Jeffrey Schnipper, MD, MPH

The coronavirus disease of 2019 (COVID-19) pandemic has resulted in a surge in hospitalizations of patients with a novel, serious, and highly contagious infectious disease for which there is yet no proven treatment. Currently, much of the focus has been on intensive care unit (ICU) and ventilator capacity for the sickest of these patients who develop respiratory failure. However, most hospitalized patients are being cared for in general medical units.¹ Some evidence exists to describe adaptations to capacity needs outside of medical wards,^2-4 but few studies have specifically addressed the ward setting. Therefore, there is a pressing need for evidence to describe how to expand capacity and deliver medical ward–based care.

To better understand how inpatient care in the United States is adapting to the COVID-19 pandemic, we surveyed 72 sites participating in the Hospital Medicine Reengineering Network (HOMERuN), a national consortium of hospital medicine groups.⁵ We report results of this survey, carried out between April 3 and April 5, 2020.

Sites and Subjects

HOMERuN is a collaborative network of hospitalists from across the United States whose primary goal is to catalyze research and share best practices across hospital medicine groups. Using surveys of Hospital Medicine leaders, targeted medical record review, and other methods, HOMERuN’s funded research interests to date have included care transitions, workforce issues, patient and family engagement, and diagnostic errors. Sites participating in HOMERuN sites are relatively large urban academic medical centers (Appendix).

Survey Development and Deployment

We designed a focused survey that aimed to provide a snapshot of evolving operational and clinical aspects of COVID-19 care (Appendix). Domains included COVID-19 testing turnaround times, personal protective equipment (PPE) stewardship,⁶ features of respiratory isolation units (RIUs; ie, dedicated units for patients with known or suspected COVID-19), and observed effects on clinical care. We tested the instrument to ensure feasibility and clarity internally, performed brief cognitive testing with several hospital medicine leaders in HOMERuN, then disseminated the survey by email on April 3, with two follow-up emails on 2 subsequent days. Our study was deemed non–human subjects research by the University of California, San Francisco, Committee on Human Research. Descriptive statistics were used to characterize survey responses.

Of 72 hospitals surveyed, 51 (71%) responded. Mean hospital bed count was 940, three were safety-net hospitals, and one was a community-based teaching center; responding and nonresponding hospitals did not differ significantly in terms of bed count (Appendix).

Health System Adaptations, Testing, and PPE Status

Nearly all responding hospitals (46 of 51; 90%) had RIUs for patients with known or suspected COVID-19 (Table 1). Nearly all hospitals took steps to keep potentially sick healthcare providers from infecting others (eg, staying home if sick or exposed). Among respondents, 32% had rapid response teams, 24% had respiratory therapy teams, and 29% had case management teams that were dedicated to COVID-19 care. Thirty-two (63%) had developed models, such as ethics or palliative care consult services, to assist with difficult resource-allocation decisions (eg, how to prioritize ventilator use if demand exceeded supply). Twenty-three (45%) had developed post-acute care monitoring programs dedicated to COVID-19 patients.

auerbach03660722e_t1.jpg

At the time of our survey, only 2 sites (4%) reported COVID-19 test time turnaround under 1 hour, and 15 (30%) reported turnaround in less than 6 hours. Of the 29 sites able to provide estimates of PPE stockpile, 14 (48%) reported a supply of 2 weeks or less. The most common approaches to PPE stewardship focused on reuse of masks and face shields if not obviously soiled, centralizing PPE distribution, and disinfecting or sterilizing masks. Ten sites (20%) were utilizing 3-D printed masks, while 10% used homemade face shields or masks.

