beliefs, and norms within an environment and has a powerful role in shaping clinician practice patterns.^9-12 The culture within medicine currently contributes to the overuse of resources,^11,13 and a culture for improvement is correlated with clinical outcomes. A systematic review found a consistent association between positive organization culture and improved outcomes including mortality.¹⁴ Across health systems, institutions with high scores on patient safety culture surveys have shown improvements in clinical behaviors and patient outcomes.^15-18

In this study, we aim to describe high-value care culture among internal medicine hospitalists across diverse hospitals and evaluate the relationship between physician reimbursement and high-value care culture.

Study Design

This study is an observational, cross-sectional survey-based study of hospitalists from 12 hospitals in California between January and June 2016.

Study Population

A total of 12 hospitals with hospitalist programs in California were chosen to represent three types of hospitals (ie, four university, four community, and four safety net). Safety-net hospitals, which traditionally serve low-income and medically and socially vulnerable patients were defined as those in the top quartile (ie, greater than 0.5) of their Disproportionate Share Index (DSH), which measures Medicaid patient load.^19-20

To select hospitals with varying value-based care performance, we stratified using CMS value-based purchasing (VBP) scores from fiscal year 2015; these scores have been used to adjust reimbursement for just over 3,000 hospitals in the VBP program of CMS.^22,23 CMS calculates the VBP total performance score as a composite of four domains: (1) clinical processes of care (20% of total performance); (2) patient satisfaction (30%); (3) patient outcomes, including mortality and complications (30%); and (4) cost defined by Medicare payment per beneficiary (20%).²¹ Established quality measures are based on data reported by participating hospitals and chart abstraction during 2011-2014.²² Although other clinical measures of care intensity have been used as proxies of value-based care,^23,24 we used the measure of value that has been publically reported by the CMS VBP given its wide use and effects on reimbursements for 80% of hospitals in the CMS VBP program in 2015.²⁵ We obtained institution-level data from the CMS VBP Program and Hospital Compare files. Each of the three types of hospitals represented institutions with low, middle, and high VBP performance (split in tertiles) as reported by the CMS VBP program. To increase the number of participants in tertiles with fewer hospitalists, a fourth hospital was selected for each hospital type.

We excluded individual hospitalists who primarily identified as working in subspecialty divisions and those who spent less than eight weeks during the last year providing direct patient care on inpatient internal medicine services at the studied institution.

Measurement

Hospitalists were asked to complete the High-Value Care Culture Survey (HVCCS^TM), which measures the culture of value-based decision making among frontline clinicians.²⁶ Similar to other validated surveys for the assessment of patient safety culture,^27,28 the HVCCS can be used to identify target areas for improvement. The survey includes four domains: (1) leadership and health system messaging, (2) data transparency and access, (3) comfort with cost conversations, and (4) blame-free environment. This tool was developed by using a two-phase national modified Delphi process. It was evaluated at two academic centers to complete factor analysis and assess internal consistency, reliability, and validity among internal medicine hospitalists and residents. Validation included estimating product-moment correlation of overall HVCCS scores and domain scores with the CMS institutional VBP scores. HVCCS scores are standardized to a 0-100 point scale for each of the four domains and are then averaged to obtain an overall score.²⁶

In the survey, value was defined as the quality of care provided to patients in relation to the costs required to deliver that care, and high-value care was defined as care that tried to maximize quality while minimizing costs. Quality was defined as the degree to which health services increased the likelihood of desired health outcomes that are safe, effective, patient centered, timely, equitable, and consistent with current professional knowledge. Cost was defined as the negative financial, physical, and emotional effects on patients and the health system.²⁶

Data Analysis

We described the overall institutional mean high-value care culture and domain scores measured by the HVCCS, hospitalist demographics and training experiences, and hospital characteristics. We also described individual survey items. Descriptive statistics were stratified and compared on the basis of hospital type (ie, safety net, community, or university). We assessed the relationship between the clinician perception of reimbursement structure within their divisions and individually reported high-value care culture scores using bivariate and multilevel linear regression. We hypothesized that compared with hospitalists who were paid with salaries or wages, those who reported reimbursement with productivity adjustments may report lower HVCCS scores and those who reported reimbursement with quality or value adjustments may report higher HVCCS scores. We adjusted for physician- and hospital-level characteristics, including age, gender, and training track, and considered hospital type and size as random effects.

This study was approved by the Institutional Review Board at all 12 sites. All analyses were conducted using STATA^® 13.0 (College Station, Texas).

Hospitalist Characteristics

A total of 255 (68.9%, 255/370) hospitalists across all sites completed the survey. Of these respondents, 135 were female (50.6%). On average, hospitalists were 39 years of age (SD 6.8), trained in categorical tracks (221; 86.7%), and had previously trained for 14.3 months at a safety-net hospital (SD 14.2). In total, 166 hospitalists (65.1%) reported being paid with salary or wages, 77 (30.2%) with salary plus productivity adjustments, and 12 (4.7%) with salary plus quality or value adjustments. Moreover, 123 (48.6%) hospitalists agreed that funding for their group depended on the volume of services they delivered. Community-based hospitalists reported higher rates of reimbursement with salary plus productivity (47; 32.0%) compared with their counterparts from university-based (24; 28.2%) and safety-net based programs (6; 26.1%). Among the three different hospital types, significant differences exist in hospitalist mean age (P < .001), gender (P = .01), and the number of months training in a safety-net hospital (P = .02; Table 1).

Hospital Characteristics

Of the 12 study sites, four from each type of hospital (ie, safety-net based, community based, and university based) and four representing each value-based purchasing performance tertile (ie, high, middle, and low) were included. Eleven (91.7%) sites were located in urban areas with an average DSH index of 0.40 (SD 0.23), case mix index of 1.97 (SD 0.28), and bed size of 435.5 (SD 146.0; Table 1).

In multilevel regression modeling across all 12 sites, hospitalists from community-based hospitalist programs reported lower mean HVCCS scores (β = −4.4, 95% CI −8.1 to −0.7; Table 2) than those from other hospital types.

High-Value Care Culture Survey Scores

The mean HVCCS score was 50.2 (SD 13.6), and mean domain scores across all sites were 65.4 (SD 15.6) for leadership and health system messaging, 32.4 (SD 22.8) for data transparency and access, 52.1 (SD 19.7) for comfort with cost conversations, and 50.7 (SD 21.4) for blame-free environment (Table 1). For the majority (two-thirds) of individual HVCCS items, more than 30% of hospitalists across all sites agreed or strongly agreed that components of a low-value care culture exist within their institutions. For example, over 80% of hospitalists reported low transparency and limited access to data (see Appendix I for complete survey responses).

Hospitalists reported different HVCCS domains as strengths or weaknesses within their institutions in accordance with hospital type. Compared with university-based and safety-net-based hospitalists, community-based hospitalists reported lower scores in having a blame-free environment (466, SD 21.8). Nearly 50% reported that the clinicians’ fear of legal repercussions affects their frequency of ordering unneeded tests or procedures, and 30% reported that individual clinicians are blamed for complications. Nearly 40% reported that clinicians are uncomfortable discussing the costs of tests or treatments with patients and reported that clinicians do not feel that physicians should discuss costs with patients. Notably, community-based hospitalists uniquely differed in how they reported components of leadership and health system messaging. Over 60% reported a work climate or role modeling supportive of delivering quality care at lower costs. Only 48%, however, reported success seen from implemented efforts, and 45% reported weighing costs in clinical decision making (Table 1, Appendix I).

University-based hospitalists had significantly higher scores in leadership and health system messaging (67.4, SD 16.9) than community-based and safety-net-based hospitalists. They reported that their institutions consider their suggestions to improve quality care at low cost (75%), openly discuss ways to deliver this care (64%), and are actively implementing projects (73%). However, only 54% reported seeing success from implemented high-value care efforts (Table 1, Appendix I).

Safety-net hospitalists reported lower scores in leadership and health system messaging (56.8, SD 10.5) than university-based and community-based hospitalists. Few hospitalists reported a work climate (26%) or role modeling (30%) that is supportive of delivering quality care at low costs, openly discusses ways to deliver this care (35%), encourages frontline clinicians to pursue improvement projects (57%), or actively implements projects (26%). They also reported higher scores in the blame-free environment domain (59.8, SD 22.3; Table 1; Appendix 1).

Productivity Adjustments and High-Value Care Culture

In multilevel regression modeling, hospitalists who reported reimbursement with salary plus productivity adjustments had a lower mean HVCCS score (β = −6.2, 95% CI −9.9 to –2.5) than those who reported payment with salary or wages alone. Further multilevel regression modeling for each HVCCS domain revealed that hospitalists who reported reimbursement with salary plus productivity adjustments had lower scores in the leadership and health system messaging domain (β = −4.9, 95% CI −9.3 to −0.6) and data transparency and access domain (β = −10.7, 95% CI −16.7 to −4.6). No statistically significant difference was found between hospitalists who reported reimbursement with quality or value adjustments.

Understanding the drivers that are associated with a high-value care culture is necessary as payment models for hospitals transition from volume-based to value-based care. In this study, we found a meaningful association (β = −6.2) between clinician reimbursement schemes and measures of high-value care culture. A six-point change in the HVCCS score would correspond with a hospital moving from the top quartile to the median, which represents a significant change in performance. The relationship between clinician reimbursement schemes and high-value care culture may be a bidirectional relationship. Fee for service, the predominant payment scheme, places pressure on clinicians to maximize volume, focus on billing, and provide reactive care.^7,29 Conversely, payment schemes that avoid these incentives (ie, salary, wages, and adjustments for quality or value), especially if incentives are felt by frontline clinicians, may better align with goals for long-term health outcomes for patient populations and reduce excess visits and services.^2-6,8,30-34 At the same time, hospitals with a strong high-value care culture may be more likely to introduce shared savings programs and alternative payment models than those without. Through these decisions, the leadership can play an important role in creating an environment for change.³⁴ Similar to the study sites, hospitals in California have a higher percentage of risk-based payments than hospitals in other states (>22%)³⁵ and may also provide incentives to promote a high-value care culture or affect local physician compensation models.

Hospitals have options in how they choose to pay their clinicians, and these decisions may have downstream effects, such as building or eroding high-value care culture among clinicians or staff. A dose-response relationship between physician compensation models and value culture is plausible (salary with productivity < salary only < salary with value incentive). However, we did not find a statistically significant difference for salary with value incentive. This result may be attributed to the relatively small sample size in this study.

Hospitals can also improve their internal processes, organizational structure, and align their institutional payment contracts with those that emphasize value over fee-for-service-based incentives to increase value in care delivery.³⁶ The operation of hospitals is challenging when competing payment incentives are used at the same time,⁷ and leadership will likely achieve more success in improving a high-value care culture and value performance when all efforts, including clinician and institutional payment, are aligned.^37-38

Enduring large systems redesign will require directing attention to local organizational culture. For the majority of individual HVCCS items, 30% or more hospitalists across all sites agreed or strongly agreed that components of low-value care culture exist within their institutions. This response demonstrates a lack of focus on culture to address high-value care improvement among the study sites. Division and program leaders can begin measuring culture within their groups to develop new interventions that target culture change and improve value.³⁴ No single panacea exists for the value improvement of hospitalist programs in California across all hospital types and sites.

Unique trends, however, emerge among each hospital type that could direct future improvements. In addition to all sites requiring increased transparency and access to data, community-based hospitalists identified the need for improvement in the creation of a blame-free environment, comfort with cost conversations, and aspects of leadership and health system messaging. While a high proportion of these hospitalists reported a work culture and role modeling that support the delivery of quality care at low costs, opportunities to create open discussion and frontline involvement in improvement efforts, weigh costs into clinical decision making, and cost conversations with patients exist. We hypothesize that these opportunities exist because community-based hospitals create infrastructure and technology to drive improvement that is often unseen by frontline providers. University-based hospitalists performed higher on three of the four domains compared with their counterparts but may have opportunities to promote a blame-free environment. A great proportion of these hospitalists reported the occurrence of open discussion and active projects within their institutions but also identified opportunities for the improvement of project implementation. Safety-net hospitalists reported the need to improve leadership and health system messaging across most domain items. Further study is required to evaluate reasons for safety-net hospitalists’ responses. We hypothesize that these responses may be related to having limited institutional resources to provide data and coordinated care and different institutional payment models. Each of these sites could identify trends in specific questions identified by the HVCCS for improvement in the high-value care culture.²⁵

Our study evaluated 12 hospitalist programs in California that represent hospitals of different sizes and institutional VBP performance. A large multisite study that evaluates HVCCS across other specialties and disciplines in medicine, all regions of the country, and ambulatory care settings may be conducted in the future. Community-based hospitalist programs also reported low mean HVCCS scores, and further studies could better understand this relationship.

The limitations of the study include its small subgroup sample size and the lack of a gold standard for the measurement of high-value care. As expected, hospitalist groups among safety-net hospitals in California are small, and we may have been underpowered to determine some correlations presented by safety-net sites when stratifying by hospital type. Other correlations also may have been limited by sample size, including differences in HVCCS scores based on reimbursement and hospital type and the correlation between a blame-free environment and reimbursement type. Additionally, the field lacks a gold standard for the measurement of high-value care to help stratify institutional value performance for site selection. The VBP measure presents policy implications and is currently the best available measure with recent value data for over 3,000 hospitals nationally and representing various types of hospitals. This study is also cross-sectional and may benefit from the further evaluation of organizational culture over time and across other settings.

The HVCCS can identify clear targets for improvement and has been evaluated among internal medicine hospitalists. Hospitalists who are paid partly based on productivity reported low measures of high-value care culture at their institutions. As the nation moves toward increasingly value-based payment models, hospitals can strive to improve their understanding of their individual culture for value and begin addressing gaps.

Acknowledgments

The authors wish to thank Michael Lazarus, MD from the University of California Los Angeles; Robert Wachter, MD, James Harrison, PhD; Victoria Valencia, MPH from Dell Medical School at the University of Texas at Austin; Mithu Molla, MD from University of California Davis; Gregory Seymann, MD from the University of California San Diego; Bindu Swaroop, MD and Alpesh Amin, MD from University of California Irvine; Jessica Murphy, DO and Danny Sam, MD from Kaiser Permanente Santa Clara; Thomas Baudendistel, MD and Rajeeva Ranga, MD from Kaiser Permanente Oakland; Yile Ding, MD from California Pacific Medical Center; Soma Wali, MD from Los Angeles County/ OliveView UCLA Medical Center; Anshu Abhat, MD, MPH from the LA BioMed Institute at Los Angeles County/ Harbor-UCLA Medical Center; Steve Tringali, MD from Community Regional Medical Center Fresno; and Dan Dworsky, MD from Scripps Green Hospital for their site leadership and participation with the study.

Disclosures

Dr. Gupta is the Director of the Teaching Value in Healthcare Learning Network at Costs of Care. Dr. Moriates receives royalties from McGraw Hill for the textbook “Understanding Value-based Healthcare” outside of the submitted work and is the Director of Implementation at Costs of Care.

The Centers of Medicare and Medicaid Services (CMS) has introduced new payment models that tie quality and value incentives to 90% of fee-for-service payments and provide 50% of Medicare payments through alternative payment models.¹ The push toward value comes after productivity-based physician reimbursement (ie, fee for service) has been associated with poor quality care, including delayed diagnoses, complications, readmissions, increased length of stay, and high costs of care.^2-5 The method of physician payment is widely believed to affect clinical behavior by incentivizing doing more, coding for more, and billing for more.^6-7 Although payment systems may be used to achieve policy objectives,⁸ little is known about the association of different payment systems with the culture of delivering value-based care among frontline clinicians.

Culture is defined as a system of shared assumptions, values, beliefs, and norms within an environment and has a powerful role in shaping clinician practice patterns.^9-12 The culture within medicine currently contributes to the overuse of resources,^11,13 and a culture for improvement is correlated with clinical outcomes. A systematic review found a consistent association between positive organization culture and improved outcomes including mortality.¹⁴ Across health systems, institutions with high scores on patient safety culture surveys have shown improvements in clinical behaviors and patient outcomes.^15-18

In this study, we aim to describe high-value care culture among internal medicine hospitalists across diverse hospitals and evaluate the relationship between physician reimbursement and high-value care culture.

Study Design

This study is an observational, cross-sectional survey-based study of hospitalists from 12 hospitals in California between January and June 2016.

Study Population

A total of 12 hospitals with hospitalist programs in California were chosen to represent three types of hospitals (ie, four university, four community, and four safety net). Safety-net hospitals, which traditionally serve low-income and medically and socially vulnerable patients were defined as those in the top quartile (ie, greater than 0.5) of their Disproportionate Share Index (DSH), which measures Medicaid patient load.^19-20

To select hospitals with varying value-based care performance, we stratified using CMS value-based purchasing (VBP) scores from fiscal year 2015; these scores have been used to adjust reimbursement for just over 3,000 hospitals in the VBP program of CMS.^22,23 CMS calculates the VBP total performance score as a composite of four domains: (1) clinical processes of care (20% of total performance); (2) patient satisfaction (30%); (3) patient outcomes, including mortality and complications (30%); and (4) cost defined by Medicare payment per beneficiary (20%).²¹ Established quality measures are based on data reported by participating hospitals and chart abstraction during 2011-2014.²² Although other clinical measures of care intensity have been used as proxies of value-based care,^23,24 we used the measure of value that has been publically reported by the CMS VBP given its wide use and effects on reimbursements for 80% of hospitals in the CMS VBP program in 2015.²⁵ We obtained institution-level data from the CMS VBP Program and Hospital Compare files. Each of the three types of hospitals represented institutions with low, middle, and high VBP performance (split in tertiles) as reported by the CMS VBP program. To increase the number of participants in tertiles with fewer hospitalists, a fourth hospital was selected for each hospital type.

We excluded individual hospitalists who primarily identified as working in subspecialty divisions and those who spent less than eight weeks during the last year providing direct patient care on inpatient internal medicine services at the studied institution.

Measurement

Hospitalists were asked to complete the High-Value Care Culture Survey (HVCCS^TM), which measures the culture of value-based decision making among frontline clinicians.²⁶ Similar to other validated surveys for the assessment of patient safety culture,^27,28 the HVCCS can be used to identify target areas for improvement. The survey includes four domains: (1) leadership and health system messaging, (2) data transparency and access, (3) comfort with cost conversations, and (4) blame-free environment. This tool was developed by using a two-phase national modified Delphi process. It was evaluated at two academic centers to complete factor analysis and assess internal consistency, reliability, and validity among internal medicine hospitalists and residents. Validation included estimating product-moment correlation of overall HVCCS scores and domain scores with the CMS institutional VBP scores. HVCCS scores are standardized to a 0-100 point scale for each of the four domains and are then averaged to obtain an overall score.²⁶

In the survey, value was defined as the quality of care provided to patients in relation to the costs required to deliver that care, and high-value care was defined as care that tried to maximize quality while minimizing costs. Quality was defined as the degree to which health services increased the likelihood of desired health outcomes that are safe, effective, patient centered, timely, equitable, and consistent with current professional knowledge. Cost was defined as the negative financial, physical, and emotional effects on patients and the health system.²⁶

Data Analysis

We described the overall institutional mean high-value care culture and domain scores measured by the HVCCS, hospitalist demographics and training experiences, and hospital characteristics. We also described individual survey items. Descriptive statistics were stratified and compared on the basis of hospital type (ie, safety net, community, or university). We assessed the relationship between the clinician perception of reimbursement structure within their divisions and individually reported high-value care culture scores using bivariate and multilevel linear regression. We hypothesized that compared with hospitalists who were paid with salaries or wages, those who reported reimbursement with productivity adjustments may report lower HVCCS scores and those who reported reimbursement with quality or value adjustments may report higher HVCCS scores. We adjusted for physician- and hospital-level characteristics, including age, gender, and training track, and considered hospital type and size as random effects.

This study was approved by the Institutional Review Board at all 12 sites. All analyses were conducted using STATA^® 13.0 (College Station, Texas).