Characteristics of COVID-19 RIUs

Forty-six hospitals (90% of all respondents) in our cohort had developed RIUs at the time of survey administration. The earliest RIU implementation date was February 10, 2020, and the most recent was launched on the day of our survey. Admission to RIUs was primarily based on clinical factors associated with known or suspected COVID-19 infection (Table 2). The number of non–critical care RIU beds among locations at that time ranged from 10 or less to more than 50. The mean number of hospitalist attendings caring for patients in the RIUs was 10.2, with a mean 4.1 advanced practice providers, 5.5 residents, and 0 medical students. The number of planned patients per attending was typically 5 to 15. Nurses and physicians typically rounded separately. Medical distancing (eg, reducing patient room entry) was accomplished most commonly by grouped timing of medication administration (76% of sites), video links to room outside of rounding times (54% of sites), the use of video or telemedicine during rounds (17%), and clustering of activities such as medication administration or phlebotomy. The most common criteria prompting discharge from the RIU were a negative COVID-19 test (59%) and hospital discharge (57%), though comments from many respondents suggested that discharge criteria were changing rapidly.

auerbach03660722e_t2.jpg

Effects of Isolation Measures on In-Room Encounters and Diagnostic Processes

More than 90% of sites reported decreases in in-room encounter frequency across all provider types whether as a result of policies in place or not. Reductions were reported among hospitalists, advanced practice providers, residents, consultants, and therapists (Table 3). Reduced room entry most often resulted from an established or developing policy, but many noted reduced room entry without formal policies in place. Nearly all sites reported moving specialty consultations to phone or video evaluations. Diagnostic error was commonly reported, with missed non–COVID-19 medical diagnoses among COVID-19 infected patients being reported by 22 sites (46%) and missed COVID-19 diagnoses in patients admitted for other reasons by 22 sites (45%).

auerbach03660722e_t3.jpg

In this study of medical wards at academic medical centers, we found that, in response to the COVID-19 pandemic, hospitals made several changes in a short period of time to adapt to the crisis. These included implementation and rapid expansion of dedicated RIUs, greatly expanded use of inpatient telehealth for patient assessments and consultation, implementation of other approaches to minimize room entry (such as grouping in-room activities), and deployment of ethics consultation services to help manage issues around potential scarcity of life-saving measures such as ventilators. We also found that availability of PPE and timely testing was limited. Finally, a large proportion of sites reported potential diagnostic problems in the assessment of both patients suspected and those not suspected of having COVID-19.

RIUs are emerging as a primary modality for caring for non-ICU COVID-19 patients, though they never involved medical students; we hope the role of students in particular will increase as new models of training emerge in response to the pandemic.⁷ In contrast, telemedicine evolved rapidly to hold a substantial role in RIUs, with both ward and specialty teams using video visit technology to communicate with patients. COVID-19 has been viewed as a perfect use case for outpatient telemedicine,⁸ and a growing number of studies are examining its outpatient use^9,10; however, to date, somewhat less attention has been paid to inpatient deployment. Although our data suggest telemedicine has found a prominent place in RIUs, it remains to be seen whether it is associated with differences in patient or provider outcomes. For example, deficiencies in the physical examination, limited face-to-face contact, and lack of physical presence could all affect the patient–provider relationship, patient engagement, and the accuracy of the diagnostic process.

Our data suggest the possibility of missing non–COVID-19 diagnoses in patients suspected of COVID-19 and missing COVID-19 in those admitted for nonrespiratory reasons. The latter may be addressed as routine COVID-19 screening of admitted patients becomes commonplace. For the former, however, it is possible that physicians are “anchoring” their thinking on COVID-19 to the exclusion of other diagnoses, that physicians are not fully aware of complications unique to COVID-19 infection (such as thromboembolism), and/or that the above-mentioned limitations of telemedicine have decreased diagnostic performance.

Although PPE stockpile data were not easily available for some sites, a distressingly large number reported stockpiles of 2 weeks or less, with reuse being the most common approach to extending PPE supply. We also found it concerning that 43% of hospital leaders did not know their stockpile data; we believe this is an important question that hospital leaders need to be asking. Most sites in our study reported test turnaround times of longer than 6 hours; lack of rapid COVID-19 testing further stresses PPE stockpile and may slow patients’ transition out of the RIU or discharge to home.

Our study has several limitations, including the evolving nature of the pandemic and rapid adaptations of care systems in the pandemic’s surge phase. However, we attempted to frame our questions in ways that provided a focused snapshot of care. Furthermore, respondents may not have had exhaustive knowledge of their institution’s COVID-19 response strategies, but most were the directors of their hospitalist services, and we encouraged the respondents to confer with others to gather high-fidelity data. Finally, as a survey of large academic medical centers, our results may not apply to nonacademic centers.