Hospitalist Characteristics

A total of 255 (68.9%, 255/370) hospitalists across all sites completed the survey. Of these respondents, 135 were female (50.6%). On average, hospitalists were 39 years of age (SD 6.8), trained in categorical tracks (221; 86.7%), and had previously trained for 14.3 months at a safety-net hospital (SD 14.2). In total, 166 hospitalists (65.1%) reported being paid with salary or wages, 77 (30.2%) with salary plus productivity adjustments, and 12 (4.7%) with salary plus quality or value adjustments. Moreover, 123 (48.6%) hospitalists agreed that funding for their group depended on the volume of services they delivered. Community-based hospitalists reported higher rates of reimbursement with salary plus productivity (47; 32.0%) compared with their counterparts from university-based (24; 28.2%) and safety-net based programs (6; 26.1%). Among the three different hospital types, significant differences exist in hospitalist mean age (P < .001), gender (P = .01), and the number of months training in a safety-net hospital (P = .02; Table 1).

Hospital Characteristics

Of the 12 study sites, four from each type of hospital (ie, safety-net based, community based, and university based) and four representing each value-based purchasing performance tertile (ie, high, middle, and low) were included. Eleven (91.7%) sites were located in urban areas with an average DSH index of 0.40 (SD 0.23), case mix index of 1.97 (SD 0.28), and bed size of 435.5 (SD 146.0; Table 1).

In multilevel regression modeling across all 12 sites, hospitalists from community-based hospitalist programs reported lower mean HVCCS scores (β = −4.4, 95% CI −8.1 to −0.7; Table 2) than those from other hospital types.

High-Value Care Culture Survey Scores

The mean HVCCS score was 50.2 (SD 13.6), and mean domain scores across all sites were 65.4 (SD 15.6) for leadership and health system messaging, 32.4 (SD 22.8) for data transparency and access, 52.1 (SD 19.7) for comfort with cost conversations, and 50.7 (SD 21.4) for blame-free environment (Table 1). For the majority (two-thirds) of individual HVCCS items, more than 30% of hospitalists across all sites agreed or strongly agreed that components of a low-value care culture exist within their institutions. For example, over 80% of hospitalists reported low transparency and limited access to data (see Appendix I for complete survey responses).

Hospitalists reported different HVCCS domains as strengths or weaknesses within their institutions in accordance with hospital type. Compared with university-based and safety-net-based hospitalists, community-based hospitalists reported lower scores in having a blame-free environment (466, SD 21.8). Nearly 50% reported that the clinicians’ fear of legal repercussions affects their frequency of ordering unneeded tests or procedures, and 30% reported that individual clinicians are blamed for complications. Nearly 40% reported that clinicians are uncomfortable discussing the costs of tests or treatments with patients and reported that clinicians do not feel that physicians should discuss costs with patients. Notably, community-based hospitalists uniquely differed in how they reported components of leadership and health system messaging. Over 60% reported a work climate or role modeling supportive of delivering quality care at lower costs. Only 48%, however, reported success seen from implemented efforts, and 45% reported weighing costs in clinical decision making (Table 1, Appendix I).

University-based hospitalists had significantly higher scores in leadership and health system messaging (67.4, SD 16.9) than community-based and safety-net-based hospitalists. They reported that their institutions consider their suggestions to improve quality care at low cost (75%), openly discuss ways to deliver this care (64%), and are actively implementing projects (73%). However, only 54% reported seeing success from implemented high-value care efforts (Table 1, Appendix I).

Safety-net hospitalists reported lower scores in leadership and health system messaging (56.8, SD 10.5) than university-based and community-based hospitalists. Few hospitalists reported a work climate (26%) or role modeling (30%) that is supportive of delivering quality care at low costs, openly discusses ways to deliver this care (35%), encourages frontline clinicians to pursue improvement projects (57%), or actively implements projects (26%). They also reported higher scores in the blame-free environment domain (59.8, SD 22.3; Table 1; Appendix 1).

Productivity Adjustments and High-Value Care Culture

In multilevel regression modeling, hospitalists who reported reimbursement with salary plus productivity adjustments had a lower mean HVCCS score (β = −6.2, 95% CI −9.9 to –2.5) than those who reported payment with salary or wages alone. Further multilevel regression modeling for each HVCCS domain revealed that hospitalists who reported reimbursement with salary plus productivity adjustments had lower scores in the leadership and health system messaging domain (β = −4.9, 95% CI −9.3 to −0.6) and data transparency and access domain (β = −10.7, 95% CI −16.7 to −4.6). No statistically significant difference was found between hospitalists who reported reimbursement with quality or value adjustments.

Understanding the drivers that are associated with a high-value care culture is necessary as payment models for hospitals transition from volume-based to value-based care. In this study, we found a meaningful association (β = −6.2) between clinician reimbursement schemes and measures of high-value care culture. A six-point change in the HVCCS score would correspond with a hospital moving from the top quartile to the median, which represents a significant change in performance. The relationship between clinician reimbursement schemes and high-value care culture may be a bidirectional relationship. Fee for service, the predominant payment scheme, places pressure on clinicians to maximize volume, focus on billing, and provide reactive care.^7,29 Conversely, payment schemes that avoid these incentives (ie, salary, wages, and adjustments for quality or value), especially if incentives are felt by frontline clinicians, may better align with goals for long-term health outcomes for patient populations and reduce excess visits and services.^2-6,8,30-34 At the same time, hospitals with a strong high-value care culture may be more likely to introduce shared savings programs and alternative payment models than those without. Through these decisions, the leadership can play an important role in creating an environment for change.³⁴ Similar to the study sites, hospitals in California have a higher percentage of risk-based payments than hospitals in other states (>22%)³⁵ and may also provide incentives to promote a high-value care culture or affect local physician compensation models.

Hospitals have options in how they choose to pay their clinicians, and these decisions may have downstream effects, such as building or eroding high-value care culture among clinicians or staff. A dose-response relationship between physician compensation models and value culture is plausible (salary with productivity < salary only < salary with value incentive). However, we did not find a statistically significant difference for salary with value incentive. This result may be attributed to the relatively small sample size in this study.

Hospitals can also improve their internal processes, organizational structure, and align their institutional payment contracts with those that emphasize value over fee-for-service-based incentives to increase value in care delivery.³⁶ The operation of hospitals is challenging when competing payment incentives are used at the same time,⁷ and leadership will likely achieve more success in improving a high-value care culture and value performance when all efforts, including clinician and institutional payment, are aligned.^37-38

Enduring large systems redesign will require directing attention to local organizational culture. For the majority of individual HVCCS items, 30% or more hospitalists across all sites agreed or strongly agreed that components of low-value care culture exist within their institutions. This response demonstrates a lack of focus on culture to address high-value care improvement among the study sites. Division and program leaders can begin measuring culture within their groups to develop new interventions that target culture change and improve value.³⁴ No single panacea exists for the value improvement of hospitalist programs in California across all hospital types and sites.

Unique trends, however, emerge among each hospital type that could direct future improvements. In addition to all sites requiring increased transparency and access to data, community-based hospitalists identified the need for improvement in the creation of a blame-free environment, comfort with cost conversations, and aspects of leadership and health system messaging. While a high proportion of these hospitalists reported a work culture and role modeling that support the delivery of quality care at low costs, opportunities to create open discussion and frontline involvement in improvement efforts, weigh costs into clinical decision making, and cost conversations with patients exist. We hypothesize that these opportunities exist because community-based hospitals create infrastructure and technology to drive improvement that is often unseen by frontline providers. University-based hospitalists performed higher on three of the four domains compared with their counterparts but may have opportunities to promote a blame-free environment. A great proportion of these hospitalists reported the occurrence of open discussion and active projects within their institutions but also identified opportunities for the improvement of project implementation. Safety-net hospitalists reported the need to improve leadership and health system messaging across most domain items. Further study is required to evaluate reasons for safety-net hospitalists’ responses. We hypothesize that these responses may be related to having limited institutional resources to provide data and coordinated care and different institutional payment models. Each of these sites could identify trends in specific questions identified by the HVCCS for improvement in the high-value care culture.²⁵

Our study evaluated 12 hospitalist programs in California that represent hospitals of different sizes and institutional VBP performance. A large multisite study that evaluates HVCCS across other specialties and disciplines in medicine, all regions of the country, and ambulatory care settings may be conducted in the future. Community-based hospitalist programs also reported low mean HVCCS scores, and further studies could better understand this relationship.

The limitations of the study include its small subgroup sample size and the lack of a gold standard for the measurement of high-value care. As expected, hospitalist groups among safety-net hospitals in California are small, and we may have been underpowered to determine some correlations presented by safety-net sites when stratifying by hospital type. Other correlations also may have been limited by sample size, including differences in HVCCS scores based on reimbursement and hospital type and the correlation between a blame-free environment and reimbursement type. Additionally, the field lacks a gold standard for the measurement of high-value care to help stratify institutional value performance for site selection. The VBP measure presents policy implications and is currently the best available measure with recent value data for over 3,000 hospitals nationally and representing various types of hospitals. This study is also cross-sectional and may benefit from the further evaluation of organizational culture over time and across other settings.

The HVCCS can identify clear targets for improvement and has been evaluated among internal medicine hospitalists. Hospitalists who are paid partly based on productivity reported low measures of high-value care culture at their institutions. As the nation moves toward increasingly value-based payment models, hospitals can strive to improve their understanding of their individual culture for value and begin addressing gaps.

Acknowledgments

The authors wish to thank Michael Lazarus, MD from the University of California Los Angeles; Robert Wachter, MD, James Harrison, PhD; Victoria Valencia, MPH from Dell Medical School at the University of Texas at Austin; Mithu Molla, MD from University of California Davis; Gregory Seymann, MD from the University of California San Diego; Bindu Swaroop, MD and Alpesh Amin, MD from University of California Irvine; Jessica Murphy, DO and Danny Sam, MD from Kaiser Permanente Santa Clara; Thomas Baudendistel, MD and Rajeeva Ranga, MD from Kaiser Permanente Oakland; Yile Ding, MD from California Pacific Medical Center; Soma Wali, MD from Los Angeles County/ OliveView UCLA Medical Center; Anshu Abhat, MD, MPH from the LA BioMed Institute at Los Angeles County/ Harbor-UCLA Medical Center; Steve Tringali, MD from Community Regional Medical Center Fresno; and Dan Dworsky, MD from Scripps Green Hospital for their site leadership and participation with the study.

Disclosures

Dr. Gupta is the Director of the Teaching Value in Healthcare Learning Network at Costs of Care. Dr. Moriates receives royalties from McGraw Hill for the textbook “Understanding Value-based Healthcare” outside of the submitted work and is the Director of Implementation at Costs of Care.

The Centers of Medicare and Medicaid Services (CMS) has introduced new payment models that tie quality and value incentives to 90% of fee-for-service payments and provide 50% of Medicare payments through alternative payment models.¹ The push toward value comes after productivity-based physician reimbursement (ie, fee for service) has been associated with poor quality care, including delayed diagnoses, complications, readmissions, increased length of stay, and high costs of care.^2-5 The method of physician payment is widely believed to affect clinical behavior by incentivizing doing more, coding for more, and billing for more.^6-7 Although payment systems may be used to achieve policy objectives,⁸ little is known about the association of different payment systems with the culture of delivering value-based care among frontline clinicians.

Culture is defined as a system of shared assumptions, values, beliefs, and norms within an environment and has a powerful role in shaping clinician practice patterns.^9-12 The culture within medicine currently contributes to the overuse of resources,^11,13 and a culture for improvement is correlated with clinical outcomes. A systematic review found a consistent association between positive organization culture and improved outcomes including mortality.¹⁴ Across health systems, institutions with high scores on patient safety culture surveys have shown improvements in clinical behaviors and patient outcomes.^15-18

In this study, we aim to describe high-value care culture among internal medicine hospitalists across diverse hospitals and evaluate the relationship between physician reimbursement and high-value care culture.

Study Design

This study is an observational, cross-sectional survey-based study of hospitalists from 12 hospitals in California between January and June 2016.

Study Population

A total of 12 hospitals with hospitalist programs in California were chosen to represent three types of hospitals (ie, four university, four community, and four safety net). Safety-net hospitals, which traditionally serve low-income and medically and socially vulnerable patients were defined as those in the top quartile (ie, greater than 0.5) of their Disproportionate Share Index (DSH), which measures Medicaid patient load.^19-20

To select hospitals with varying value-based care performance, we stratified using CMS value-based purchasing (VBP) scores from fiscal year 2015; these scores have been used to adjust reimbursement for just over 3,000 hospitals in the VBP program of CMS.^22,23 CMS calculates the VBP total performance score as a composite of four domains: (1) clinical processes of care (20% of total performance); (2) patient satisfaction (30%); (3) patient outcomes, including mortality and complications (30%); and (4) cost defined by Medicare payment per beneficiary (20%).²¹ Established quality measures are based on data reported by participating hospitals and chart abstraction during 2011-2014.²² Although other clinical measures of care intensity have been used as proxies of value-based care,^23,24 we used the measure of value that has been publically reported by the CMS VBP given its wide use and effects on reimbursements for 80% of hospitals in the CMS VBP program in 2015.²⁵ We obtained institution-level data from the CMS VBP Program and Hospital Compare files. Each of the three types of hospitals represented institutions with low, middle, and high VBP performance (split in tertiles) as reported by the CMS VBP program. To increase the number of participants in tertiles with fewer hospitalists, a fourth hospital was selected for each hospital type.

We excluded individual hospitalists who primarily identified as working in subspecialty divisions and those who spent less than eight weeks during the last year providing direct patient care on inpatient internal medicine services at the studied institution.

Measurement

Hospitalists were asked to complete the High-Value Care Culture Survey (HVCCS^TM), which measures the culture of value-based decision making among frontline clinicians.²⁶ Similar to other validated surveys for the assessment of patient safety culture,^27,28 the HVCCS can be used to identify target areas for improvement. The survey includes four domains: (1) leadership and health system messaging, (2) data transparency and access, (3) comfort with cost conversations, and (4) blame-free environment. This tool was developed by using a two-phase national modified Delphi process. It was evaluated at two academic centers to complete factor analysis and assess internal consistency, reliability, and validity among internal medicine hospitalists and residents. Validation included estimating product-moment correlation of overall HVCCS scores and domain scores with the CMS institutional VBP scores. HVCCS scores are standardized to a 0-100 point scale for each of the four domains and are then averaged to obtain an overall score.²⁶

In the survey, value was defined as the quality of care provided to patients in relation to the costs required to deliver that care, and high-value care was defined as care that tried to maximize quality while minimizing costs. Quality was defined as the degree to which health services increased the likelihood of desired health outcomes that are safe, effective, patient centered, timely, equitable, and consistent with current professional knowledge. Cost was defined as the negative financial, physical, and emotional effects on patients and the health system.²⁶

Data Analysis

We described the overall institutional mean high-value care culture and domain scores measured by the HVCCS, hospitalist demographics and training experiences, and hospital characteristics. We also described individual survey items. Descriptive statistics were stratified and compared on the basis of hospital type (ie, safety net, community, or university). We assessed the relationship between the clinician perception of reimbursement structure within their divisions and individually reported high-value care culture scores using bivariate and multilevel linear regression. We hypothesized that compared with hospitalists who were paid with salaries or wages, those who reported reimbursement with productivity adjustments may report lower HVCCS scores and those who reported reimbursement with quality or value adjustments may report higher HVCCS scores. We adjusted for physician- and hospital-level characteristics, including age, gender, and training track, and considered hospital type and size as random effects.

This study was approved by the Institutional Review Board at all 12 sites. All analyses were conducted using STATA^® 13.0 (College Station, Texas).

Hospitalist Characteristics

A total of 255 (68.9%, 255/370) hospitalists across all sites completed the survey. Of these respondents, 135 were female (50.6%). On average, hospitalists were 39 years of age (SD 6.8), trained in categorical tracks (221; 86.7%), and had previously trained for 14.3 months at a safety-net hospital (SD 14.2). In total, 166 hospitalists (65.1%) reported being paid with salary or wages, 77 (30.2%) with salary plus productivity adjustments, and 12 (4.7%) with salary plus quality or value adjustments. Moreover, 123 (48.6%) hospitalists agreed that funding for their group depended on the volume of services they delivered. Community-based hospitalists reported higher rates of reimbursement with salary plus productivity (47; 32.0%) compared with their counterparts from university-based (24; 28.2%) and safety-net based programs (6; 26.1%). Among the three different hospital types, significant differences exist in hospitalist mean age (P < .001), gender (P = .01), and the number of months training in a safety-net hospital (P = .02; Table 1).

Hospital Characteristics

Of the 12 study sites, four from each type of hospital (ie, safety-net based, community based, and university based) and four representing each value-based purchasing performance tertile (ie, high, middle, and low) were included. Eleven (91.7%) sites were located in urban areas with an average DSH index of 0.40 (SD 0.23), case mix index of 1.97 (SD 0.28), and bed size of 435.5 (SD 146.0; Table 1).

In multilevel regression modeling across all 12 sites, hospitalists from community-based hospitalist programs reported lower mean HVCCS scores (β = −4.4, 95% CI −8.1 to −0.7; Table 2) than those from other hospital types.

High-Value Care Culture Survey Scores

The mean HVCCS score was 50.2 (SD 13.6), and mean domain scores across all sites were 65.4 (SD 15.6) for leadership and health system messaging, 32.4 (SD 22.8) for data transparency and access, 52.1 (SD 19.7) for comfort with cost conversations, and 50.7 (SD 21.4) for blame-free environment (Table 1). For the majority (two-thirds) of individual HVCCS items, more than 30% of hospitalists across all sites agreed or strongly agreed that components of a low-value care culture exist within their institutions. For example, over 80% of hospitalists reported low transparency and limited access to data (see Appendix I for complete survey responses).

Hospitalists reported different HVCCS domains as strengths or weaknesses within their institutions in accordance with hospital type. Compared with university-based and safety-net-based hospitalists, community-based hospitalists reported lower scores in having a blame-free environment (466, SD 21.8). Nearly 50% reported that the clinicians’ fear of legal repercussions affects their frequency of ordering unneeded tests or procedures, and 30% reported that individual clinicians are blamed for complications. Nearly 40% reported that clinicians are uncomfortable discussing the costs of tests or treatments with patients and reported that clinicians do not feel that physicians should discuss costs with patients. Notably, community-based hospitalists uniquely differed in how they reported components of leadership and health system messaging. Over 60% reported a work climate or role modeling supportive of delivering quality care at lower costs. Only 48%, however, reported success seen from implemented efforts, and 45% reported weighing costs in clinical decision making (Table 1, Appendix I).

University-based hospitalists had significantly higher scores in leadership and health system messaging (67.4, SD 16.9) than community-based and safety-net-based hospitalists. They reported that their institutions consider their suggestions to improve quality care at low cost (75%), openly discuss ways to deliver this care (64%), and are actively implementing projects (73%). However, only 54% reported seeing success from implemented high-value care efforts (Table 1, Appendix I).

Safety-net hospitalists reported lower scores in leadership and health system messaging (56.8, SD 10.5) than university-based and community-based hospitalists. Few hospitalists reported a work climate (26%) or role modeling (30%) that is supportive of delivering quality care at low costs, openly discusses ways to deliver this care (35%), encourages frontline clinicians to pursue improvement projects (57%), or actively implements projects (26%). They also reported higher scores in the blame-free environment domain (59.8, SD 22.3; Table 1; Appendix 1).

Productivity Adjustments and High-Value Care Culture

In multilevel regression modeling, hospitalists who reported reimbursement with salary plus productivity adjustments had a lower mean HVCCS score (β = −6.2, 95% CI −9.9 to –2.5) than those who reported payment with salary or wages alone. Further multilevel regression modeling for each HVCCS domain revealed that hospitalists who reported reimbursement with salary plus productivity adjustments had lower scores in the leadership and health system messaging domain (β = −4.9, 95% CI −9.3 to −0.6) and data transparency and access domain (β = −10.7, 95% CI −16.7 to −4.6). No statistically significant difference was found between hospitalists who reported reimbursement with quality or value adjustments.

Understanding the drivers that are associated with a high-value care culture is necessary as payment models for hospitals transition from volume-based to value-based care. In this study, we found a meaningful association (β = −6.2) between clinician reimbursement schemes and measures of high-value care culture. A six-point change in the HVCCS score would correspond with a hospital moving from the top quartile to the median, which represents a significant change in performance. The relationship between clinician reimbursement schemes and high-value care culture may be a bidirectional relationship. Fee for service, the predominant payment scheme, places pressure on clinicians to maximize volume, focus on billing, and provide reactive care.^7,29 Conversely, payment schemes that avoid these incentives (ie, salary, wages, and adjustments for quality or value), especially if incentives are felt by frontline clinicians, may better align with goals for long-term health outcomes for patient populations and reduce excess visits and services.^2-6,8,30-34 At the same time, hospitals with a strong high-value care culture may be more likely to introduce shared savings programs and alternative payment models than those without. Through these decisions, the leadership can play an important role in creating an environment for change.³⁴ Similar to the study sites, hospitals in California have a higher percentage of risk-based payments than hospitals in other states (>22%)³⁵ and may also provide incentives to promote a high-value care culture or affect local physician compensation models.

Hospitals have options in how they choose to pay their clinicians, and these decisions may have downstream effects, such as building or eroding high-value care culture among clinicians or staff. A dose-response relationship between physician compensation models and value culture is plausible (salary with productivity < salary only < salary with value incentive). However, we did not find a statistically significant difference for salary with value incentive. This result may be attributed to the relatively small sample size in this study.

Hospitals can also improve their internal processes, organizational structure, and align their institutional payment contracts with those that emphasize value over fee-for-service-based incentives to increase value in care delivery.³⁶ The operation of hospitals is challenging when competing payment incentives are used at the same time,⁷ and leadership will likely achieve more success in improving a high-value care culture and value performance when all efforts, including clinician and institutional payment, are aligned.^37-38

Enduring large systems redesign will require directing attention to local organizational culture. For the majority of individual HVCCS items, 30% or more hospitalists across all sites agreed or strongly agreed that components of low-value care culture exist within their institutions. This response demonstrates a lack of focus on culture to address high-value care improvement among the study sites. Division and program leaders can begin measuring culture within their groups to develop new interventions that target culture change and improve value.³⁴ No single panacea exists for the value improvement of hospitalist programs in California across all hospital types and sites.

Unique trends, however, emerge among each hospital type that could direct future improvements. In addition to all sites requiring increased transparency and access to data, community-based hospitalists identified the need for improvement in the creation of a blame-free environment, comfort with cost conversations, and aspects of leadership and health system messaging. While a high proportion of these hospitalists reported a work culture and role modeling that support the delivery of quality care at low costs, opportunities to create open discussion and frontline involvement in improvement efforts, weigh costs into clinical decision making, and cost conversations with patients exist. We hypothesize that these opportunities exist because community-based hospitals create infrastructure and technology to drive improvement that is often unseen by frontline providers. University-based hospitalists performed higher on three of the four domains compared with their counterparts but may have opportunities to promote a blame-free environment. A great proportion of these hospitalists reported the occurrence of open discussion and active projects within their institutions but also identified opportunities for the improvement of project implementation. Safety-net hospitalists reported the need to improve leadership and health system messaging across most domain items. Further study is required to evaluate reasons for safety-net hospitalists’ responses. We hypothesize that these responses may be related to having limited institutional resources to provide data and coordinated care and different institutional payment models. Each of these sites could identify trends in specific questions identified by the HVCCS for improvement in the high-value care culture.²⁵

Our study evaluated 12 hospitalist programs in California that represent hospitals of different sizes and institutional VBP performance. A large multisite study that evaluates HVCCS across other specialties and disciplines in medicine, all regions of the country, and ambulatory care settings may be conducted in the future. Community-based hospitalist programs also reported low mean HVCCS scores, and further studies could better understand this relationship.

The limitations of the study include its small subgroup sample size and the lack of a gold standard for the measurement of high-value care. As expected, hospitalist groups among safety-net hospitals in California are small, and we may have been underpowered to determine some correlations presented by safety-net sites when stratifying by hospital type. Other correlations also may have been limited by sample size, including differences in HVCCS scores based on reimbursement and hospital type and the correlation between a blame-free environment and reimbursement type. Additionally, the field lacks a gold standard for the measurement of high-value care to help stratify institutional value performance for site selection. The VBP measure presents policy implications and is currently the best available measure with recent value data for over 3,000 hospitals nationally and representing various types of hospitals. This study is also cross-sectional and may benefit from the further evaluation of organizational culture over time and across other settings.

The HVCCS can identify clear targets for improvement and has been evaluated among internal medicine hospitalists. Hospitalists who are paid partly based on productivity reported low measures of high-value care culture at their institutions. As the nation moves toward increasingly value-based payment models, hospitals can strive to improve their understanding of their individual culture for value and begin addressing gaps.

Acknowledgments

The authors wish to thank Michael Lazarus, MD from the University of California Los Angeles; Robert Wachter, MD, James Harrison, PhD; Victoria Valencia, MPH from Dell Medical School at the University of Texas at Austin; Mithu Molla, MD from University of California Davis; Gregory Seymann, MD from the University of California San Diego; Bindu Swaroop, MD and Alpesh Amin, MD from University of California Irvine; Jessica Murphy, DO and Danny Sam, MD from Kaiser Permanente Santa Clara; Thomas Baudendistel, MD and Rajeeva Ranga, MD from Kaiser Permanente Oakland; Yile Ding, MD from California Pacific Medical Center; Soma Wali, MD from Los Angeles County/ OliveView UCLA Medical Center; Anshu Abhat, MD, MPH from the LA BioMed Institute at Los Angeles County/ Harbor-UCLA Medical Center; Steve Tringali, MD from Community Regional Medical Center Fresno; and Dan Dworsky, MD from Scripps Green Hospital for their site leadership and participation with the study.

Disclosures

Dr. Gupta is the Director of the Teaching Value in Healthcare Learning Network at Costs of Care. Dr. Moriates receives royalties from McGraw Hill for the textbook “Understanding Value-based Healthcare” outside of the submitted work and is the Director of Implementation at Costs of Care.

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

Patient flow throughout the hospital has been shown to be adversely affected by discharge delays.¹ When hospitals are operating at peak capacity, these delays impact throughput, length of stay (LOS), and cost of care and block patients from the emergency department (ED), postanesthesia recovery unit (PACU), or home awaiting inpatient beds.^2-5 As patients wait in locations not ideal for inpatient care, they may suffer from adverse events and poor satisfaction.^3,6 Several studies have analyzed discharge timing as it relates to ED boarding of admitted patients and demonstrated that early discharges (EDCs) can impact boarding times.^7-9 A number of recent improvement efforts directed at moving discharges earlier in the day have been published.^10-15 However, these improvements are often targeted at specific units or teams within a larger hospital setting and only one is in the pediatric setting.

Lucile Packard Children’s Hospital Stanford (LPCHS) is a 311-bed quaternary care academic women and children’s hospital in Northern California. As our organization expanded, the demand for hospital beds often exceeded capacity. The challenge of overall demand was regularly compounded by a mismatch in bed availability timing – bed demand is early in the day and bed availability is later. This mismatch results in delays for admitted patients waiting in the ED and PACU. Organization leaders identified increasing early discharges (EDCs) as one initiative to contribute to improved patient flow.

Our organization aimed to increase the number of discharges before 11 am across the acute care units from an average of 8% in the 17 months prior to May 2015 to 25% by December 2016. Based on the average number and timing of planned admissions, they hypothesized that 25% of EDCs would decrease ED and PACU wait times.

Setting

We focused our EDC interventions on the 87 acute care beds at LPCHS. All patients discharged from these beds were included in the study. We excluded patients discharged from intensive care, maternity, and nursery. Acute care includes five units, one focused on hematology/oncology (Unit A), one focused on cardiology (Unit B), and the others with a surgical and medical pediatric patient mix (Units C, D, and E). Although physician teams have primary units, due to unit size, patients on teams other than cardiology and hematology/oncology are often spread across multiple units wherever there is a bed (including Units A and B). Most of the frontline care physicians are residents supervised by attendings; however, a minority of patients are cared for by nurse practitioners (NPs) or physician assistants (PAs).

Improvement Team

In early 2015, we formed a multidisciplinary group inclusive of a case manager, frontline nurses, nurse management, pediatric residents, and hospitalist physicians with support from performance improvement. We periodically included physician leaders from other specialties to help initiate changes within their own clinical areas. Our group used Lean A3 thinking¹⁶ to gather information about the current state, formulate the problem statement, analyze the problem, and consider interventions implemented in three Plan–Do-Check-Act (PDCA) cycles. The A3 is a structured tool to analyze problems before jumping to solutions and communicate with stakeholders. We interviewed leaders, nurses, residents, case managers, etc. and observed work processes around discharge. We met weekly to follow data, assess results of interventions, and problem solve.

Barriers and Interventions

The first barrier we identified and addressed was poor identification and shared team mental model of potential EDC patients and lack of preparation when an EDC was identified. In intervention one starting May 2015, charge nurses on Units C, D, and E were each asked to identify one EDC for the following day. The identified patient was discussed at the previously existing afternoon daily unit huddle¹⁷ attended by nurse management, case management, and hospitalist leaders. Following the huddle, the resident, NP, or PA responsible for the patient was paged regarding the EDC plan and tasked with medication reconciliation and discharge paperwork. Others were asked to address their specific area of patient care for discharge (eg, case manager–supplies, nursing–education). The patient was identified on the unit white board with a yellow magnet (use of a visual control¹⁸), so that all would be aware of the EDC. An e-mail was sent to case management, nurse leaders, and patient placement coordinators regarding the planned EDCs. Finally, the EDCs were discussed during regularly scheduled huddles throughout the evening and into the next day.¹⁷

Despite this first intervention, we noted that progress toward increased EDCs was slow. Thus, we spent approximately seven days (spread over one month) further observing the work processes.¹⁹ Over five days, we asked each unit’s charge nurse every hour which patients were waiting to be discharged and the primary reason for waiting. From this information, we created a pareto chart demonstrating that rounds were the highest contributor to waiting (Appendix A). Thus, our second intervention was a daily physician morning huddle that the four nonsurgical physician teams (excluding cardiology, hematology/oncology) implemented one team at a time between November 2015 and February 2016. At the huddle, previously identified EDCs (located on any of the five units) were confirmed and preparatory work was completed (inclusive of the discharge order) before rounds. Further, the attending and resident physicians were to see the patient before or at the start of rounds.

Our working group still observed slow EDC improvement and sought feedback from all providers. EDC was described as “extra” work, apart from routine practices and culture. In addition, our interventions had not addressed most discharges on Units A and B. Consequently, our third intervention in February 2016 aimed to recognize and incentivize teams, units, and individuals for EDC successes. Units and/or physician teams that met 25% of EDCs the previous week were acknowledged through hospital-wide screensavers and certificates of appreciation signed by the Chief Nursing Officer. Units and/or physician teams that met 25% of EDC the previous month were acknowledged with a trophy. Residents received coffee cards for each EDC (though not without controversy among the improvement group as we acknowledged that all providers contributed to EDCs). Finally, weekly, we shared an EDC dashboard displaying unit, team, and organization performance at the hospital-wide leader huddle. We also e-mailed the dashboard regularly to division chiefs, medical directors, and nursing leaders.

Measures

Our primary outcome was percentage of EDCs (based on the time the patient left the room) across acute care. Secondary outcome measures were median wait times for an inpatient bed from the ED (time bed requested to the time patient left the ED) and the average PACU wait time (time the patient is ready to leave the PACU to time the patient left the PACU) per admitted patient. We also assessed balancing measures, including discharge satisfaction, seven-day readmission rates, and LOS. We obtained the mean discharge satisfaction score from the organization’s Press Ganey survey results across acute care (the three discharge questions’ mean – “degree … you felt ready to have your child discharged,” “speed of discharge process …,” and “instructions… to care for your child…”). We obtained seven-day readmission rates from acute care discharges using the hospital’s regularly reported data. We assessed patient characteristics, including sex, age, case mix index (CMI; >2 vs <2), insurance type (nongovernment vs government), day of discharge (weekend vs weekday), and LOS from those patients categorized as inpatients. Complete patient characteristics were not available for observation (InterQual^® criteria) status patients.

Analysis

We used descriptive statistics to describe the inpatient population characteristics by analyzing differences when EDC did and did not occur using chi-square and the Mann–Whitney U tests. Patients with missing data were removed from analyses that incorporated patient factors.

To assess our primary outcome, we used an interrupted time series analysis assessing the percentage of EDC in the total population before any intervention (May 2015) and after the last intervention (March 2016). We used the Durbin–Watson statistic to assess autocorrelation of errors in our regression models. As we had only patient characteristics for the inpatient population, we repeated the analysis including only inpatients and accounting for patient factors significantly associated with EDC.

As units and physician teams had differential exposure to the interventions, we performed a subanalysis (using interrupted time series) creating groups based on the combination of interventions to which a patient’s discharge was exposed (based on unit and physician team at discharge). Patient discharges from group 1 (medical patients on Units C, D, and E) were exposed to all three interventions, group 2 patient discharges (medical patients on Units A and B) were exposed to interventions 2 and 3, group 3 (cardiology, hematology/oncology, surgical patients on Units A and B) were exposed to intervention 3, and group 4 (surgical, cardiology, hematology/oncology patients on Units C, D, and E) were exposed to interventions 1 and 3 (Figure 1). Interrupted time series models were fit using the R Statistical Software Package.²⁰

There were 16,175 discharges on acute care from January 2014 through December 2016. Across all acute care units, EDCs increased from an average of 8.8% before the start of interventions (May 2015) to 15.8% after all interventions (February 2016). From the estimated trend in the preintervention period, there was a jump of 3.9% to the start of the postintervention trend (P = .02; Figure 2). Furthermore, there was an increase of 0.48% (95% CI 0.15-0.82%; P < .01) per month in the trend of the slope between the pre- and postintervention. The autocorrelation function and the Durbin–Watson test did not show evidence of autocorrelation (P = .85). Lack of evidence for autocorrelation in this and each of our subsequent fitted models led to excluding an autocorrelation parameter from our models.

From 16,175 discharges, 1,764 (11%) were assigned to observation status. Among inpatients (14,411), patients with missing values (CMI, insurance status) were also excluded (n = 66, 0.5%). Among the remaining 14,345 inpatients, 54% were males, 50% were government-insured, and 1,645 (11.5%) were discharged early. The average age was 8.5 years, the average LOS was seven days, and the median CMI was 2.2. Children who were younger, had shorter LOS, CMI <2, and nongovernment insurance were more likely to be discharged early (P < .01 for all). For each of these variables, F-tests were performed to determine whether there was a statistically significant reduction in variation by adding the variable to our initial model. None of the variables alone or in combination led to a statistically significant reduction in variation. Including these factors in the interrupted time series did not change the significance of the results (jump at postintervention start 3.6%, 95% CI 0.7%-7.2%; P = .02, slope increased by 0.59% per month, 95% CI 0.29-0.89%; P < .01).

In the subgroup analysis, we did not account for patient factors as they did not change the results in the analysis of total population. Though each group had a greater percentage of EDCs in the postintervention period, the changes in slopes and jumps were primarily nonsignificant (Figure 3). Only the change in slope in group 4 was significant (1.1%, 95% CI 0.3-1.9%; P = .01).

The percentage of patients who left the hospital prior to 11 am significantly improved after a number of interventions aimed at emphasizing EDC and discharge task completion earlier within the hospital stay. Our EDC improvement was associated with improved ED and PACU wait times without negatively impacting discharge satisfaction, seven-day readmissions, or LOS.

It is difficult to compare our EDC improvements to those of previous studies, as we are unaware of published data on pediatric EDC efforts across an entire hospital. In addition, studies have reported discharges prior to different times in the day (noon, 1 pm, etc).^{12, 13} Our interventions were similar to those of Wertheimer et al.,¹¹ including the use of interdisciplinary rounds, identification of potential EDCs the afternoon before discharge, and “reward and recognition.” Wertheimer also sent an e-mail about EDCs to a multidisciplinary group, which was then updated as conditions changed. Unlike Wertheimer, we did not include physicians in our e-mail due to the large number and frequently changing physician teams. Our EDC rate prior to 11 am was lower than their achieved rate of 35% prior to 12 pm. When we assessed our discharges using 12 pm, our rate was still lower (22%-28%), but a direct comparison was complicated by different patient populations. Still, our study adds to the evidence that interdisciplinary rounds and reward and recognition lead to earlier discharge. In addition, this study builds upon Wertheimer’s results as although they later assessed the timing of ED admissions as a result of their EDC improvements, they did not directly assess inpatient bed wait times as we did in our study.¹⁴

As providers of all types were aware of the constant push for beds due to canceled surgeries, delayed admissions and intensive care transfers, and the inability to accept admission, it is difficult to compare the subgroups directly. Furthermore, although physician teams and units are distinct, individuals (nurses, case managers, trainees) may rotate through different units and teams and we cannot account for individual influences on EDCs depending on exposure to interventions over time. Although all groups improved, the improvement in slope in group 4 (exposed to interventions 1 and 3) was the only significant change. As group 4 contained a large number of surgical patients who often have more predictable hospital stays, perhaps this group was more responsive to the interventions.

Our EDC improvements were associated with a decrease in ED and PACU bed wait times. Importantly, we did not address potential confounding factors impacting these times such as total hospital admission volumes, ED and PACU patient complexity, and distribution of ED and PACU admission requests throughout the day. Modeling has suggested that EDCs could also improve ED flow,⁷ but studies implementing EDC have not necessarily assessed this outcome.^10-15 One study retrospectively evaluated ED boarding times in the context of an EDC improvement effort and found a decrease in boarding times.²¹ This decrease is important as ED boarders may be at a higher risk for adverse events, a longer LOS, and more readmissions.^3,7 Less is known about prolonged PACU wait times; however, studies have reported delays in receiving patients from the operating room (OR), which could presumably impact timeliness of other scheduled procedures and patient satisfaction.^22-24 It is worth noting that OR holds as a result of PACU backups happened more frequently at our institution before our EDC work.

Our limitations include that individual providers in the various groups were not completely blind to the interventions and groups often comprised distinct patient populations. Second, LPCHS has a high CMI and LOS relative to most other children’s hospitals, complicating comparison with patient populations at other children’s hospitals. In addition, our work was done at this single institution. However, since a higher CMI was associated with a lower probability of EDC, hospitals with a lower CMI may have a greater opportunity for EDC improvements. Third, hospital systems are more impacted by low EDCs when operating at high occupancy (as we were at LPCHS); thus, improvements in ED and PACU wait times for inpatient beds might not be noted for hospitals operating with a >10% inventory of beds.²⁵ Importantly, our hospital had multiple daily management structures in place, which we harnessed for our interventions, and better patient flow was a key hospital initiative garnering improvement of resources. Hospitals without these resources may have more difficulty implementing similar interventions. Finally, other work to improve patient flow was concurrently implemented, including matching numbers of scheduled OR admissions with anticipated capacity, which probably also contributed to the decrease in ED and PACU wait times.

We found that a multimodal intervention was associated with more EDCs and improved ED and PACU bed wait times. We observed no impact on discharge satisfaction or readmissions. Our EDC improvement efforts may guide institutions operating at high capacity and aiming to improve EDCs to improve patient flow.

Acknowledgments

The authors would like to acknowledge all those engaged in the early discharge work at LPCHS. They would like to particularly acknowledge Ava Rezvani for her engagement and work in helping to implement the interventions.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose. The authors have no financial relationships relevant to this article to disclose.

Funding

This project was accomplished without specific funding. Funding for incentives was provided by the Lucile Packard Children’s Hospital Stanford.