Approaches to caring for non-ICU patients during the COVID-19 pandemic are rapidly evolving. Expansion of RIUs and developing the workforce to support them has been a primary focus, with rapid innovation in use of technology emerging as a critical adaptation while PPE limitations persist and needs for “medical distancing” continue to grow. Although rates of missed COVID-19 diagnoses will likely be reduced with testing and systems improvements, physicians and systems will also need to consider how to utilize emerging technology in ways that can improve clinical care and provider safety while aiding diagnostic thinking. This survey illustrates the rapid adaptations made by our hospitals in response to the pandemic; ongoing adaptation will likely be needed to optimally care for hospitalized patients with COVID-19 while the pandemic continues to evolve.

Thanks to members of the HOMERuN COVID-19 Collaborative Group: Baylor Scott & White Medical Center – Temple, Texas - Tresa McNeal MD; Beth Israel Deaconess Medical Center - Shani Herzig MD MPH, Joseph Li MD, Julius Yang MD PhD; Brigham and Women’s Hospital - Christopher Roy MD, Jeffrey Schnipper MD MPH; Cedars-Sinai Medical Center - Ed Seferian MD, ; ChristianaCare - Surekha Bhamidipati MD; Cleveland Clinic - Matthew Pappas MD MPH; Dartmouth-Hitchcock Medical Center - Jonathan Lurie MD MS; Dell Medical School at The University of Texas at Austin - Chris Moriates MD, Luci Leykum MD MBA MSc; Denver Health and Hospitals Authority - Diana Mancini MD; Emory University Hospital - Dan Hunt MD; Johns Hopkins Hospital - Daniel J Brotman MD, Zishan K Siddiqui MD, Shaker Eid MD MBA; Maine Medical Center - Daniel A Meyer MD, Robert Trowbridge MD; Massachusetts General Hospital - Melissa Mattison MD; Mayo Clinic Rochester – Caroline Burton MD, Sagar Dugani MD PhD; Medical College of Wisconsin - Sanjay Bhandari MD; Miriam Hospital - Kwame Dapaah-Afriyie MD MBA; Mount Sinai Hospital - Andrew Dunn MD; NorthShore - David Lovinger MD; Northwestern Memorial Hospital - Kevin O’Leary MD MS; Ohio State University Wexner Medical Center - Eric Schumacher DO; Oregon Health & Science University - Angela Alday MD; Penn Medicine - Ryan Greysen MD MHS MA; Rutgers- Robert Wood Johnson University Hospital - Michael Steinberg MD MPH; Stanford University School of Medicine - Neera Ahuja MD; Tulane Hospital and University Medical Center - Geraldine Ménard MD; UC San Diego Health - Ian Jenkins MD; UC Los Angeles Health - Michael Lazarus MD, Magdalena E. Ptaszny, MD; UC San Francisco Health - Bradley A Sharpe, MD, Margaret Fang MD MPH; UK HealthCare - Mark Williams MD MHM, John Romond MD; University of Chicago – David Meltzer MD PhD, Gregory Ruhnke MD; University of Colorado - Marisha Burden MD; University of Florida - Nila Radhakrishnan MD; University of Iowa Hospitals and Clinics - Kevin Glenn MD MS; University of Miami - Efren Manjarrez MD; University of Michigan - Vineet Chopra MD MSc, Valerie Vaughn MD MSc; University of Missouri-Columbia Hospital - Hasan Naqvi MD; University of Nebraska Medical Center - Chad Vokoun MD; University of North Carolina at Chapel Hill - David Hemsey MD; University of Pittsburgh Medical Center - Gena Marie Walker MD; University of Vermont Medical Center - Steven Grant MD; University of Washington Medical Center - Christopher Kim MD MBA, Andrew White MD; University of Washington-Harborview Medical Center - Maralyssa Bann MD; University of Wisconsin Hospital and Clinics - David Sterken MD, Farah Kaiksow MD MPP, Ann Sheehy MD MS, Jordan Kenik MD MPH; UW Northwest Campus - Ben Wolpaw MD; Vanderbilt University Medical Center - Sunil Kripalani MD MSc, Eduard E Vasilevskis MD, Kathleene T Wooldridge MD MPH; Wake Forest Baptist Health - Erik Summers MD; Washington University St. Louis - Michael Lin MD; Weill Cornell - Justin Choi MD; Yale New Haven Hospital - William Cushing MA, Chris Sankey MD; Zuckerberg San Francisco General Hospital - Sumant Ranji MD.