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

Many hospitals and emergency departments (EDs) face challenges posed by overcrowding and hospital throughput. Slow ED throughput has been associated with worse patient outcomes.¹ One strategy increasingly employed to improve hospital throughput is to increase the rate of inpatient discharges earlier in the day, which is often defined as discharges before noon (DCBNs). The hypothesis behind DCBN is that earlier hospital discharges will allow for earlier ED admissions and thus mitigate ED overcrowding while optimizing inpatient hospital flow. Previous quality improvement efforts to increase the percentage of DCBNs have been successfully implemented. For example, Wertheimer et al. implemented a process for earlier discharges and reported a 27-percentage point (11% to 38%) increase in DCBN on general medicine units.² In a recent survey among leaders in hospital medicine programs, a majority reported early discharge as an important institutional goal.³

Studies of the effectiveness of DCBN initiatives on improving throughput and shortening length of stay (LOS) in adult patients have had mixed results. Computer modeling has supported the idea that earlier inpatient discharges would shorten ED patient boarding time.⁴Wertheimer et al. performed a retrospective analysis of a DCBN intervention on two inpatient medicine units and reported an association between slightly shorter observed versus expected inpatient LOS² and earlier arrival time of inpatient admissions from the ED.⁵ In contrast, Rajkomar et al. conducted a retrospective analysis of the association of DCBN and LOS in a predominantly surgical services population and reported a longer LOS for DCBN patients when controlling for patient characteristics and comorbidities.⁶ These mixed findings have led some authors to question the value of DCBN initiatives and created concern for the potential of prolonged patient hospitalizations as a result of institutional DCBN goals.⁷ The impact of DCBN in pediatric patients is much less studied.

A question of interest for hospitals is if DCBN is a good indicator of shorter LOS, or is DCBN an arbitrary indicator, as morning discharges might just be the result of a delayed discharge of a patient ready for discharge the prior afternoon/evening. Our study objectives were: (1) to determine whether DCBN is associated with a shorter LOS in a pediatric population at an academic medical center, and (2) to examine separately this association in medical and surgical patients given the different provider workflow and patient clinical characteristics in those groups.

Patients and Settings

This retrospective cohort analysis included pediatric medical and surgical inpatient admissions from a single academic medical center from May 2014 to April 2017. The University of North Carolina (UNC) Children’s Hospital is a 175-bed tertiary care ‘hospital within a hospital’ in an academic setting with multiple residencies. UNC Children’s Hospital contains three units providing inpatient pediatric care. Each unit occupies a floor of the Children’s hospital and are loosely regionalized, as follows: (1) Unit 7 is focused on surgical patients; (2) Unit 6 is focused on general, neurologic, and renal patients; and (3) Unit 5 is focused on hematology/oncology and pulmonary patients. Extending the entire study period, Unit 6 initiated a quality improvement effort to discharge patients earlier in the day, specifically before 1 pm; however, the initiative did not extend beyond this one unit.

We included patients 21 years or younger with an inpatient admission to any of the following pediatric medical or surgical services: cardiac surgery, cardiology, endocrinology, gastroenterology, general services, hematology/oncology, nephrology, orthopedics, otolaryngology, plastic surgery, pulmonology, and urology. Patients whose stay did not extend beyond one midnight were excluded because discharge time of day for these short stays was strongly related to the time of admission. We also excluded patients whose stay extended beyond two standard deviations of the average LOS for the discharge service under the assumption that these patients represented atypical circumstances. Finally, we excluded patients who died or left against medical advice. A consortium diagram of all exclusion criteria can be found in Supplemental Figure 1. Discharge data were extracted from the Carolina Database Warehouse, a data repository of the University of North Carolina Health System. The University of North Carolina Institutional Review Board reviewed and approved this study (IRB 17-0500).

Measures

The outcome of interest was LOS, defined as discharge date and time minus admission date and time, and thus a continuous measure of time in the hospital rather than a number of midnights. Rajkomar et al. used the same definition of LOS.⁶ The independent variable of interest was whether the discharge occurred before noon. Because discharges between midnight and 8:00 am are likely unplanned and not attributable to any particular workflow, we followed a similar definition of DCBN used by Rajkomer et al. and defined DCBN as a patient leaving between 8:00 am and 11:59 am (pre-8:00 am discharges accounted for less than one half of one percent of discharges).⁶

All model covariates were collected at the patient level (Table 1), including demographic characteristics such as age, sex, race, and ethnicity. We also collected covariates describing the patient’s hospitalization as follows: (1) whether the patient was discharged on a weekend versus weekday; (2) hospital service at time of discharge (dichotomized to a surgical or medical service); (3) whether the patient was discharged from the unit that had a DCBN quality improvement initiative; (4) discharge disposition (home with self-care, assisted living or home health, or other); (5) insurance type during hospitalization (commercial, Medicaid, no insurance, or other); and (6) case mix index (CMI), a measure of hospital resource intensity of a patient’s principal diagnosis. Covariate selection was made on the basis of a priori knowledge of causal pathways.⁸

Statistical Analysis

Student t tests and χ² statistics were used to compare baseline characteristics of hospitalizations of patients DCBN and after noon. We used ordinary least squares (OLS) regression models to assess the association between DCBN and LOS. Because DCBN may be correlated with patient characteristics, we used propensity score weighted models. Propensity scores were estimated using a logistic regression predicting DCBN using the variables given in Table 1 (excluding the outcome variable LOS). To estimate the average treatment effect on the entire sample for each model, we weighted each observation by the inverse-probability of treatment as per recent propensity score methods detailed by Garrido et al.⁹ In the inverse-probability weighted models, we clustered on attending physician to adjust for the autocorrelation caused by unobservable similarities of discharges by the same attending. We tested for multicollinearity using the variance inflation factor (VIF). To test our secondary hypothesis that there was a difference in the relationship between DCBN and LOS based on service type (medical versus surgical), we tested if the service type moderated any of the coefficients using a joint Wald test on the 10 coefficients interacted with the service type.

For our sensitivity analysis, we reran all surgical and medical discharges models changing the LOS outlier exclusion criteria to greater than three and then four standard deviations. Statistical modeling and analysis were completed using Stata version 14 (StataCorp, College Station, Texas).

Our study sample comprised 8,226 pediatric hospitalizations with a LOS mean of 5.10 and a median of 3.91 days respectively (range, 1.25-32.83 days). There were 1,531 (18.6%) DCBNs. Compared to those discharged after noon, patients with DCBN had a higher probability of being surgical patients, having commercial insurance, discharge home with self-care, discharge on the weekend, and discharge from a nonquality improvement unit (Table 1). Patients with DCBN were also more likely to be white, non-Hispanic, and male.

Our propensity score weighted ordinary least score (OLS) LOS regression results are presented in Table 2. In the bivariate analysis, DCBN was associated with an average 0.40 day, or roughly 10 hours, shorter LOS (P < .001). In the multivariate model of all discharges, we found that DCBN was associated with a mean of 0.27 day (P = .010) shorter LOS when compared to discharge in the afternoon when controlling for age, race, ethnicity, weekend discharge, discharge from quality improvement unit, discharge service type, CMI, insurance type, and discharge disposition. In the multivariate analysis, weekend discharge, surgical discharge, and discharge disposition of home with self-care, compared to assisted living or home health were associated with shorter LOS.

In our study of over 8,000 pediatric discharges during a three-year period, DCBN was associated with shorter LOS for medical pediatric patients, but this finding was not consistent for surgical patients. Among medical discharges, DCBN was associated with shorter LOS, an effect robust enough to include or exclude outliers (for LOS, outliers are an important subset because there are always, in general, a few patients with very long lengths of stay). Discharge before noon showed no association with LOS for surgical patients unless we included outlier values.

The differential effect of DCBN on LOS in surgical and medical discharges suggests that the relationship between DCBN and LOS may be related to provider team workflow. For example, surgical teams may tend to round one time per day early in the morning before spending the entire day in the operating room, and thus completing more early morning discharge orders compared to medical teams. However, if a patient on a surgical service is not ready for discharge first thing in the morning, the patient may be more likely to wait until the following morning for a discharge order. On medical services, physician schedules may allow for more flexibility for rounding and responding with a discharge order when a patient becomes ready; however, medical services may round later in the day compared to surgeons and for a longer period of time, delaying discharges beyond noon that could have been made earlier. Another possibility, given UNC pediatric services are loosely regionalized with surgical patients concentrated more in one unit, is that unit-level differences in how staff processed discharges could have contributed to the difference observed between medical and surgical patients, particularly as there was a unit-level quality improvement effort for decreasing discharge time on one of two medical floors. However, we analyzed for differences based on the discharging unit and found no association. The influence of outliers on the association between DCBN and LOS increases also suggests that this group of children who have extremely long hospital stays might need further exploration.

Our study has some similar and some contrasting results with prior studies in adult patients. Our findings support the modeling literature that suggests DCBN may improve discharge efficiency by shortening patient LOS for some discharges.⁴ These findings contrast with Rajkomar et al., who reported that DCBN was associated with a longer LOS in adult patients.⁶ The contrasting findings could be due to differences in pediatric versus adult patients. Additionally, the population Rajkomar et al. studied was predominantly surgical patients, whose discharges may differ from medical patients’ in many aspects. Another possible explanation is that the Rajkomar et al. study was performed in a setting with clearly set institutional targets for DCBN, whereas, our institution lacked any hospital-wide DCBN initiatives or standards to which providers were held accountable. Some authors have argued setting DCBN as a measure of hospital quality perhaps creates the unintended consequence of providers holding potential afternoon or evening discharges until the next day so that they can be DCBN.^7,10 In that scenario, perhaps there would be a relationship between DCBN and longer LOS compared to patients who are reevaluated in the afternoon or evening and discharged. We did not find evidence of these effects in our analysis, however, understanding the potential for this is important when designing quality improvement efforts aimed at increasing discharge efficiency.

While shorter LOS can be an indicator of high-value care, the relationship between LOS, DCBN, and efficiency of discharge processes remains unclear. Prior studies have found evidence that multidisciplinary care teams with frequent care coordination rounds and integration of electronic admission order sets can be effective in improving discharge efficiency as measured by discharge within two hours of meeting discharge goals.^11,12 Measuring discharge efficiency on an ongoing basis is very difficult; however, easy-to-measure targets such as discharge before noon may be used as a proxy measure of efficiency. These targets also have “face validity,” and because of these two factors, measures like DCBN have been widely implemented even though evidence to support their validity is minimal.

Our study has several limitations. While we controlled for observable characteristics using covariates and propensity score weighted analyses, there are likely unobservable characteristics that confound our analysis. We did not measure other factors that may affect discharge time of day such as high occupancy, staffing levels, patient transportation availability, and patient and family preferences. Given these limitations, we caution against interpreting a causal relationship between independent variables and the outcome. Finally, this analysis was conducted at a single tertiary care, academic medical center. The majority of pediatric admissions at this institution are either transferred from other hospitals or scheduled admissions for medical or surgical care. A smaller proportion of discharges are acute, unplanned admissions through our emergency department in children with or without underlying medical complexity. These factors plus the exclusion of observation, extended recovery, and all the less than two-day stays in this study contribute to a relatively higher average LOS. These factors potentially limit generalizability to other care settings. Additionally, the majority of the care teams involve care by resident physicians, and they are often the primary caregivers and write the majority of orders in patient charts such as discharge orders. While we were not able to control for within resident physician similarities between patients, we did control for autocorrelation at the attending level.

The results of our study suggest that DCBN is associated with a decreased LOS for medical but not surgical pediatric patients. DCBN may not be an appropriate measure for all services. Further research should be done to identify other feasible but more valid indicators for shorter LOS.

Disclosures

The authors have no financial relationships relative to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Funding

There were no external sources of funding for this work.

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

Community-acquired pneumonia (CAP) is the most common infection in hospitalized patients and the eighth most common cause of death in the United States.¹ Mortality from CAP is estimated to be 5.1% in the outpatient population,13.6% in hospitalized patients, and 35.1% in patients admitted to the intensive care unit.^2,3 CAP accounts for more than 50,000 deaths annually in the United States.² There are multiple risk factors for CAP, including tobacco use, malnutrition, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and mechanical bronchial obstruction. Underlying immunodeficiency, specifically humoral immunodeficiency, is also a risk factor for CAP.

Primary immunodeficiency (PIDD) is estimated to affect one in 1,800 individuals in the United States.⁴ The National Institutes of Health (NIH) estimates that only one out of three individuals with PIDD are appropriately diagnosed. Based on probability calculations on known PIDD patients versus incidence of disease, the NIH estimates that more than 500,000 individuals with PIDD remain undiagnosed in the United States.⁴ Further, there exists an average diagnostic delay of at least five years. This delay increases both morbidity and mortality and leads to increased healthcare utilization.^5,6

The most common form of primary immunodeficiency is due to humoral immunodeficiency, including selective IgA deficiency, specific antibody deficiency, and common variable immunodeficiency. Specific antibody deficiency is defined as a lack of response to polysaccharide antigens in the setting of low to normal Ig levels and an intact response to peptide antigens.⁷ Selective IgA deficiency is defined as the isolated deficiency of serum IgA in the setting of normal serum levels of IgG and IgM in an individual older than four years in whom other causes of hypogammaglobinemia have been excluded.⁸ Common variable immunodeficiency (CVID) is defined as a decreased serum concentration of IgG in combination with low levels of IgA and/or IgM with a poor or absent response to immunization in the absence of other defined immunodeficiency state.⁹ In addition to experiencing recurrent infections—namely bronchitis, sinusitis, otitis, and pneumonia—patients with CVID are also at increased risk of autoimmunity and malignancy. In adults, secondary immunodeficiency is more common than primary immunodeficiency. Secondary immunodeficiency occurs commonly with disease states like HIV infection, diabetes, cirrhosis, malnutrition, and autoimmune conditions.¹⁰ Additional causes of secondary immune defects due to humoral immunodeficiency include immune-modulating drugs—such as rituximab and ibrutinib—and hematologic malignancies, including chronic lymphocytic leukemia and multiple myeloma. Recurrent infections remain the leading cause of morbidity and mortality in patients with both primary and secondary immunodeficiency.^11,12

Evaluation of the humoral immune system begins with measurement of serum immunoglobulin (Ig) levels. Although abnormal Ig levels are not diagnostic of immunodeficiency, abnormal results may prompt additional evaluation. Screening strategies may assist in making an earlier diagnoses, potentially decreasing morbidity and mortality in patients with immunodeficiency.^13-15 To date, there have been no studies evaluating the utility of screening Ig levels to evaluate for underlying humoral immunodeficiency in patients hospitalized for CAP.

Study Design

This was a prospective cohort study conducted at Rochester General Hospital, a 528-bed tertiary care medical center, from February 2017 to April 2017. We enrolled 100 consecutive patients admitted to the inpatient internal medicine service with a physician diagnosis of CAP. Written consent was obtained from each patient. The study was approved by the institutional review board at Rochester General Hospital.

Case Definition

The following criteria were used to diagnose CAP: (1) Respiratory symptoms of productive cough or pleuritic chest pain, (2) Fever >38°C before or at the time of admission, and (3) chest imaging with infiltrate. Exclusion criteria included a diagnosis of hospital-acquired pneumonia, prior diagnosis of primary immunodeficiency, immunosuppression due to an underlying condition, such as HIV or malignancy, therapy with immunosuppressive medications including chemotherapy, Ig replacement within the past six months, or treatment with >10 mg prednisone for greater than 14 days before hospital admission.

Patients underwent an additional evaluation by a clinical immunologist if they met one of the following criteria: any hypergammaglobinemia (elevated IgG, IgM, or IgA), IgG hypogammaglobinemia <550 mg/dL, undetectable IgM or IgA, or if IgG, IgM, and IgA were all below the lower limit of normal.

CURB-65 was used for estimation of the severity of illness with CAP. The components of the score include age ≥65, confusion, BUN >19 mg/dl, respiratory rate ≥30 breaths per minute and systolic blood pressure <90 mm Hg or diastolic blood pressure ≤60 mm Hg. Each component is scored zero if absent or one if present. Predicted mortality ranges from 0.6% for a score of zero to 27.8% for a score of 5.

Data Collection

Patient health information including age, race, gender, medical history, admission notes, results of chest imaging studies, and relevant laboratory studies including serum levels of IgG, IgM, IgA, IgE on admission was obtained from the electronic medical health record. An additional evaluation by the immunologist occurred within three months of hospital discharge and included repeat Ig levels, pre- and postvaccination titers of polysaccharide and peptide antigens, serum protein electrophoresis, and B & T cell panels.

Description of Normal Levels

The normal levels of immunoglobulins were defined based on standard reference ranges at the laboratory at Rochester General Hospital; IgG (700-1,600 mg/dl), IgM (50-300 mg/dl), IgA (70-400 mg/dl), and IgE (0-378 IU/ml). Although there is no established classification regarding the degree of IgG hypogammaglobinemia,¹⁶ clinical immunologists commonly classify the severity of IgG hypogammaglobinemia as follows: mild (550-699 mg/dL), moderate (400-549 mg/dL), and severe (<400 mg/dL) IgG hypogammaglobinemia.

Statistical Analysis

Statistical analysis was performed using STATA software (StataCorp LLC, College Station, Texas). We conducted a Wilcoxon rank-sum test to compare the median difference in length of stay between groups with a low versus normal range of immunoglobulins. A Kruskal–Wallis test was performed to check for the median difference in IgG levels across degrees of illness severity (CURB-65 score categories). We conducted a simple linear regression analysis using the logarithmic data of the length of stay and IgG level variables. A chi-square test was used to determine the association between comorbidities and Ig levels.

Baseline Characteristics

There were 100 patients with CAP enrolled in this study with a median age of 65.04 ± 18.8, and 53% were female. Forty-seven patients reported a previous history of pneumonia and 18 reported a history of recurrent sinusitis or otitis media. Of the 100 enrolled patients, 46 had received pneumococcal polysaccharide vaccine (PPV23), 26 had received the 13-valent pneumococcal conjugate vaccine (PCV13), and 22 had received both (Table 1). The mean white blood cell count on admission was 12.9 ± 7 × 10³/uL with 75 ± 12.5% neutrophils. Total protein (6.5 ± 0.8) and albumin (3.7 ± 0.5) were within the normal range for the study population.

Immunoglobulin Analyses

The prevalence of hypogammaglobinemia in the study was 38% (95% CI: 28.47% to 48.25%). The median values of Ig levels for the entire study population and in patients with hypogammaglobinemia are summarized in Table 2.

• IgG hypogammaglobinemia (<700 mg/dl) was found in 27/100 patients, with a median level of 598 mg/dL, IQ range: 459-654. The median age in this group was 76.5 years, and 13 were female. Of these 27 patients, 10 had low IgM, four had low IgA, and four had an elevated IgE. In this group, 11 patients had received PPSV23, nine had received PCV13, and six had received both PPV23 and PCV13 before the index hospital admission.
• IgG hypergammaglobinemia (>1,600 mg/dl) was found in 9/100 patients, with a median level of 1,381 mg/dL, IQ range: 1,237-1,627. The median age was 61 years, and six were female. Of these nine patients, three had low IgM, one had low IgA, and four had elevated IgE.
• IgM hypogammaglobinemia (<50 mg/dl) was found in 23/100 patients with a median level of 38 mg/dL, IQ range: 25-43. In this group, the median age was 69 years, and 10 were female. Of these 23 patients, 10 had low IgG, and three had an elevated IgG.
• IgM hypergammaglobinemia (>300 mg/dl) was noted in two patients, with a median level of 491 mg/dL, IQ range: 418-564. Both patients were female, and one had elevated IgG.
• IgA hypogammaglobinemia (<70 mg/dl) was discovered in six patients, with a median level of 36 mg/dL, IQ range: 18-50. In this group, four patients had low IgG, four had low IgM, one had elevated IgE, and one had elevated IgG.
• IgA hypergammaglobinemia (>400 mg/dl) was noted in five patients, with a median level of 561 mg/dL, IQ range: 442-565: Two patients were female. Of these five patients, one had high IgG, and one had low IgG.

Length of Stay and Severity of Pneumonia

The median length of stay in the hospital for the entire study population was three days (IQ range: 2-5.5 days). Among patients with IgG hypogammaglobinemia, the median length of stay was two days longer as compared with patients who had IgG levels in the normal range (5 days, IQ range [3-10] vs 3days, IQ range [2-5], P = .0085).

The median CURB-65 score for the entire study population was two (IQ range: 1-3). The median CURB-65 score did not differ between patients with low and normal ranges of IgG levels (Median: 2, IQ range [1-3] vs Median: 1, IQ range [0-3], P = .2922). The CURB-65 score was not correlated with IgG levels (ρ = −0.0776, P = .4428). Length of stay, however, was positively correlated with CURB-65 score (ρ = .4673, P = .000)

A simple linear regression analysis using the logarithmic transformation of both length of stay and IgG level revealed a linear relationship between serum IgG levels and hospital length of stay (P = .0335, [R² = .0453]).

Comorbidities and New Diagnoses

No significant association was found between smoking status, obesity, COPD, asthma, diabetes mellitus, and hypogammaglobinemia.

Fourteen patients with abnormal Ig levels as defined by (1) the presence of hypergammaglobinemia (elevated IgG, IgM, or IgA), (2) IgG levels <550, (3) undetectable IgA or IgM, and (4) either IgG or both IgM and IgA below the lower limit of normal underwent further evaluation. Of these 14 patients, one was diagnosed with multiple myeloma, one with selective IgA deficiency, and three with specific antibody deficiency (Table 3).

Vascular access is a cornerstone of safe and effective medical care. The use of peripherally inserted central catheters (PICCs) to meet vascular access needs has recently increased.^1,2 PICCs offer several advantages over other central venous catheters. These advantages include increased reliability over intermediate to long-term use and reductions in complication rates during insertion.^3,4

Multiple studies have suggested a strong association between the number of PICC lumens and risk of complications, such as central-line associated bloodstream infection (CLABSI), venous thrombosis, and catheter occlusion.^5-8,9,10-12 These complications may lead to device failure, interrupt therapy, prolonged length of stay, and increased healthcare costs.^13-15 Thus, available guidelines recommend using PICCs with the least clinically necessary number of lumens.^1,16 Quality improvement strategies that have targeted decreasing the number of PICC lumens have reduced complications and healthcare costs.^17-19 However, variability exists in the selection of the number of PICC lumens, and many providers request multilumen devices “just in case” additional lumens are needed.^20,21 Such variation in device selection may stem from the paucity of information that defines the appropriate indications for the use of single- versus multi-lumen PICCs.

Therefore, to ensure appropriateness of PICC use, we designed an intervention to improve selection of the number of PICC lumens.

We conducted this pre–post quasi-experimental study in accordance with SQUIRE guidelines.²² Details regarding clinical parameters associated with the decision to place a PICC, patient characteristics, comorbidities, complications, and laboratory values were collected from the medical records of patients. All PICCs were placed by the Vascular Access Service Team (VAST) during the study period.

Intervention

The intervention consisted of three components: first, all hospitalists, pharmacists, and VAST nurses received education in the form of a CME lecture that emphasized use of the Michigan Appropriateness Guide for Intravenous Catheters (MAGIC).¹ These criteria define when use of a PICC is appropriate and emphasize how best to select the most appropriate device characteristics such as lumens and catheter gauge. Next, a multidisciplinary task force that consisted of hospitalists, VAST nurses, and pharmacists developed a list of indications specifying when use of a multilumen PICC was appropriate.¹ Third, the order for a PICC in our electronic medical record (EMR) system was modified to set single-lumen PICCs as default. If a multilumen PICC was requested, text-based justification from the ordering clinician was required.

As an additional safeguard, a VAST nurse reviewed the number of lumens and clinical scenario for each PICC order prior to insertion. If the number of lumens ordered was considered inappropriate on the basis of the developed list of MAGIC recommendations, the case was referred to a pharmacist for additional review. The pharmacist then reviewed active and anticipated medications, explored options for adjusting the medication delivery plan, and discussed these options with the ordering clinician to determine the most appropriate number of lumens.

Measures and Definitions

In accordance with the criteria set by the Centers for Disease Control National Healthcare Safety Network,²³ CLABSI was defined as a confirmed positive blood culture with a PICC in place for 48 hours or longer without another identified infection source or a positive PICC tip culture in the setting of clinically suspected infection. Venous thrombosis was defined as symptomatic upper extremity deep vein thromboembolism or pulmonary embolism that was radiographically confirmed after the placement of a PICC or within one week of device removal. Catheter occlusion was captured when documented or when tPA was administered for problems related to the PICC. The appropriateness of the number of PICC lumens was independently adjudicated by an attending physician and clinical pharmacist by comparing the indications of the device placed against predefined appropriateness criteria.

Outcomes

The primary outcome of interest was the change in the proportion of single-lumen PICCs placed. Secondary outcomes included (1) the placement of PICCs with an appropriate number of lumens, (2) the occurrence of PICC-related complications (CLABSI, venous thrombosis, and catheter occlusion), and (3) the need for a second procedure to place a multilumen device or additional vascular access.

Statistical Analysis

Descriptive statistics were used to tabulate and summarize patient and PICC characteristics. Differences between pre- and postintervention populations were assessed using χ², Fishers exact, t-, and Wilcoxon rank sum tests. Differences in complications were assessed using the two-sample tests of proportions. Results were reported as medians (IQR) and percentages with corresponding 95% confidence intervals. All statistical tests were two-sided, with P < .05 considered statistically significant. Analyses were conducted with Stata v.14 (stataCorp, College Station, Texas).

Ethical and Regulatory Oversight

This study was approved by the Institutional Review Board at the University of Michigan (IRB#HUM00118168).

Of the 133 PICCs placed preintervention, 64.7% (n = 86) were single lumen, 33.1% (n = 44) were double lumen, and 2.3% (n = 3) were triple lumen. Compared with the preintervention period, the use of single-lumen PICCs significantly increased following the intervention (64.7% to 93.6%; P < .001; Figure 1). As well, the proportion of PICCs with an inappropriate number of lumens decreased from 25.6% to 2.2% (P < .001; Table 1).

Preintervention, 14.3% (95% CI = 8.34-20.23) of the patients with PICCs experienced at least one complication (n = 19). Following the intervention, 15.1% (95% CI = 7.79-22.32) of the 93 patients with PICCs experienced at least one complication (absolute difference = 0.8%, P = .872). With respect to individual complications, CLABSI decreased from 5.3% (n = 7; 95% CI = 1.47-9.06) to 2.2% (n = 2; 95% CI = −0.80-5.10) (P = .239). Similarly, the incidence of catheter occlusion decreased from 8.3% (n = 11; 95% CI = 3.59-12.95) to 6.5% (n = 6; 95% CI = 1.46-11.44; P = .610; Table). Notably, only 12.1% (n = 21) of patients with a single-lumen PICC experienced any complication, whereas 20.0% (n = 10) of patients with a double lumen, and 66.7% (n = 2) with a triple lumen experienced a PICC-associated complication (P = .022). Patients with triple lumens had a significantly higher incidence of catheter occlusion compared with patients that received double- and single-lumen PICCs (66.7% vs. 12.0% and 5.2%, respectively; P = .003).

No patient who received a single-lumen device required a second procedure for the placement of a device with additional lumens. Similarly, no documentation suggesting an insufficient number of PICC lumens or the need for additional vascular access (eg, placement of additional PICCs) was found in medical records of patients postintervention. Pharmacists supporting the interventions and VAST team members reported no disagreements when discussing number of lumens or appropriateness of catheter choice.

In this single center, pre–post quasi-experimental study, a multimodal intervention based on the MAGIC criteria significantly reduced the use of multilumen PICCs. Additionally, a trend toward reductions in complications, including CLABSI and catheter occlusion, was also observed. Notably, these changes in ordering practices did not lead to requests for additional devices or replacement with a multilumen PICC when a single-lumen device was inserted. Collectively, our findings suggest that the use of single-lumen devices in a large direct care service can be feasibly and safely increased through this approach. Larger scale studies that implement MAGIC to inform placement of multilumen PICCs and reduce PICC-related complications now appear necessary.

Disclosures

The authors have nothing to disclose.

Vascular access is a cornerstone of safe and effective medical care. The use of peripherally inserted central catheters (PICCs) to meet vascular access needs has recently increased.^1,2 PICCs offer several advantages over other central venous catheters. These advantages include increased reliability over intermediate to long-term use and reductions in complication rates during insertion.^3,4

Multiple studies have suggested a strong association between the number of PICC lumens and risk of complications, such as central-line associated bloodstream infection (CLABSI), venous thrombosis, and catheter occlusion.^5-8,9,10-12 These complications may lead to device failure, interrupt therapy, prolonged length of stay, and increased healthcare costs.^13-15 Thus, available guidelines recommend using PICCs with the least clinically necessary number of lumens.^1,16 Quality improvement strategies that have targeted decreasing the number of PICC lumens have reduced complications and healthcare costs.^17-19 However, variability exists in the selection of the number of PICC lumens, and many providers request multilumen devices “just in case” additional lumens are needed.^20,21 Such variation in device selection may stem from the paucity of information that defines the appropriate indications for the use of single- versus multi-lumen PICCs.

Therefore, to ensure appropriateness of PICC use, we designed an intervention to improve selection of the number of PICC lumens.

We conducted this pre–post quasi-experimental study in accordance with SQUIRE guidelines.²² Details regarding clinical parameters associated with the decision to place a PICC, patient characteristics, comorbidities, complications, and laboratory values were collected from the medical records of patients. All PICCs were placed by the Vascular Access Service Team (VAST) during the study period.

Intervention

The intervention consisted of three components: first, all hospitalists, pharmacists, and VAST nurses received education in the form of a CME lecture that emphasized use of the Michigan Appropriateness Guide for Intravenous Catheters (MAGIC).¹ These criteria define when use of a PICC is appropriate and emphasize how best to select the most appropriate device characteristics such as lumens and catheter gauge. Next, a multidisciplinary task force that consisted of hospitalists, VAST nurses, and pharmacists developed a list of indications specifying when use of a multilumen PICC was appropriate.¹ Third, the order for a PICC in our electronic medical record (EMR) system was modified to set single-lumen PICCs as default. If a multilumen PICC was requested, text-based justification from the ordering clinician was required.

As an additional safeguard, a VAST nurse reviewed the number of lumens and clinical scenario for each PICC order prior to insertion. If the number of lumens ordered was considered inappropriate on the basis of the developed list of MAGIC recommendations, the case was referred to a pharmacist for additional review. The pharmacist then reviewed active and anticipated medications, explored options for adjusting the medication delivery plan, and discussed these options with the ordering clinician to determine the most appropriate number of lumens.

Measures and Definitions

In accordance with the criteria set by the Centers for Disease Control National Healthcare Safety Network,²³ CLABSI was defined as a confirmed positive blood culture with a PICC in place for 48 hours or longer without another identified infection source or a positive PICC tip culture in the setting of clinically suspected infection. Venous thrombosis was defined as symptomatic upper extremity deep vein thromboembolism or pulmonary embolism that was radiographically confirmed after the placement of a PICC or within one week of device removal. Catheter occlusion was captured when documented or when tPA was administered for problems related to the PICC. The appropriateness of the number of PICC lumens was independently adjudicated by an attending physician and clinical pharmacist by comparing the indications of the device placed against predefined appropriateness criteria.

Outcomes

The primary outcome of interest was the change in the proportion of single-lumen PICCs placed. Secondary outcomes included (1) the placement of PICCs with an appropriate number of lumens, (2) the occurrence of PICC-related complications (CLABSI, venous thrombosis, and catheter occlusion), and (3) the need for a second procedure to place a multilumen device or additional vascular access.

Statistical Analysis

Descriptive statistics were used to tabulate and summarize patient and PICC characteristics. Differences between pre- and postintervention populations were assessed using χ², Fishers exact, t-, and Wilcoxon rank sum tests. Differences in complications were assessed using the two-sample tests of proportions. Results were reported as medians (IQR) and percentages with corresponding 95% confidence intervals. All statistical tests were two-sided, with P < .05 considered statistically significant. Analyses were conducted with Stata v.14 (stataCorp, College Station, Texas).

Ethical and Regulatory Oversight

This study was approved by the Institutional Review Board at the University of Michigan (IRB#HUM00118168).

Of the 133 PICCs placed preintervention, 64.7% (n = 86) were single lumen, 33.1% (n = 44) were double lumen, and 2.3% (n = 3) were triple lumen. Compared with the preintervention period, the use of single-lumen PICCs significantly increased following the intervention (64.7% to 93.6%; P < .001; Figure 1). As well, the proportion of PICCs with an inappropriate number of lumens decreased from 25.6% to 2.2% (P < .001; Table 1).

Preintervention, 14.3% (95% CI = 8.34-20.23) of the patients with PICCs experienced at least one complication (n = 19). Following the intervention, 15.1% (95% CI = 7.79-22.32) of the 93 patients with PICCs experienced at least one complication (absolute difference = 0.8%, P = .872). With respect to individual complications, CLABSI decreased from 5.3% (n = 7; 95% CI = 1.47-9.06) to 2.2% (n = 2; 95% CI = −0.80-5.10) (P = .239). Similarly, the incidence of catheter occlusion decreased from 8.3% (n = 11; 95% CI = 3.59-12.95) to 6.5% (n = 6; 95% CI = 1.46-11.44; P = .610; Table). Notably, only 12.1% (n = 21) of patients with a single-lumen PICC experienced any complication, whereas 20.0% (n = 10) of patients with a double lumen, and 66.7% (n = 2) with a triple lumen experienced a PICC-associated complication (P = .022). Patients with triple lumens had a significantly higher incidence of catheter occlusion compared with patients that received double- and single-lumen PICCs (66.7% vs. 12.0% and 5.2%, respectively; P = .003).

No patient who received a single-lumen device required a second procedure for the placement of a device with additional lumens. Similarly, no documentation suggesting an insufficient number of PICC lumens or the need for additional vascular access (eg, placement of additional PICCs) was found in medical records of patients postintervention. Pharmacists supporting the interventions and VAST team members reported no disagreements when discussing number of lumens or appropriateness of catheter choice.

In this single center, pre–post quasi-experimental study, a multimodal intervention based on the MAGIC criteria significantly reduced the use of multilumen PICCs. Additionally, a trend toward reductions in complications, including CLABSI and catheter occlusion, was also observed. Notably, these changes in ordering practices did not lead to requests for additional devices or replacement with a multilumen PICC when a single-lumen device was inserted. Collectively, our findings suggest that the use of single-lumen devices in a large direct care service can be feasibly and safely increased through this approach. Larger scale studies that implement MAGIC to inform placement of multilumen PICCs and reduce PICC-related complications now appear necessary.

Disclosures

The authors have nothing to disclose.

Vascular access is a cornerstone of safe and effective medical care. The use of peripherally inserted central catheters (PICCs) to meet vascular access needs has recently increased.^1,2 PICCs offer several advantages over other central venous catheters. These advantages include increased reliability over intermediate to long-term use and reductions in complication rates during insertion.^3,4

Multiple studies have suggested a strong association between the number of PICC lumens and risk of complications, such as central-line associated bloodstream infection (CLABSI), venous thrombosis, and catheter occlusion.^5-8,9,10-12 These complications may lead to device failure, interrupt therapy, prolonged length of stay, and increased healthcare costs.^13-15 Thus, available guidelines recommend using PICCs with the least clinically necessary number of lumens.^1,16 Quality improvement strategies that have targeted decreasing the number of PICC lumens have reduced complications and healthcare costs.^17-19 However, variability exists in the selection of the number of PICC lumens, and many providers request multilumen devices “just in case” additional lumens are needed.^20,21 Such variation in device selection may stem from the paucity of information that defines the appropriate indications for the use of single- versus multi-lumen PICCs.

Therefore, to ensure appropriateness of PICC use, we designed an intervention to improve selection of the number of PICC lumens.

We conducted this pre–post quasi-experimental study in accordance with SQUIRE guidelines.²² Details regarding clinical parameters associated with the decision to place a PICC, patient characteristics, comorbidities, complications, and laboratory values were collected from the medical records of patients. All PICCs were placed by the Vascular Access Service Team (VAST) during the study period.

Intervention

The intervention consisted of three components: first, all hospitalists, pharmacists, and VAST nurses received education in the form of a CME lecture that emphasized use of the Michigan Appropriateness Guide for Intravenous Catheters (MAGIC).¹ These criteria define when use of a PICC is appropriate and emphasize how best to select the most appropriate device characteristics such as lumens and catheter gauge. Next, a multidisciplinary task force that consisted of hospitalists, VAST nurses, and pharmacists developed a list of indications specifying when use of a multilumen PICC was appropriate.¹ Third, the order for a PICC in our electronic medical record (EMR) system was modified to set single-lumen PICCs as default. If a multilumen PICC was requested, text-based justification from the ordering clinician was required.

As an additional safeguard, a VAST nurse reviewed the number of lumens and clinical scenario for each PICC order prior to insertion. If the number of lumens ordered was considered inappropriate on the basis of the developed list of MAGIC recommendations, the case was referred to a pharmacist for additional review. The pharmacist then reviewed active and anticipated medications, explored options for adjusting the medication delivery plan, and discussed these options with the ordering clinician to determine the most appropriate number of lumens.

Measures and Definitions

In accordance with the criteria set by the Centers for Disease Control National Healthcare Safety Network,²³ CLABSI was defined as a confirmed positive blood culture with a PICC in place for 48 hours or longer without another identified infection source or a positive PICC tip culture in the setting of clinically suspected infection. Venous thrombosis was defined as symptomatic upper extremity deep vein thromboembolism or pulmonary embolism that was radiographically confirmed after the placement of a PICC or within one week of device removal. Catheter occlusion was captured when documented or when tPA was administered for problems related to the PICC. The appropriateness of the number of PICC lumens was independently adjudicated by an attending physician and clinical pharmacist by comparing the indications of the device placed against predefined appropriateness criteria.

Outcomes

The primary outcome of interest was the change in the proportion of single-lumen PICCs placed. Secondary outcomes included (1) the placement of PICCs with an appropriate number of lumens, (2) the occurrence of PICC-related complications (CLABSI, venous thrombosis, and catheter occlusion), and (3) the need for a second procedure to place a multilumen device or additional vascular access.

Statistical Analysis

Descriptive statistics were used to tabulate and summarize patient and PICC characteristics. Differences between pre- and postintervention populations were assessed using χ², Fishers exact, t-, and Wilcoxon rank sum tests. Differences in complications were assessed using the two-sample tests of proportions. Results were reported as medians (IQR) and percentages with corresponding 95% confidence intervals. All statistical tests were two-sided, with P < .05 considered statistically significant. Analyses were conducted with Stata v.14 (stataCorp, College Station, Texas).

Ethical and Regulatory Oversight

This study was approved by the Institutional Review Board at the University of Michigan (IRB#HUM00118168).

Of the 133 PICCs placed preintervention, 64.7% (n = 86) were single lumen, 33.1% (n = 44) were double lumen, and 2.3% (n = 3) were triple lumen. Compared with the preintervention period, the use of single-lumen PICCs significantly increased following the intervention (64.7% to 93.6%; P < .001; Figure 1). As well, the proportion of PICCs with an inappropriate number of lumens decreased from 25.6% to 2.2% (P < .001; Table 1).

Preintervention, 14.3% (95% CI = 8.34-20.23) of the patients with PICCs experienced at least one complication (n = 19). Following the intervention, 15.1% (95% CI = 7.79-22.32) of the 93 patients with PICCs experienced at least one complication (absolute difference = 0.8%, P = .872). With respect to individual complications, CLABSI decreased from 5.3% (n = 7; 95% CI = 1.47-9.06) to 2.2% (n = 2; 95% CI = −0.80-5.10) (P = .239). Similarly, the incidence of catheter occlusion decreased from 8.3% (n = 11; 95% CI = 3.59-12.95) to 6.5% (n = 6; 95% CI = 1.46-11.44; P = .610; Table). Notably, only 12.1% (n = 21) of patients with a single-lumen PICC experienced any complication, whereas 20.0% (n = 10) of patients with a double lumen, and 66.7% (n = 2) with a triple lumen experienced a PICC-associated complication (P = .022). Patients with triple lumens had a significantly higher incidence of catheter occlusion compared with patients that received double- and single-lumen PICCs (66.7% vs. 12.0% and 5.2%, respectively; P = .003).

No patient who received a single-lumen device required a second procedure for the placement of a device with additional lumens. Similarly, no documentation suggesting an insufficient number of PICC lumens or the need for additional vascular access (eg, placement of additional PICCs) was found in medical records of patients postintervention. Pharmacists supporting the interventions and VAST team members reported no disagreements when discussing number of lumens or appropriateness of catheter choice.