The coronavirus disease of 2019 (COVID-19) pandemic has resulted in a surge in hospitalizations of patients with a novel, serious, and highly contagious infectious disease for which there is yet no proven treatment. Currently, much of the focus has been on intensive care unit (ICU) and ventilator capacity for the sickest of these patients who develop respiratory failure. However, most hospitalized patients are being cared for in general medical units.¹ Some evidence exists to describe adaptations to capacity needs outside of medical wards,^2-4 but few studies have specifically addressed the ward setting. Therefore, there is a pressing need for evidence to describe how to expand capacity and deliver medical ward–based care.

To better understand how inpatient care in the United States is adapting to the COVID-19 pandemic, we surveyed 72 sites participating in the Hospital Medicine Reengineering Network (HOMERuN), a national consortium of hospital medicine groups.⁵ We report results of this survey, carried out between April 3 and April 5, 2020.

Sites and Subjects

HOMERuN is a collaborative network of hospitalists from across the United States whose primary goal is to catalyze research and share best practices across hospital medicine groups. Using surveys of Hospital Medicine leaders, targeted medical record review, and other methods, HOMERuN’s funded research interests to date have included care transitions, workforce issues, patient and family engagement, and diagnostic errors. Sites participating in HOMERuN sites are relatively large urban academic medical centers (Appendix).

Survey Development and Deployment

We designed a focused survey that aimed to provide a snapshot of evolving operational and clinical aspects of COVID-19 care (Appendix). Domains included COVID-19 testing turnaround times, personal protective equipment (PPE) stewardship,⁶ features of respiratory isolation units (RIUs; ie, dedicated units for patients with known or suspected COVID-19), and observed effects on clinical care. We tested the instrument to ensure feasibility and clarity internally, performed brief cognitive testing with several hospital medicine leaders in HOMERuN, then disseminated the survey by email on April 3, with two follow-up emails on 2 subsequent days. Our study was deemed non–human subjects research by the University of California, San Francisco, Committee on Human Research. Descriptive statistics were used to characterize survey responses.

Of 72 hospitals surveyed, 51 (71%) responded. Mean hospital bed count was 940, three were safety-net hospitals, and one was a community-based teaching center; responding and nonresponding hospitals did not differ significantly in terms of bed count (Appendix).

Health System Adaptations, Testing, and PPE Status

Nearly all responding hospitals (46 of 51; 90%) had RIUs for patients with known or suspected COVID-19 (Table 1). Nearly all hospitals took steps to keep potentially sick healthcare providers from infecting others (eg, staying home if sick or exposed). Among respondents, 32% had rapid response teams, 24% had respiratory therapy teams, and 29% had case management teams that were dedicated to COVID-19 care. Thirty-two (63%) had developed models, such as ethics or palliative care consult services, to assist with difficult resource-allocation decisions (eg, how to prioritize ventilator use if demand exceeded supply). Twenty-three (45%) had developed post-acute care monitoring programs dedicated to COVID-19 patients.

auerbach03660722e_t1.jpg

At the time of our survey, only 2 sites (4%) reported COVID-19 test time turnaround under 1 hour, and 15 (30%) reported turnaround in less than 6 hours. Of the 29 sites able to provide estimates of PPE stockpile, 14 (48%) reported a supply of 2 weeks or less. The most common approaches to PPE stewardship focused on reuse of masks and face shields if not obviously soiled, centralizing PPE distribution, and disinfecting or sterilizing masks. Ten sites (20%) were utilizing 3-D printed masks, while 10% used homemade face shields or masks.

Characteristics of COVID-19 RIUs

Forty-six hospitals (90% of all respondents) in our cohort had developed RIUs at the time of survey administration. The earliest RIU implementation date was February 10, 2020, and the most recent was launched on the day of our survey. Admission to RIUs was primarily based on clinical factors associated with known or suspected COVID-19 infection (Table 2). The number of non–critical care RIU beds among locations at that time ranged from 10 or less to more than 50. The mean number of hospitalist attendings caring for patients in the RIUs was 10.2, with a mean 4.1 advanced practice providers, 5.5 residents, and 0 medical students. The number of planned patients per attending was typically 5 to 15. Nurses and physicians typically rounded separately. Medical distancing (eg, reducing patient room entry) was accomplished most commonly by grouped timing of medication administration (76% of sites), video links to room outside of rounding times (54% of sites), the use of video or telemedicine during rounds (17%), and clustering of activities such as medication administration or phlebotomy. The most common criteria prompting discharge from the RIU were a negative COVID-19 test (59%) and hospital discharge (57%), though comments from many respondents suggested that discharge criteria were changing rapidly.

auerbach03660722e_t2.jpg

Effects of Isolation Measures on In-Room Encounters and Diagnostic Processes

More than 90% of sites reported decreases in in-room encounter frequency across all provider types whether as a result of policies in place or not. Reductions were reported among hospitalists, advanced practice providers, residents, consultants, and therapists (Table 3). Reduced room entry most often resulted from an established or developing policy, but many noted reduced room entry without formal policies in place. Nearly all sites reported moving specialty consultations to phone or video evaluations. Diagnostic error was commonly reported, with missed non–COVID-19 medical diagnoses among COVID-19 infected patients being reported by 22 sites (46%) and missed COVID-19 diagnoses in patients admitted for other reasons by 22 sites (45%).

auerbach03660722e_t3.jpg

In this study of medical wards at academic medical centers, we found that, in response to the COVID-19 pandemic, hospitals made several changes in a short period of time to adapt to the crisis. These included implementation and rapid expansion of dedicated RIUs, greatly expanded use of inpatient telehealth for patient assessments and consultation, implementation of other approaches to minimize room entry (such as grouping in-room activities), and deployment of ethics consultation services to help manage issues around potential scarcity of life-saving measures such as ventilators. We also found that availability of PPE and timely testing was limited. Finally, a large proportion of sites reported potential diagnostic problems in the assessment of both patients suspected and those not suspected of having COVID-19.

RIUs are emerging as a primary modality for caring for non-ICU COVID-19 patients, though they never involved medical students; we hope the role of students in particular will increase as new models of training emerge in response to the pandemic.⁷ In contrast, telemedicine evolved rapidly to hold a substantial role in RIUs, with both ward and specialty teams using video visit technology to communicate with patients. COVID-19 has been viewed as a perfect use case for outpatient telemedicine,⁸ and a growing number of studies are examining its outpatient use^9,10; however, to date, somewhat less attention has been paid to inpatient deployment. Although our data suggest telemedicine has found a prominent place in RIUs, it remains to be seen whether it is associated with differences in patient or provider outcomes. For example, deficiencies in the physical examination, limited face-to-face contact, and lack of physical presence could all affect the patient–provider relationship, patient engagement, and the accuracy of the diagnostic process.

Our data suggest the possibility of missing non–COVID-19 diagnoses in patients suspected of COVID-19 and missing COVID-19 in those admitted for nonrespiratory reasons. The latter may be addressed as routine COVID-19 screening of admitted patients becomes commonplace. For the former, however, it is possible that physicians are “anchoring” their thinking on COVID-19 to the exclusion of other diagnoses, that physicians are not fully aware of complications unique to COVID-19 infection (such as thromboembolism), and/or that the above-mentioned limitations of telemedicine have decreased diagnostic performance.

Although PPE stockpile data were not easily available for some sites, a distressingly large number reported stockpiles of 2 weeks or less, with reuse being the most common approach to extending PPE supply. We also found it concerning that 43% of hospital leaders did not know their stockpile data; we believe this is an important question that hospital leaders need to be asking. Most sites in our study reported test turnaround times of longer than 6 hours; lack of rapid COVID-19 testing further stresses PPE stockpile and may slow patients’ transition out of the RIU or discharge to home.

Our study has several limitations, including the evolving nature of the pandemic and rapid adaptations of care systems in the pandemic’s surge phase. However, we attempted to frame our questions in ways that provided a focused snapshot of care. Furthermore, respondents may not have had exhaustive knowledge of their institution’s COVID-19 response strategies, but most were the directors of their hospitalist services, and we encouraged the respondents to confer with others to gather high-fidelity data. Finally, as a survey of large academic medical centers, our results may not apply to nonacademic centers.