In this single center, pre–post quasi-experimental study, a multimodal intervention based on the MAGIC criteria significantly reduced the use of multilumen PICCs. Additionally, a trend toward reductions in complications, including CLABSI and catheter occlusion, was also observed. Notably, these changes in ordering practices did not lead to requests for additional devices or replacement with a multilumen PICC when a single-lumen device was inserted. Collectively, our findings suggest that the use of single-lumen devices in a large direct care service can be feasibly and safely increased through this approach. Larger scale studies that implement MAGIC to inform placement of multilumen PICCs and reduce PICC-related complications now appear necessary.

Disclosures

The authors have nothing to disclose.

Inspired by the ABIM Foundation's Choosing Wisely campaign, the “Things We Do for No Reason” series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A 74-year-old man with a history of diabetes and gastrointestinal bleeding two months prior, presents with nausea/vomiting and diarrhea after eating unrefrigerated leftovers. Body mass index is 25. Labs are unremarkable except for a blood urea nitrogen of 37 mg/dL, serum creatinine of 1.6 mg/dL up from 1.3, and white blood cell count of 12 K/µL. He is afebrile with blood pressure of 100/60 mm Hg. He lives alone and is fully ambulatory at baseline. The Emergency Department physician requests observation admission for “dehydration/gastroenteritis.” The admitting hospitalist orders intermittent pneumatic compression (IPC) for venous thromboembolism (VTE) prophylaxis.

The American Public Health Association has called VTE prophylaxis a “public health crisis” due to the gap between existing evidence and implementation.¹ The incidence of symptomatic deep venous thrombosis (DVT) and pulmonary embolism (PE) in hospitalized medical patients managed without prophylaxis is 0.96% and 1.2%, respectively,² whereas that of asymptomatic DVT in hospitalized patients is approximately 1.8%.^2,3 IPC is widely used, and an international registry of 15,156 hospitalized acutely ill medical patients found that 22% of United States patients received IPC for VTE prophylaxis compared with 0.2% of patients in other countries.⁴

The main reason clinicians opt to use IPC for VTE prophylaxis is the wish to avoid the bleeding risk associated with heparin. The American College of Chest Physicians antithrombotic guideline 9th edition (ACCP-AT9) recommends mechanical prophylaxis for patients at increased risk for thrombosis who are either bleeding or at “high risk for major bleeding.”⁵ The guideline considered patients to have an excessive bleeding risk if they had an active gastroduodenal ulcer, bleeding within the past three months, a platelet count below 50,000/ml, or more than one of the following risk factors: age ≥ 85, hepatic failure with INR >1.5, severe renal failure with GFR <30 mL/min/m², ICU/CCU admission, central venous catheter, rheumatic disease, current cancer, or male gender.⁵ IPC also avoids the risk of heparin-induced thrombocytopenia, which is a rare but potentially devastating condition.

Prior studies have shown that IPC reduces VTE in high-risk groups such as orthopedic, surgical, trauma, and stroke patients. The largest systematic review on the topic found 70 studies of 16,164 high-risk patients and concluded that IPC reduced the rate of DVT from 16.7% to 7.3% and PE from 2.8% to 1.2%.⁶Since the publication of this systematic review, an additional large randomized trial of immobile patients with acute stroke was published, which found a reduction in the composite endpoint of proximal DVT on screening compression ultrasound or symptomatic proximal DVT from 12.1% to 8.5%.⁷ Another systematic review of 12 studies of high-risk ICU patients found that IPC conferred a relative risk of 0.5 (95% CI: 0.20-1.23) for DVT, although this result was not statistically significant.⁸ Finally, a Cochrane review of studies that compared IPC combined with pharmacologic prophylaxis with pharmacologic prophylaxis alone in high-risk trauma and surgical patients found reduced PE for the combination.⁹

IPC devices are frequently not worn or turned on. A study at two university-affiliated level one trauma centers found IPC to be functioning properly in only 19% of trauma patients.¹⁰ In another study of gynecologic oncology patients, 52% of IPCs were functioning improperly and 25% of patients experienced some discomfort, inconvenience, or problems with external pneumatic compression.¹¹ Redness, itching, or discomfort was cited by 26% of patients, and patients removed IPCs 11% of the time when nurses left the room.^11,12 In another study, skin breakdown occurred in 3% of IPC patients as compared with 1% in the control group.⁷

Concerns about a possible link between IPC and increased fall risk was raised by a 2005 report of 40 falls by the Pennsylvania Patient Safety Reporting System,¹³ and IPC accounted for 16 of 3,562 hospital falls according to Boelig and colleagues.¹⁴ Ritsema et al. found that the most important perceived barriers to IPC compliance according to patient surveys were that the devices “prevented walking or getting up” (47%), “were tethering or tangling” (25%), and “woke the patient from sleep” (15%).¹⁵

IPC devices are not created equally, differing in “anatomical location of the sleeve garment, number and location of air bladders, patterns for compression cycles and duration of inflation time and deflation time.”¹⁶ Comparative effectiveness may differ. A study comparing a rapid inflation asymmetrical compression device by Venaflow with a sequential circumferential compression device by Kendall in a high-risk post knee replacement population produced DVT rates of 6.9% versus 15%, respectively (P = .007).^16,17 Furthermore, the type of sleeve and device may affect comfort and compliance as some sleeves are considered “breathable.”

Perhaps most importantly, data supporting IPC efficacy in general medical ward patients are virtually nonexistent. Ho’s meta-analysis of IPC after excluding surgical patients found a relative risk (RR) of 0.53 (95% CI: 0.35-0.81, P < .01) for DVT in nine trials and a nonstatistically significant RR of 0.64 (95% CI: 0.29-1.42. P = .27) for PE in six trials.⁶ However, if high-risk populations such as trauma, critical care, and stroke are excluded, then the only remaining study is a letter to the editor published in 1982 that compared 20 patients with unstable angina treated with IPC with 23 controls and found a nonsignificant reduction in screened VTE.¹⁸ Given the near complete lack of data supporting IPC in medical patients, the ACCP-AT9 guideline rates the strength of evidence recommendation to use IPC only in medical patients who are currently bleeding or at high risk of major bleeding as “2C,” which is defined as “weak recommendation” based on “low-quality or very low-quality evidence.”¹⁹ Similarly, the latest American College of Physicians guidelines (2011) recommend pharmacologic prophylaxis for medical patients rather than IPC, except when bleeding risk outweighs the likely benefit of pharmacologic prophylaxis. The guidelines specifically recommend against graduated compression stockings given the lack of efficacy and increased risk of skin breakdown.²⁰

IPC is expensive. The cost for pneumatic compression boots is quoted in the literature at $120 with a range of $80-$250.²¹ Furthermore, patients averaged 2.5 pairs per hospitalization.²² An online search of retail prices revealed a pair of knee-length Covidien 5329 compression sleeves at $299.19 per pair²³ and knee-length Kendall 7325-2 compression sleeves at $433.76 per pair²⁴ with pumps costing $7,518.07 for Venodyne 610 Advantage,²⁵ $6,965.98 for VenaFlow Elite,²⁶ and $5,750.50 for Covidien 29525 700 series Kendall SCD.²⁷ However, using these prices would be overestimating costs given that hospitals do not pay retail prices. A prior surgical cost/benefit analysis used a prevalence of 6.9% and a 69% reduction of DVT.²⁸ However, recent data showed that VTE incidence in 31,219 medical patients was only 0.57% and RR for a large VTE prevention initiative was a nonsignificant 10% reduction.²⁹ Even if we use a VTE prevalence of 1% for the general medical floor and 0.5% RR reduction, 200 patients would need to be treated to prevent one symptomatic VTE and would cost about $24,000 for IPC sleeves alone (estimating $120 per patient) without factoring in additional costs of pump purchase or rental and six additional episodes of anticipated skin breakdown. In comparison, the cost for VTE treatment ranges from $7,712 to $16,644.³⁰

First, one should consider if VTE prophylaxis is needed based on risk assessment. According to the Agency for Healthcare Research and Quality (AHRQ), the most widely used risk stratification model is the University of California San Diego “3 bucket model” (Table 1) derived from tables in ACCP-AT8 guidelines.³¹The Caprini risk assessment model has been validated for surgical patients, but AHRQ offers caveats related to the complexity of the tool, the difficulty many sites have integrating it into order sets, and the negative experience of the Michigan Hospital Medicine Safety Consortium. The consortium enrolled 43 hospitals with the great majority using the Caprini risk assessment model, but it failed to reduce VTE in medical patients.³¹ Alternatively, the ACCP-AT9 guidelines recommend the Padua prediction score for risk assessment of medical patients (Table 2). VTE occurs in 0.3% of low-risk patients (Padua score <4) and 11.0% of high-risk patients (Padua score ≥4). If IPC is used in the low-risk populations with a predicted VTE rate of 0.3, then 666 patients would need to be treated to prevent one VTE. Treating 666 patients would cost $79,920 for IPC sleeves alone plus $5,500-$7,500 per pump and result in 20 additional episodes of skin breakdown. Therefore, IPC should be reserved for high-risk populations with contraindications to pharmacologic prophylaxis.

• The VTE risk of general medicine ward patients should be assessed, preferably with the “3 bucket” or Padua risk assessment models.
• For low-risk patients, no VTE prophylaxis is indicated. Ambulation ought to be encouraged for low-risk patients.
• If prophylaxis is indicated, then bleeding risk should be assessed to determine a contraindication to pharmacologic prophylaxis. If there is excessive bleeding risk, then treatment with IPC may be considered even though there are only data to support this in high-risk populations such as surgical, stroke, trauma, and critical care patients.
• If using IPC, then strategies that ensure compliance and consider patient comfort based on type and location of sleeves should be implemented.
• Combined IPC and pharmacologic prophylaxis should be used for high-risk trauma or surgical patients.

No current evidence supports IPC efficacy in general medical ward patients despite its widespread use; thus, prospective trials in this population are needed. Given costs, potential side effects, and uncertain efficacy in general medical ward patients, IPC should be reserved for surgical, trauma, critical care, or stroke patients. It may be considered for moderate to high-risk medical patients with excessive bleeding risk. Our clinical scenario patient bled within the past three months (odds ratio for bleeding 3.64; 95% CI, 2.21-5.99).³² On the basis of the increased risk, a dutiful hospitalist might be tempted to order IPC. However, given that our patient is ambulatory, is toileting frequently, and has an expected observation stay of less than 48 hours, he is considered low risk for VTE (Table 1). Additionally, his Padua score of two confirms his low risk status (Table 2). No VTE prophylaxis would be indicated.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailingTWDFNR@hospitalmedicine.org.

Disclosures

The authors have nothing to disclose.

Inspired by the ABIM Foundation's Choosing Wisely campaign, the “Things We Do for No Reason” series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A 74-year-old man with a history of diabetes and gastrointestinal bleeding two months prior, presents with nausea/vomiting and diarrhea after eating unrefrigerated leftovers. Body mass index is 25. Labs are unremarkable except for a blood urea nitrogen of 37 mg/dL, serum creatinine of 1.6 mg/dL up from 1.3, and white blood cell count of 12 K/µL. He is afebrile with blood pressure of 100/60 mm Hg. He lives alone and is fully ambulatory at baseline. The Emergency Department physician requests observation admission for “dehydration/gastroenteritis.” The admitting hospitalist orders intermittent pneumatic compression (IPC) for venous thromboembolism (VTE) prophylaxis.

The American Public Health Association has called VTE prophylaxis a “public health crisis” due to the gap between existing evidence and implementation.¹ The incidence of symptomatic deep venous thrombosis (DVT) and pulmonary embolism (PE) in hospitalized medical patients managed without prophylaxis is 0.96% and 1.2%, respectively,² whereas that of asymptomatic DVT in hospitalized patients is approximately 1.8%.^2,3 IPC is widely used, and an international registry of 15,156 hospitalized acutely ill medical patients found that 22% of United States patients received IPC for VTE prophylaxis compared with 0.2% of patients in other countries.⁴

The main reason clinicians opt to use IPC for VTE prophylaxis is the wish to avoid the bleeding risk associated with heparin. The American College of Chest Physicians antithrombotic guideline 9th edition (ACCP-AT9) recommends mechanical prophylaxis for patients at increased risk for thrombosis who are either bleeding or at “high risk for major bleeding.”⁵ The guideline considered patients to have an excessive bleeding risk if they had an active gastroduodenal ulcer, bleeding within the past three months, a platelet count below 50,000/ml, or more than one of the following risk factors: age ≥ 85, hepatic failure with INR >1.5, severe renal failure with GFR <30 mL/min/m², ICU/CCU admission, central venous catheter, rheumatic disease, current cancer, or male gender.⁵ IPC also avoids the risk of heparin-induced thrombocytopenia, which is a rare but potentially devastating condition.

Prior studies have shown that IPC reduces VTE in high-risk groups such as orthopedic, surgical, trauma, and stroke patients. The largest systematic review on the topic found 70 studies of 16,164 high-risk patients and concluded that IPC reduced the rate of DVT from 16.7% to 7.3% and PE from 2.8% to 1.2%.⁶Since the publication of this systematic review, an additional large randomized trial of immobile patients with acute stroke was published, which found a reduction in the composite endpoint of proximal DVT on screening compression ultrasound or symptomatic proximal DVT from 12.1% to 8.5%.⁷ Another systematic review of 12 studies of high-risk ICU patients found that IPC conferred a relative risk of 0.5 (95% CI: 0.20-1.23) for DVT, although this result was not statistically significant.⁸ Finally, a Cochrane review of studies that compared IPC combined with pharmacologic prophylaxis with pharmacologic prophylaxis alone in high-risk trauma and surgical patients found reduced PE for the combination.⁹

IPC devices are frequently not worn or turned on. A study at two university-affiliated level one trauma centers found IPC to be functioning properly in only 19% of trauma patients.¹⁰ In another study of gynecologic oncology patients, 52% of IPCs were functioning improperly and 25% of patients experienced some discomfort, inconvenience, or problems with external pneumatic compression.¹¹ Redness, itching, or discomfort was cited by 26% of patients, and patients removed IPCs 11% of the time when nurses left the room.^11,12 In another study, skin breakdown occurred in 3% of IPC patients as compared with 1% in the control group.⁷

Concerns about a possible link between IPC and increased fall risk was raised by a 2005 report of 40 falls by the Pennsylvania Patient Safety Reporting System,¹³ and IPC accounted for 16 of 3,562 hospital falls according to Boelig and colleagues.¹⁴ Ritsema et al. found that the most important perceived barriers to IPC compliance according to patient surveys were that the devices “prevented walking or getting up” (47%), “were tethering or tangling” (25%), and “woke the patient from sleep” (15%).¹⁵

IPC devices are not created equally, differing in “anatomical location of the sleeve garment, number and location of air bladders, patterns for compression cycles and duration of inflation time and deflation time.”¹⁶ Comparative effectiveness may differ. A study comparing a rapid inflation asymmetrical compression device by Venaflow with a sequential circumferential compression device by Kendall in a high-risk post knee replacement population produced DVT rates of 6.9% versus 15%, respectively (P = .007).^16,17 Furthermore, the type of sleeve and device may affect comfort and compliance as some sleeves are considered “breathable.”

Perhaps most importantly, data supporting IPC efficacy in general medical ward patients are virtually nonexistent. Ho’s meta-analysis of IPC after excluding surgical patients found a relative risk (RR) of 0.53 (95% CI: 0.35-0.81, P < .01) for DVT in nine trials and a nonstatistically significant RR of 0.64 (95% CI: 0.29-1.42. P = .27) for PE in six trials.⁶ However, if high-risk populations such as trauma, critical care, and stroke are excluded, then the only remaining study is a letter to the editor published in 1982 that compared 20 patients with unstable angina treated with IPC with 23 controls and found a nonsignificant reduction in screened VTE.¹⁸ Given the near complete lack of data supporting IPC in medical patients, the ACCP-AT9 guideline rates the strength of evidence recommendation to use IPC only in medical patients who are currently bleeding or at high risk of major bleeding as “2C,” which is defined as “weak recommendation” based on “low-quality or very low-quality evidence.”¹⁹ Similarly, the latest American College of Physicians guidelines (2011) recommend pharmacologic prophylaxis for medical patients rather than IPC, except when bleeding risk outweighs the likely benefit of pharmacologic prophylaxis. The guidelines specifically recommend against graduated compression stockings given the lack of efficacy and increased risk of skin breakdown.²⁰

IPC is expensive. The cost for pneumatic compression boots is quoted in the literature at $120 with a range of $80-$250.²¹ Furthermore, patients averaged 2.5 pairs per hospitalization.²² An online search of retail prices revealed a pair of knee-length Covidien 5329 compression sleeves at $299.19 per pair²³ and knee-length Kendall 7325-2 compression sleeves at $433.76 per pair²⁴ with pumps costing $7,518.07 for Venodyne 610 Advantage,²⁵ $6,965.98 for VenaFlow Elite,²⁶ and $5,750.50 for Covidien 29525 700 series Kendall SCD.²⁷ However, using these prices would be overestimating costs given that hospitals do not pay retail prices. A prior surgical cost/benefit analysis used a prevalence of 6.9% and a 69% reduction of DVT.²⁸ However, recent data showed that VTE incidence in 31,219 medical patients was only 0.57% and RR for a large VTE prevention initiative was a nonsignificant 10% reduction.²⁹ Even if we use a VTE prevalence of 1% for the general medical floor and 0.5% RR reduction, 200 patients would need to be treated to prevent one symptomatic VTE and would cost about $24,000 for IPC sleeves alone (estimating $120 per patient) without factoring in additional costs of pump purchase or rental and six additional episodes of anticipated skin breakdown. In comparison, the cost for VTE treatment ranges from $7,712 to $16,644.³⁰

First, one should consider if VTE prophylaxis is needed based on risk assessment. According to the Agency for Healthcare Research and Quality (AHRQ), the most widely used risk stratification model is the University of California San Diego “3 bucket model” (Table 1) derived from tables in ACCP-AT8 guidelines.³¹The Caprini risk assessment model has been validated for surgical patients, but AHRQ offers caveats related to the complexity of the tool, the difficulty many sites have integrating it into order sets, and the negative experience of the Michigan Hospital Medicine Safety Consortium. The consortium enrolled 43 hospitals with the great majority using the Caprini risk assessment model, but it failed to reduce VTE in medical patients.³¹ Alternatively, the ACCP-AT9 guidelines recommend the Padua prediction score for risk assessment of medical patients (Table 2). VTE occurs in 0.3% of low-risk patients (Padua score <4) and 11.0% of high-risk patients (Padua score ≥4). If IPC is used in the low-risk populations with a predicted VTE rate of 0.3, then 666 patients would need to be treated to prevent one VTE. Treating 666 patients would cost $79,920 for IPC sleeves alone plus $5,500-$7,500 per pump and result in 20 additional episodes of skin breakdown. Therefore, IPC should be reserved for high-risk populations with contraindications to pharmacologic prophylaxis.

• The VTE risk of general medicine ward patients should be assessed, preferably with the “3 bucket” or Padua risk assessment models.
• For low-risk patients, no VTE prophylaxis is indicated. Ambulation ought to be encouraged for low-risk patients.
• If prophylaxis is indicated, then bleeding risk should be assessed to determine a contraindication to pharmacologic prophylaxis. If there is excessive bleeding risk, then treatment with IPC may be considered even though there are only data to support this in high-risk populations such as surgical, stroke, trauma, and critical care patients.
• If using IPC, then strategies that ensure compliance and consider patient comfort based on type and location of sleeves should be implemented.
• Combined IPC and pharmacologic prophylaxis should be used for high-risk trauma or surgical patients.

No current evidence supports IPC efficacy in general medical ward patients despite its widespread use; thus, prospective trials in this population are needed. Given costs, potential side effects, and uncertain efficacy in general medical ward patients, IPC should be reserved for surgical, trauma, critical care, or stroke patients. It may be considered for moderate to high-risk medical patients with excessive bleeding risk. Our clinical scenario patient bled within the past three months (odds ratio for bleeding 3.64; 95% CI, 2.21-5.99).³² On the basis of the increased risk, a dutiful hospitalist might be tempted to order IPC. However, given that our patient is ambulatory, is toileting frequently, and has an expected observation stay of less than 48 hours, he is considered low risk for VTE (Table 1). Additionally, his Padua score of two confirms his low risk status (Table 2). No VTE prophylaxis would be indicated.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailingTWDFNR@hospitalmedicine.org.

Disclosures

The authors have nothing to disclose.