Approaches to caring for non-ICU patients during the COVID-19 pandemic are rapidly evolving. Expansion of RIUs and developing the workforce to support them has been a primary focus, with rapid innovation in use of technology emerging as a critical adaptation while PPE limitations persist and needs for “medical distancing” continue to grow. Although rates of missed COVID-19 diagnoses will likely be reduced with testing and systems improvements, physicians and systems will also need to consider how to utilize emerging technology in ways that can improve clinical care and provider safety while aiding diagnostic thinking. This survey illustrates the rapid adaptations made by our hospitals in response to the pandemic; ongoing adaptation will likely be needed to optimally care for hospitalized patients with COVID-19 while the pandemic continues to evolve.

Thanks to members of the HOMERuN COVID-19 Collaborative Group: Baylor Scott & White Medical Center – Temple, Texas - Tresa McNeal MD; Beth Israel Deaconess Medical Center - Shani Herzig MD MPH, Joseph Li MD, Julius Yang MD PhD; Brigham and Women’s Hospital - Christopher Roy MD, Jeffrey Schnipper MD MPH; Cedars-Sinai Medical Center - Ed Seferian MD, ; ChristianaCare - Surekha Bhamidipati MD; Cleveland Clinic - Matthew Pappas MD MPH; Dartmouth-Hitchcock Medical Center - Jonathan Lurie MD MS; Dell Medical School at The University of Texas at Austin - Chris Moriates MD, Luci Leykum MD MBA MSc; Denver Health and Hospitals Authority - Diana Mancini MD; Emory University Hospital - Dan Hunt MD; Johns Hopkins Hospital - Daniel J Brotman MD, Zishan K Siddiqui MD, Shaker Eid MD MBA; Maine Medical Center - Daniel A Meyer MD, Robert Trowbridge MD; Massachusetts General Hospital - Melissa Mattison MD; Mayo Clinic Rochester – Caroline Burton MD, Sagar Dugani MD PhD; Medical College of Wisconsin - Sanjay Bhandari MD; Miriam Hospital - Kwame Dapaah-Afriyie MD MBA; Mount Sinai Hospital - Andrew Dunn MD; NorthShore - David Lovinger MD; Northwestern Memorial Hospital - Kevin O’Leary MD MS; Ohio State University Wexner Medical Center - Eric Schumacher DO; Oregon Health & Science University - Angela Alday MD; Penn Medicine - Ryan Greysen MD MHS MA; Rutgers- Robert Wood Johnson University Hospital - Michael Steinberg MD MPH; Stanford University School of Medicine - Neera Ahuja MD; Tulane Hospital and University Medical Center - Geraldine Ménard MD; UC San Diego Health - Ian Jenkins MD; UC Los Angeles Health - Michael Lazarus MD, Magdalena E. Ptaszny, MD; UC San Francisco Health - Bradley A Sharpe, MD, Margaret Fang MD MPH; UK HealthCare - Mark Williams MD MHM, John Romond MD; University of Chicago – David Meltzer MD PhD, Gregory Ruhnke MD; University of Colorado - Marisha Burden MD; University of Florida - Nila Radhakrishnan MD; University of Iowa Hospitals and Clinics - Kevin Glenn MD MS; University of Miami - Efren Manjarrez MD; University of Michigan - Vineet Chopra MD MSc, Valerie Vaughn MD MSc; University of Missouri-Columbia Hospital - Hasan Naqvi MD; University of Nebraska Medical Center - Chad Vokoun MD; University of North Carolina at Chapel Hill - David Hemsey MD; University of Pittsburgh Medical Center - Gena Marie Walker MD; University of Vermont Medical Center - Steven Grant MD; University of Washington Medical Center - Christopher Kim MD MBA, Andrew White MD; University of Washington-Harborview Medical Center - Maralyssa Bann MD; University of Wisconsin Hospital and Clinics - David Sterken MD, Farah Kaiksow MD MPP, Ann Sheehy MD MS, Jordan Kenik MD MPH; UW Northwest Campus - Ben Wolpaw MD; Vanderbilt University Medical Center - Sunil Kripalani MD MSc, Eduard E Vasilevskis MD, Kathleene T Wooldridge MD MPH; Wake Forest Baptist Health - Erik Summers MD; Washington University St. Louis - Michael Lin MD; Weill Cornell - Justin Choi MD; Yale New Haven Hospital - William Cushing MA, Chris Sankey MD; Zuckerberg San Francisco General Hospital - Sumant Ranji MD.