Inspired by the ABIM Foundation's Choosing Wisely campaign, the “Things We Do for No Reason” series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

A 74-year-old man with a history of diabetes and gastrointestinal bleeding two months prior, presents with nausea/vomiting and diarrhea after eating unrefrigerated leftovers. Body mass index is 25. Labs are unremarkable except for a blood urea nitrogen of 37 mg/dL, serum creatinine of 1.6 mg/dL up from 1.3, and white blood cell count of 12 K/µL. He is afebrile with blood pressure of 100/60 mm Hg. He lives alone and is fully ambulatory at baseline. The Emergency Department physician requests observation admission for “dehydration/gastroenteritis.” The admitting hospitalist orders intermittent pneumatic compression (IPC) for venous thromboembolism (VTE) prophylaxis.

The American Public Health Association has called VTE prophylaxis a “public health crisis” due to the gap between existing evidence and implementation.¹ The incidence of symptomatic deep venous thrombosis (DVT) and pulmonary embolism (PE) in hospitalized medical patients managed without prophylaxis is 0.96% and 1.2%, respectively,² whereas that of asymptomatic DVT in hospitalized patients is approximately 1.8%.^2,3 IPC is widely used, and an international registry of 15,156 hospitalized acutely ill medical patients found that 22% of United States patients received IPC for VTE prophylaxis compared with 0.2% of patients in other countries.⁴

The main reason clinicians opt to use IPC for VTE prophylaxis is the wish to avoid the bleeding risk associated with heparin. The American College of Chest Physicians antithrombotic guideline 9th edition (ACCP-AT9) recommends mechanical prophylaxis for patients at increased risk for thrombosis who are either bleeding or at “high risk for major bleeding.”⁵ The guideline considered patients to have an excessive bleeding risk if they had an active gastroduodenal ulcer, bleeding within the past three months, a platelet count below 50,000/ml, or more than one of the following risk factors: age ≥ 85, hepatic failure with INR >1.5, severe renal failure with GFR <30 mL/min/m², ICU/CCU admission, central venous catheter, rheumatic disease, current cancer, or male gender.⁵ IPC also avoids the risk of heparin-induced thrombocytopenia, which is a rare but potentially devastating condition.

Prior studies have shown that IPC reduces VTE in high-risk groups such as orthopedic, surgical, trauma, and stroke patients. The largest systematic review on the topic found 70 studies of 16,164 high-risk patients and concluded that IPC reduced the rate of DVT from 16.7% to 7.3% and PE from 2.8% to 1.2%.⁶Since the publication of this systematic review, an additional large randomized trial of immobile patients with acute stroke was published, which found a reduction in the composite endpoint of proximal DVT on screening compression ultrasound or symptomatic proximal DVT from 12.1% to 8.5%.⁷ Another systematic review of 12 studies of high-risk ICU patients found that IPC conferred a relative risk of 0.5 (95% CI: 0.20-1.23) for DVT, although this result was not statistically significant.⁸ Finally, a Cochrane review of studies that compared IPC combined with pharmacologic prophylaxis with pharmacologic prophylaxis alone in high-risk trauma and surgical patients found reduced PE for the combination.⁹

IPC devices are frequently not worn or turned on. A study at two university-affiliated level one trauma centers found IPC to be functioning properly in only 19% of trauma patients.¹⁰ In another study of gynecologic oncology patients, 52% of IPCs were functioning improperly and 25% of patients experienced some discomfort, inconvenience, or problems with external pneumatic compression.¹¹ Redness, itching, or discomfort was cited by 26% of patients, and patients removed IPCs 11% of the time when nurses left the room.^11,12 In another study, skin breakdown occurred in 3% of IPC patients as compared with 1% in the control group.⁷

Concerns about a possible link between IPC and increased fall risk was raised by a 2005 report of 40 falls by the Pennsylvania Patient Safety Reporting System,¹³ and IPC accounted for 16 of 3,562 hospital falls according to Boelig and colleagues.¹⁴ Ritsema et al. found that the most important perceived barriers to IPC compliance according to patient surveys were that the devices “prevented walking or getting up” (47%), “were tethering or tangling” (25%), and “woke the patient from sleep” (15%).¹⁵

IPC devices are not created equally, differing in “anatomical location of the sleeve garment, number and location of air bladders, patterns for compression cycles and duration of inflation time and deflation time.”¹⁶ Comparative effectiveness may differ. A study comparing a rapid inflation asymmetrical compression device by Venaflow with a sequential circumferential compression device by Kendall in a high-risk post knee replacement population produced DVT rates of 6.9% versus 15%, respectively (P = .007).^16,17 Furthermore, the type of sleeve and device may affect comfort and compliance as some sleeves are considered “breathable.”

Perhaps most importantly, data supporting IPC efficacy in general medical ward patients are virtually nonexistent. Ho’s meta-analysis of IPC after excluding surgical patients found a relative risk (RR) of 0.53 (95% CI: 0.35-0.81, P < .01) for DVT in nine trials and a nonstatistically significant RR of 0.64 (95% CI: 0.29-1.42. P = .27) for PE in six trials.⁶ However, if high-risk populations such as trauma, critical care, and stroke are excluded, then the only remaining study is a letter to the editor published in 1982 that compared 20 patients with unstable angina treated with IPC with 23 controls and found a nonsignificant reduction in screened VTE.¹⁸ Given the near complete lack of data supporting IPC in medical patients, the ACCP-AT9 guideline rates the strength of evidence recommendation to use IPC only in medical patients who are currently bleeding or at high risk of major bleeding as “2C,” which is defined as “weak recommendation” based on “low-quality or very low-quality evidence.”¹⁹ Similarly, the latest American College of Physicians guidelines (2011) recommend pharmacologic prophylaxis for medical patients rather than IPC, except when bleeding risk outweighs the likely benefit of pharmacologic prophylaxis. The guidelines specifically recommend against graduated compression stockings given the lack of efficacy and increased risk of skin breakdown.²⁰

IPC is expensive. The cost for pneumatic compression boots is quoted in the literature at $120 with a range of $80-$250.²¹ Furthermore, patients averaged 2.5 pairs per hospitalization.²² An online search of retail prices revealed a pair of knee-length Covidien 5329 compression sleeves at $299.19 per pair²³ and knee-length Kendall 7325-2 compression sleeves at $433.76 per pair²⁴ with pumps costing $7,518.07 for Venodyne 610 Advantage,²⁵ $6,965.98 for VenaFlow Elite,²⁶ and $5,750.50 for Covidien 29525 700 series Kendall SCD.²⁷ However, using these prices would be overestimating costs given that hospitals do not pay retail prices. A prior surgical cost/benefit analysis used a prevalence of 6.9% and a 69% reduction of DVT.²⁸ However, recent data showed that VTE incidence in 31,219 medical patients was only 0.57% and RR for a large VTE prevention initiative was a nonsignificant 10% reduction.²⁹ Even if we use a VTE prevalence of 1% for the general medical floor and 0.5% RR reduction, 200 patients would need to be treated to prevent one symptomatic VTE and would cost about $24,000 for IPC sleeves alone (estimating $120 per patient) without factoring in additional costs of pump purchase or rental and six additional episodes of anticipated skin breakdown. In comparison, the cost for VTE treatment ranges from $7,712 to $16,644.³⁰

First, one should consider if VTE prophylaxis is needed based on risk assessment. According to the Agency for Healthcare Research and Quality (AHRQ), the most widely used risk stratification model is the University of California San Diego “3 bucket model” (Table 1) derived from tables in ACCP-AT8 guidelines.³¹The Caprini risk assessment model has been validated for surgical patients, but AHRQ offers caveats related to the complexity of the tool, the difficulty many sites have integrating it into order sets, and the negative experience of the Michigan Hospital Medicine Safety Consortium. The consortium enrolled 43 hospitals with the great majority using the Caprini risk assessment model, but it failed to reduce VTE in medical patients.³¹ Alternatively, the ACCP-AT9 guidelines recommend the Padua prediction score for risk assessment of medical patients (Table 2). VTE occurs in 0.3% of low-risk patients (Padua score <4) and 11.0% of high-risk patients (Padua score ≥4). If IPC is used in the low-risk populations with a predicted VTE rate of 0.3, then 666 patients would need to be treated to prevent one VTE. Treating 666 patients would cost $79,920 for IPC sleeves alone plus $5,500-$7,500 per pump and result in 20 additional episodes of skin breakdown. Therefore, IPC should be reserved for high-risk populations with contraindications to pharmacologic prophylaxis.

• The VTE risk of general medicine ward patients should be assessed, preferably with the “3 bucket” or Padua risk assessment models.
• For low-risk patients, no VTE prophylaxis is indicated. Ambulation ought to be encouraged for low-risk patients.
• If prophylaxis is indicated, then bleeding risk should be assessed to determine a contraindication to pharmacologic prophylaxis. If there is excessive bleeding risk, then treatment with IPC may be considered even though there are only data to support this in high-risk populations such as surgical, stroke, trauma, and critical care patients.
• If using IPC, then strategies that ensure compliance and consider patient comfort based on type and location of sleeves should be implemented.
• Combined IPC and pharmacologic prophylaxis should be used for high-risk trauma or surgical patients.

No current evidence supports IPC efficacy in general medical ward patients despite its widespread use; thus, prospective trials in this population are needed. Given costs, potential side effects, and uncertain efficacy in general medical ward patients, IPC should be reserved for surgical, trauma, critical care, or stroke patients. It may be considered for moderate to high-risk medical patients with excessive bleeding risk. Our clinical scenario patient bled within the past three months (odds ratio for bleeding 3.64; 95% CI, 2.21-5.99).³² On the basis of the increased risk, a dutiful hospitalist might be tempted to order IPC. However, given that our patient is ambulatory, is toileting frequently, and has an expected observation stay of less than 48 hours, he is considered low risk for VTE (Table 1). Additionally, his Padua score of two confirms his low risk status (Table 2). No VTE prophylaxis would be indicated.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailingTWDFNR@hospitalmedicine.org.

Disclosures

The authors have nothing to disclose.

A 14-year-old girl with a history of asthma presented to the Emergency Department (ED) with three months of persistent, nonproductive cough, and progressive shortness of breath. She reported fatigue, chest tightness, orthopnea, and dyspnea with exertion. She denied fever, rhinorrhea, congestion, hemoptysis, or paroxysmal nocturnal dyspnea.

Her age and past medical history of asthma are incongruent with her new symptoms, as asthma is typified by intermittent exacerbations, not progressive symptoms. Thus, another process, in addition to asthma, is most likely present; it is also important to question the accuracy of previous diagnoses in light of new information. Her symptoms may signify an underlying cardiopulmonary process, such as infiltrative diseases (eg, lymphoma or sarcoidosis), atypical infections, genetic conditions (eg, variant cystic fibrosis), autoimmune conditions, or cardiomyopathy. A detailed symptom history, family history, and careful physical examination will help expand and then refine the differential diagnosis. At this stage, typical infections are less likely.

She had presented two months prior with nonproductive cough and dyspnea. At that presentation, her temperature was 36.3°C, heart rate 110 beats per minute, blood pressure 119/63 mm Hg, respiratory rate 43 breaths per minute, and oxygen saturation 86% while breathing ambient air. A chest CT with contrast demonstrated diffuse patchy multifocal ground-glass opacities in the bilateral lungs as well as a mixture of atelectasis and lobular emphysema in the dependent lobes bilaterally (Figure 1). Her main pulmonary artery was dilated at 3.6 cm (mean of 2.42 cm with SD 0.22). She was diagnosed with atypical pneumonia. She was administered azithromycin, weaned off oxygen, and discharged after a seven-day hospitalization.

Her white blood cell count was 11.9 x 10 ⁹ /L (67% neutrophils, 24.2% lymphocytes, 6% monocytes, and 2% eosinophils), hemoglobin 11.2 g/dL, and platelet count 278,000/mm ³ . Her complete metabolic panel was normal. The C-reactive protein (CRP) was 24 mg/L (normal range, < 4.9) and erythrocyte sedimentation rate (ESR) 103 mm/hour (normal range, 0-32). A venous blood gas (VBG) showed a pH of 7.42 and pCO2 39. An EKG demonstrated sinus tachycardia.

The combination of the patient’s tachypnea, hypoxemia, respiratory distress, and obesity is striking. Her lack of adventitious lung sounds is surprising given her CT findings, but the sensitivity of chest auscultation may be limited in obese patients. Her laboratory findings help narrow the diagnostic frame: she has mild anemia and leukocytosis along with significant inflammation. The normal CO₂ concentration on VBG is concerning given the degree of her tachypnea and reflects significant alveolar hypoventilation.

This marked inflammation with diffuse lung findings again raises the possibility of an inflammatory or, less likely, infectious disorder. Sjogren’s syndrome, systemic lupus erythematosus (SLE), and juvenile dermatomyositis can present in young women with interstitial lung disease. She does have exposure to pets and hypersensitivity pneumonitis can worsen rapidly with continued exposure. Another possibility is that she has an underlying immunodeficiency such as common variable immunodeficiency, although a history of recurrent infections such as pneumonia, bacteremia, or sinusitis is lacking.

An echocardiogram should be performed. In addition, laboratory evaluation for the aforementioned autoimmune causes of interstitial lung disease, immunoglobulin levels, pulmonary function testing (if available as an inpatient), and potentially a bronchoscopy with bronchoalveolar lavage (BAL), and biopsy should be pursued. The BAL and biopsy would be helpful in evaluating for infection and interstitial lung disease in an expeditious manner.

A chest CT without contrast was done and compared to the scan from two months prior. New diffuse, ill-defined centrilobular ground-glass opacities were evident throughout the lung fields; dilation of the main pulmonary artery was unchanged, and previously seen ground-glass opacities had resolved. There were patchy areas of air-trapping and mosaic attenuation in the lower lobes (Figure 2).

Transthoracic echocardiogram demonstrated a right ventricular systolic pressure of 58 mm Hg with flattened intraventricular septum during systole. Left and right ventricular systolic function were normal. The left ventricular diastolic function was normal. Pulmonary function testing demonstrated a FEV1/FVC ratio of 100 (112% predicted), FVC 1.07 L (35 % predicted) and FEV1 1.07 L (39% predicted), and total lung capacity was 2.7L (56% predicted) (Figure 3). Single-breath carbon monoxide uptake in the lung was not interpretable based on 2017 European Respiratory Society (ERS)/American Thoracic Society (ATS) technical standards.

Three weeks later, she returned to clinic for follow up. She had re-experienced dyspnea, cough, and wheezing, which improved when she was outdoors. She was afebrile, tachypneic, tachycardic, and her oxygen saturation was 92% on ambient air.

Her steroid-responsive interstitial lung disease and rapid improvement upon avoidance of the offending antigen is consistent with HP. The positive serum precipitins assay lends further credence to the diagnosis of HP, although serologic analysis with such antibody assays is limited by false positives and false negatives; further, individuals exposed to pigeons often have antibodies present without evidence of HP. History taking at this visit should ask specifically about further pigeon exposure: were the pigeons removed from the home completely, were heating-cooling filters changed, carpets cleaned, and bedding laundered? An in-home evaluation may be helpful before conducting further diagnostic testing.

She was admitted for oxygen therapy and a bronchoscopy, which showed mucosal friability and cobblestoning, suggesting inflammation. BAL revealed a normal CD4:CD8 ratio of 3; BAL cultures were sterile. Her shortness of breath significantly improved following a prolonged course of systemic steroids and removal from the triggering environment. PFTs improved with a FEV1/FVC ratio of 94 (105% predicted), FVC of 2.00 L (66% predicted), FEV1 of 1.88L (69% predicted) (Figure 3B). Her presenting symptoms of persistent cough and progressive dyspnea on exertion, characteristic CT, sterile BAL cultures, positive serum precipitants against pigeon serum, and resolution of her symptoms with withdrawal of the offending antigen were diagnostic of hypersensitivity pneumonitis due to pigeon exposure, also known as bird fancier’s disease.

The patient’s original presentation of dyspnea, tachypnea, and hypoxia is commonly associated with pediatric pneumonia and asthma exacerbations.¹ However, an alternative diagnosis was suggested by the lack of wheezing, absence of fever, and recurrent presentations with progressive symptoms.

Hypersensitivity pneumonitis (HP) represents an exaggerated T-cell meditated immune response to inhalation of an offending antigen that results in a restrictive ventilatory defect and interstitial infiltrates.² Bird pneumonitis (also known as bird fancier’s disease) is a frequent cause of HP, accounting for approximately 65-70% of cases.³ HP, however, only manifests in a small number of subjects exposed to culprit antigens, suggesting an underlying genetic susceptibility.⁴ Prevalence estimates vary depending on bird species, county, climate, and other possible factors.

There are no standard criteria for the diagnosis of HP, though a combination of findings is suggestive. A recent prospective multicenter study created a scoring system for HP based on factors associated with the disease to aid in accurate diagnosis. The most relevant criteria included antigen exposure, recurrent symptoms noted within 4-8 hours after antigen exposure, weight loss, presence of specific IgG antibodies to avian antigens, and inspiratory crackles on exam. Using this rule, the probability that our patient has HP based on clinical characteristics was 93% with an area under the receiver operating curve of 0.93 (96% confidence interval: 0.90-0.95)⁵. Chest imaging (high resolution CT) often consists of a mosaic pattern of air trapping, as seen in this patient in combination with ground-glass opacities⁶. Bronchoalveolar lavage (BAL) is sensitive in detecting lung inflammation in a patient with suspected HP. On BAL, a lymphocytic alveolitis can be seen, but absence of this finding does not exclude HP.^5,7,8 Pulmonary function tests (PFTs) may be normal in acute HP. When abnormal, PFTs may reveal a restrictive pattern and reduction in carbon monoxide diffusing capacity.⁷ However, BAL and PFT results are neither specific nor diagnostic of HP; it is important to consider results in the context of the clinical picture.

The respiratory response to inhalation of the avian antigen has traditionally been classified as acute, subacute, or chronic.⁹ The acute response occurs within hours of exposure to the offending agent and usually resolves within 24 hours after antigen withdrawal. The subacute presentation involves cough and dyspnea over several days to weeks, and can progress to chronic and permanent lung damage if unrecognized and untreated. In chronic presentations, lung abnormalities may persist despite antigen avoidance and pharmacologic interventions.^4,10 The patient’s symptoms occurred over a six-month period which coincided with pigeon exposure and resolved during each hospitalization with steroid treatment and removal from the offending agent. Her presentation was consistent with a subacute time course of HP.

The dilated pulmonary artery, elevated right systolic ventricular pressure, and normal right ventricular function in our patient suggested pulmonary hypertension of chronic duration. Her risk factors for pulmonary hypertension included asthma, sleep apnea, possible obesity-hypoventilation syndrome, and HP-associated interstitial lung disease.¹¹

The most important intervention in HP is avoidance of the causative antigen. Medical therapy without removal of antigen is inadequate. Systemic corticosteroids can help ameliorate acute symptoms though dosing and duration remains unclear. For chronic patients unresponsive to steroid therapy, lung transplantation can be considered.⁴

The key to diagnosis of HP in this patient—and to minimizing repeat testing upon the patient’s recrudescence of symptoms—was the clinician’s consideration that the major impetus for the patient’s improvement in the hospital was removal from the offending antigen in her home environment. As in this case, taking time to delve deeply into a patient’s environment—even by descending the basement stairs—may lead to the diagnosis.

• Consider hypersensitivity pneumonitis (HP) in patients with recurrent respiratory distress, offending exposure, and resolution of symptoms with removal of culprit antigen.
• The most important treatment of HP is removal of offending antigen; systemic and/or inhaled corticosteroids are indicated until the full resolution of respiratory symptoms.
• Prognosis is dependent on early diagnosis and removal of offending exposures.
• Failure to treat HP might result in end-stage lung disease from pulmonary fibrosis secondary to long-term inflammation.

Disclosures

Dr. Manesh is supported by the Jeremiah A. Barondess Fellowship in the Clinical Transaction of the New York Academy of Medicine, in collaboration with the Accreditation Council for Graduate Medical Education (ACGME). The authors declare no conflicts of interests.

A 14-year-old girl with a history of asthma presented to the Emergency Department (ED) with three months of persistent, nonproductive cough, and progressive shortness of breath. She reported fatigue, chest tightness, orthopnea, and dyspnea with exertion. She denied fever, rhinorrhea, congestion, hemoptysis, or paroxysmal nocturnal dyspnea.

Her age and past medical history of asthma are incongruent with her new symptoms, as asthma is typified by intermittent exacerbations, not progressive symptoms. Thus, another process, in addition to asthma, is most likely present; it is also important to question the accuracy of previous diagnoses in light of new information. Her symptoms may signify an underlying cardiopulmonary process, such as infiltrative diseases (eg, lymphoma or sarcoidosis), atypical infections, genetic conditions (eg, variant cystic fibrosis), autoimmune conditions, or cardiomyopathy. A detailed symptom history, family history, and careful physical examination will help expand and then refine the differential diagnosis. At this stage, typical infections are less likely.

She had presented two months prior with nonproductive cough and dyspnea. At that presentation, her temperature was 36.3°C, heart rate 110 beats per minute, blood pressure 119/63 mm Hg, respiratory rate 43 breaths per minute, and oxygen saturation 86% while breathing ambient air. A chest CT with contrast demonstrated diffuse patchy multifocal ground-glass opacities in the bilateral lungs as well as a mixture of atelectasis and lobular emphysema in the dependent lobes bilaterally (Figure 1). Her main pulmonary artery was dilated at 3.6 cm (mean of 2.42 cm with SD 0.22). She was diagnosed with atypical pneumonia. She was administered azithromycin, weaned off oxygen, and discharged after a seven-day hospitalization.

Her white blood cell count was 11.9 x 10 ⁹ /L (67% neutrophils, 24.2% lymphocytes, 6% monocytes, and 2% eosinophils), hemoglobin 11.2 g/dL, and platelet count 278,000/mm ³ . Her complete metabolic panel was normal. The C-reactive protein (CRP) was 24 mg/L (normal range, < 4.9) and erythrocyte sedimentation rate (ESR) 103 mm/hour (normal range, 0-32). A venous blood gas (VBG) showed a pH of 7.42 and pCO2 39. An EKG demonstrated sinus tachycardia.

The combination of the patient’s tachypnea, hypoxemia, respiratory distress, and obesity is striking. Her lack of adventitious lung sounds is surprising given her CT findings, but the sensitivity of chest auscultation may be limited in obese patients. Her laboratory findings help narrow the diagnostic frame: she has mild anemia and leukocytosis along with significant inflammation. The normal CO₂ concentration on VBG is concerning given the degree of her tachypnea and reflects significant alveolar hypoventilation.

This marked inflammation with diffuse lung findings again raises the possibility of an inflammatory or, less likely, infectious disorder. Sjogren’s syndrome, systemic lupus erythematosus (SLE), and juvenile dermatomyositis can present in young women with interstitial lung disease. She does have exposure to pets and hypersensitivity pneumonitis can worsen rapidly with continued exposure. Another possibility is that she has an underlying immunodeficiency such as common variable immunodeficiency, although a history of recurrent infections such as pneumonia, bacteremia, or sinusitis is lacking.

An echocardiogram should be performed. In addition, laboratory evaluation for the aforementioned autoimmune causes of interstitial lung disease, immunoglobulin levels, pulmonary function testing (if available as an inpatient), and potentially a bronchoscopy with bronchoalveolar lavage (BAL), and biopsy should be pursued. The BAL and biopsy would be helpful in evaluating for infection and interstitial lung disease in an expeditious manner.

A chest CT without contrast was done and compared to the scan from two months prior. New diffuse, ill-defined centrilobular ground-glass opacities were evident throughout the lung fields; dilation of the main pulmonary artery was unchanged, and previously seen ground-glass opacities had resolved. There were patchy areas of air-trapping and mosaic attenuation in the lower lobes (Figure 2).

Transthoracic echocardiogram demonstrated a right ventricular systolic pressure of 58 mm Hg with flattened intraventricular septum during systole. Left and right ventricular systolic function were normal. The left ventricular diastolic function was normal. Pulmonary function testing demonstrated a FEV1/FVC ratio of 100 (112% predicted), FVC 1.07 L (35 % predicted) and FEV1 1.07 L (39% predicted), and total lung capacity was 2.7L (56% predicted) (Figure 3). Single-breath carbon monoxide uptake in the lung was not interpretable based on 2017 European Respiratory Society (ERS)/American Thoracic Society (ATS) technical standards.

Three weeks later, she returned to clinic for follow up. She had re-experienced dyspnea, cough, and wheezing, which improved when she was outdoors. She was afebrile, tachypneic, tachycardic, and her oxygen saturation was 92% on ambient air.

Her steroid-responsive interstitial lung disease and rapid improvement upon avoidance of the offending antigen is consistent with HP. The positive serum precipitins assay lends further credence to the diagnosis of HP, although serologic analysis with such antibody assays is limited by false positives and false negatives; further, individuals exposed to pigeons often have antibodies present without evidence of HP. History taking at this visit should ask specifically about further pigeon exposure: were the pigeons removed from the home completely, were heating-cooling filters changed, carpets cleaned, and bedding laundered? An in-home evaluation may be helpful before conducting further diagnostic testing.

She was admitted for oxygen therapy and a bronchoscopy, which showed mucosal friability and cobblestoning, suggesting inflammation. BAL revealed a normal CD4:CD8 ratio of 3; BAL cultures were sterile. Her shortness of breath significantly improved following a prolonged course of systemic steroids and removal from the triggering environment. PFTs improved with a FEV1/FVC ratio of 94 (105% predicted), FVC of 2.00 L (66% predicted), FEV1 of 1.88L (69% predicted) (Figure 3B). Her presenting symptoms of persistent cough and progressive dyspnea on exertion, characteristic CT, sterile BAL cultures, positive serum precipitants against pigeon serum, and resolution of her symptoms with withdrawal of the offending antigen were diagnostic of hypersensitivity pneumonitis due to pigeon exposure, also known as bird fancier’s disease.

The patient’s original presentation of dyspnea, tachypnea, and hypoxia is commonly associated with pediatric pneumonia and asthma exacerbations.¹ However, an alternative diagnosis was suggested by the lack of wheezing, absence of fever, and recurrent presentations with progressive symptoms.

Hypersensitivity pneumonitis (HP) represents an exaggerated T-cell meditated immune response to inhalation of an offending antigen that results in a restrictive ventilatory defect and interstitial infiltrates.² Bird pneumonitis (also known as bird fancier’s disease) is a frequent cause of HP, accounting for approximately 65-70% of cases.³ HP, however, only manifests in a small number of subjects exposed to culprit antigens, suggesting an underlying genetic susceptibility.⁴ Prevalence estimates vary depending on bird species, county, climate, and other possible factors.

There are no standard criteria for the diagnosis of HP, though a combination of findings is suggestive. A recent prospective multicenter study created a scoring system for HP based on factors associated with the disease to aid in accurate diagnosis. The most relevant criteria included antigen exposure, recurrent symptoms noted within 4-8 hours after antigen exposure, weight loss, presence of specific IgG antibodies to avian antigens, and inspiratory crackles on exam. Using this rule, the probability that our patient has HP based on clinical characteristics was 93% with an area under the receiver operating curve of 0.93 (96% confidence interval: 0.90-0.95)⁵. Chest imaging (high resolution CT) often consists of a mosaic pattern of air trapping, as seen in this patient in combination with ground-glass opacities⁶. Bronchoalveolar lavage (BAL) is sensitive in detecting lung inflammation in a patient with suspected HP. On BAL, a lymphocytic alveolitis can be seen, but absence of this finding does not exclude HP.^5,7,8 Pulmonary function tests (PFTs) may be normal in acute HP. When abnormal, PFTs may reveal a restrictive pattern and reduction in carbon monoxide diffusing capacity.⁷ However, BAL and PFT results are neither specific nor diagnostic of HP; it is important to consider results in the context of the clinical picture.

The respiratory response to inhalation of the avian antigen has traditionally been classified as acute, subacute, or chronic.⁹ The acute response occurs within hours of exposure to the offending agent and usually resolves within 24 hours after antigen withdrawal. The subacute presentation involves cough and dyspnea over several days to weeks, and can progress to chronic and permanent lung damage if unrecognized and untreated. In chronic presentations, lung abnormalities may persist despite antigen avoidance and pharmacologic interventions.^4,10 The patient’s symptoms occurred over a six-month period which coincided with pigeon exposure and resolved during each hospitalization with steroid treatment and removal from the offending agent. Her presentation was consistent with a subacute time course of HP.

The dilated pulmonary artery, elevated right systolic ventricular pressure, and normal right ventricular function in our patient suggested pulmonary hypertension of chronic duration. Her risk factors for pulmonary hypertension included asthma, sleep apnea, possible obesity-hypoventilation syndrome, and HP-associated interstitial lung disease.¹¹

The most important intervention in HP is avoidance of the causative antigen. Medical therapy without removal of antigen is inadequate. Systemic corticosteroids can help ameliorate acute symptoms though dosing and duration remains unclear. For chronic patients unresponsive to steroid therapy, lung transplantation can be considered.⁴

The key to diagnosis of HP in this patient—and to minimizing repeat testing upon the patient’s recrudescence of symptoms—was the clinician’s consideration that the major impetus for the patient’s improvement in the hospital was removal from the offending antigen in her home environment. As in this case, taking time to delve deeply into a patient’s environment—even by descending the basement stairs—may lead to the diagnosis.

• Consider hypersensitivity pneumonitis (HP) in patients with recurrent respiratory distress, offending exposure, and resolution of symptoms with removal of culprit antigen.
• The most important treatment of HP is removal of offending antigen; systemic and/or inhaled corticosteroids are indicated until the full resolution of respiratory symptoms.
• Prognosis is dependent on early diagnosis and removal of offending exposures.
• Failure to treat HP might result in end-stage lung disease from pulmonary fibrosis secondary to long-term inflammation.

Disclosures

Dr. Manesh is supported by the Jeremiah A. Barondess Fellowship in the Clinical Transaction of the New York Academy of Medicine, in collaboration with the Accreditation Council for Graduate Medical Education (ACGME). The authors declare no conflicts of interests.

A 14-year-old girl with a history of asthma presented to the Emergency Department (ED) with three months of persistent, nonproductive cough, and progressive shortness of breath. She reported fatigue, chest tightness, orthopnea, and dyspnea with exertion. She denied fever, rhinorrhea, congestion, hemoptysis, or paroxysmal nocturnal dyspnea.

Her age and past medical history of asthma are incongruent with her new symptoms, as asthma is typified by intermittent exacerbations, not progressive symptoms. Thus, another process, in addition to asthma, is most likely present; it is also important to question the accuracy of previous diagnoses in light of new information. Her symptoms may signify an underlying cardiopulmonary process, such as infiltrative diseases (eg, lymphoma or sarcoidosis), atypical infections, genetic conditions (eg, variant cystic fibrosis), autoimmune conditions, or cardiomyopathy. A detailed symptom history, family history, and careful physical examination will help expand and then refine the differential diagnosis. At this stage, typical infections are less likely.

She had presented two months prior with nonproductive cough and dyspnea. At that presentation, her temperature was 36.3°C, heart rate 110 beats per minute, blood pressure 119/63 mm Hg, respiratory rate 43 breaths per minute, and oxygen saturation 86% while breathing ambient air. A chest CT with contrast demonstrated diffuse patchy multifocal ground-glass opacities in the bilateral lungs as well as a mixture of atelectasis and lobular emphysema in the dependent lobes bilaterally (Figure 1). Her main pulmonary artery was dilated at 3.6 cm (mean of 2.42 cm with SD 0.22). She was diagnosed with atypical pneumonia. She was administered azithromycin, weaned off oxygen, and discharged after a seven-day hospitalization.

Her white blood cell count was 11.9 x 10 ⁹ /L (67% neutrophils, 24.2% lymphocytes, 6% monocytes, and 2% eosinophils), hemoglobin 11.2 g/dL, and platelet count 278,000/mm ³ . Her complete metabolic panel was normal. The C-reactive protein (CRP) was 24 mg/L (normal range, < 4.9) and erythrocyte sedimentation rate (ESR) 103 mm/hour (normal range, 0-32). A venous blood gas (VBG) showed a pH of 7.42 and pCO2 39. An EKG demonstrated sinus tachycardia.

The combination of the patient’s tachypnea, hypoxemia, respiratory distress, and obesity is striking. Her lack of adventitious lung sounds is surprising given her CT findings, but the sensitivity of chest auscultation may be limited in obese patients. Her laboratory findings help narrow the diagnostic frame: she has mild anemia and leukocytosis along with significant inflammation. The normal CO₂ concentration on VBG is concerning given the degree of her tachypnea and reflects significant alveolar hypoventilation.

This marked inflammation with diffuse lung findings again raises the possibility of an inflammatory or, less likely, infectious disorder. Sjogren’s syndrome, systemic lupus erythematosus (SLE), and juvenile dermatomyositis can present in young women with interstitial lung disease. She does have exposure to pets and hypersensitivity pneumonitis can worsen rapidly with continued exposure. Another possibility is that she has an underlying immunodeficiency such as common variable immunodeficiency, although a history of recurrent infections such as pneumonia, bacteremia, or sinusitis is lacking.

An echocardiogram should be performed. In addition, laboratory evaluation for the aforementioned autoimmune causes of interstitial lung disease, immunoglobulin levels, pulmonary function testing (if available as an inpatient), and potentially a bronchoscopy with bronchoalveolar lavage (BAL), and biopsy should be pursued. The BAL and biopsy would be helpful in evaluating for infection and interstitial lung disease in an expeditious manner.

A chest CT without contrast was done and compared to the scan from two months prior. New diffuse, ill-defined centrilobular ground-glass opacities were evident throughout the lung fields; dilation of the main pulmonary artery was unchanged, and previously seen ground-glass opacities had resolved. There were patchy areas of air-trapping and mosaic attenuation in the lower lobes (Figure 2).

Transthoracic echocardiogram demonstrated a right ventricular systolic pressure of 58 mm Hg with flattened intraventricular septum during systole. Left and right ventricular systolic function were normal. The left ventricular diastolic function was normal. Pulmonary function testing demonstrated a FEV1/FVC ratio of 100 (112% predicted), FVC 1.07 L (35 % predicted) and FEV1 1.07 L (39% predicted), and total lung capacity was 2.7L (56% predicted) (Figure 3). Single-breath carbon monoxide uptake in the lung was not interpretable based on 2017 European Respiratory Society (ERS)/American Thoracic Society (ATS) technical standards.

Three weeks later, she returned to clinic for follow up. She had re-experienced dyspnea, cough, and wheezing, which improved when she was outdoors. She was afebrile, tachypneic, tachycardic, and her oxygen saturation was 92% on ambient air.

Her steroid-responsive interstitial lung disease and rapid improvement upon avoidance of the offending antigen is consistent with HP. The positive serum precipitins assay lends further credence to the diagnosis of HP, although serologic analysis with such antibody assays is limited by false positives and false negatives; further, individuals exposed to pigeons often have antibodies present without evidence of HP. History taking at this visit should ask specifically about further pigeon exposure: were the pigeons removed from the home completely, were heating-cooling filters changed, carpets cleaned, and bedding laundered? An in-home evaluation may be helpful before conducting further diagnostic testing.

She was admitted for oxygen therapy and a bronchoscopy, which showed mucosal friability and cobblestoning, suggesting inflammation. BAL revealed a normal CD4:CD8 ratio of 3; BAL cultures were sterile. Her shortness of breath significantly improved following a prolonged course of systemic steroids and removal from the triggering environment. PFTs improved with a FEV1/FVC ratio of 94 (105% predicted), FVC of 2.00 L (66% predicted), FEV1 of 1.88L (69% predicted) (Figure 3B). Her presenting symptoms of persistent cough and progressive dyspnea on exertion, characteristic CT, sterile BAL cultures, positive serum precipitants against pigeon serum, and resolution of her symptoms with withdrawal of the offending antigen were diagnostic of hypersensitivity pneumonitis due to pigeon exposure, also known as bird fancier’s disease.

The patient’s original presentation of dyspnea, tachypnea, and hypoxia is commonly associated with pediatric pneumonia and asthma exacerbations.¹ However, an alternative diagnosis was suggested by the lack of wheezing, absence of fever, and recurrent presentations with progressive symptoms.

Hypersensitivity pneumonitis (HP) represents an exaggerated T-cell meditated immune response to inhalation of an offending antigen that results in a restrictive ventilatory defect and interstitial infiltrates.² Bird pneumonitis (also known as bird fancier’s disease) is a frequent cause of HP, accounting for approximately 65-70% of cases.³ HP, however, only manifests in a small number of subjects exposed to culprit antigens, suggesting an underlying genetic susceptibility.⁴ Prevalence estimates vary depending on bird species, county, climate, and other possible factors.

There are no standard criteria for the diagnosis of HP, though a combination of findings is suggestive. A recent prospective multicenter study created a scoring system for HP based on factors associated with the disease to aid in accurate diagnosis. The most relevant criteria included antigen exposure, recurrent symptoms noted within 4-8 hours after antigen exposure, weight loss, presence of specific IgG antibodies to avian antigens, and inspiratory crackles on exam. Using this rule, the probability that our patient has HP based on clinical characteristics was 93% with an area under the receiver operating curve of 0.93 (96% confidence interval: 0.90-0.95)⁵. Chest imaging (high resolution CT) often consists of a mosaic pattern of air trapping, as seen in this patient in combination with ground-glass opacities⁶. Bronchoalveolar lavage (BAL) is sensitive in detecting lung inflammation in a patient with suspected HP. On BAL, a lymphocytic alveolitis can be seen, but absence of this finding does not exclude HP.^5,7,8 Pulmonary function tests (PFTs) may be normal in acute HP. When abnormal, PFTs may reveal a restrictive pattern and reduction in carbon monoxide diffusing capacity.⁷ However, BAL and PFT results are neither specific nor diagnostic of HP; it is important to consider results in the context of the clinical picture.

The respiratory response to inhalation of the avian antigen has traditionally been classified as acute, subacute, or chronic.⁹ The acute response occurs within hours of exposure to the offending agent and usually resolves within 24 hours after antigen withdrawal. The subacute presentation involves cough and dyspnea over several days to weeks, and can progress to chronic and permanent lung damage if unrecognized and untreated. In chronic presentations, lung abnormalities may persist despite antigen avoidance and pharmacologic interventions.^4,10 The patient’s symptoms occurred over a six-month period which coincided with pigeon exposure and resolved during each hospitalization with steroid treatment and removal from the offending agent. Her presentation was consistent with a subacute time course of HP.

The dilated pulmonary artery, elevated right systolic ventricular pressure, and normal right ventricular function in our patient suggested pulmonary hypertension of chronic duration. Her risk factors for pulmonary hypertension included asthma, sleep apnea, possible obesity-hypoventilation syndrome, and HP-associated interstitial lung disease.¹¹

The most important intervention in HP is avoidance of the causative antigen. Medical therapy without removal of antigen is inadequate. Systemic corticosteroids can help ameliorate acute symptoms though dosing and duration remains unclear. For chronic patients unresponsive to steroid therapy, lung transplantation can be considered.⁴

The key to diagnosis of HP in this patient—and to minimizing repeat testing upon the patient’s recrudescence of symptoms—was the clinician’s consideration that the major impetus for the patient’s improvement in the hospital was removal from the offending antigen in her home environment. As in this case, taking time to delve deeply into a patient’s environment—even by descending the basement stairs—may lead to the diagnosis.

• Consider hypersensitivity pneumonitis (HP) in patients with recurrent respiratory distress, offending exposure, and resolution of symptoms with removal of culprit antigen.
• The most important treatment of HP is removal of offending antigen; systemic and/or inhaled corticosteroids are indicated until the full resolution of respiratory symptoms.
• Prognosis is dependent on early diagnosis and removal of offending exposures.
• Failure to treat HP might result in end-stage lung disease from pulmonary fibrosis secondary to long-term inflammation.

Disclosures

Dr. Manesh is supported by the Jeremiah A. Barondess Fellowship in the Clinical Transaction of the New York Academy of Medicine, in collaboration with the Accreditation Council for Graduate Medical Education (ACGME). The authors declare no conflicts of interests.

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.