The coronavirus disease of 2019 (COVID-19) pandemic has resulted in a surge in hospitalizations of patients with a novel, serious, and highly contagious infectious disease for which there is yet no proven treatment. Currently, much of the focus has been on intensive care unit (ICU) and ventilator capacity for the sickest of these patients who develop respiratory failure. However, most hospitalized patients are being cared for in general medical units.¹ Some evidence exists to describe adaptations to capacity needs outside of medical wards,^2-4 but few studies have specifically addressed the ward setting. Therefore, there is a pressing need for evidence to describe how to expand capacity and deliver medical ward–based care.

To better understand how inpatient care in the United States is adapting to the COVID-19 pandemic, we surveyed 72 sites participating in the Hospital Medicine Reengineering Network (HOMERuN), a national consortium of hospital medicine groups.⁵ We report results of this survey, carried out between April 3 and April 5, 2020.

Sites and Subjects

HOMERuN is a collaborative network of hospitalists from across the United States whose primary goal is to catalyze research and share best practices across hospital medicine groups. Using surveys of Hospital Medicine leaders, targeted medical record review, and other methods, HOMERuN’s funded research interests to date have included care transitions, workforce issues, patient and family engagement, and diagnostic errors. Sites participating in HOMERuN sites are relatively large urban academic medical centers (Appendix).

Survey Development and Deployment

We designed a focused survey that aimed to provide a snapshot of evolving operational and clinical aspects of COVID-19 care (Appendix). Domains included COVID-19 testing turnaround times, personal protective equipment (PPE) stewardship,⁶ features of respiratory isolation units (RIUs; ie, dedicated units for patients with known or suspected COVID-19), and observed effects on clinical care. We tested the instrument to ensure feasibility and clarity internally, performed brief cognitive testing with several hospital medicine leaders in HOMERuN, then disseminated the survey by email on April 3, with two follow-up emails on 2 subsequent days. Our study was deemed non–human subjects research by the University of California, San Francisco, Committee on Human Research. Descriptive statistics were used to characterize survey responses.

Of 72 hospitals surveyed, 51 (71%) responded. Mean hospital bed count was 940, three were safety-net hospitals, and one was a community-based teaching center; responding and nonresponding hospitals did not differ significantly in terms of bed count (Appendix).

Health System Adaptations, Testing, and PPE Status

Nearly all responding hospitals (46 of 51; 90%) had RIUs for patients with known or suspected COVID-19 (Table 1). Nearly all hospitals took steps to keep potentially sick healthcare providers from infecting others (eg, staying home if sick or exposed). Among respondents, 32% had rapid response teams, 24% had respiratory therapy teams, and 29% had case management teams that were dedicated to COVID-19 care. Thirty-two (63%) had developed models, such as ethics or palliative care consult services, to assist with difficult resource-allocation decisions (eg, how to prioritize ventilator use if demand exceeded supply). Twenty-three (45%) had developed post-acute care monitoring programs dedicated to COVID-19 patients.

auerbach03660722e_t1.jpg

At the time of our survey, only 2 sites (4%) reported COVID-19 test time turnaround under 1 hour, and 15 (30%) reported turnaround in less than 6 hours. Of the 29 sites able to provide estimates of PPE stockpile, 14 (48%) reported a supply of 2 weeks or less. The most common approaches to PPE stewardship focused on reuse of masks and face shields if not obviously soiled, centralizing PPE distribution, and disinfecting or sterilizing masks. Ten sites (20%) were utilizing 3-D printed masks, while 10% used homemade face shields or masks.

Characteristics of COVID-19 RIUs

Forty-six hospitals (90% of all respondents) in our cohort had developed RIUs at the time of survey administration. The earliest RIU implementation date was February 10, 2020, and the most recent was launched on the day of our survey. Admission to RIUs was primarily based on clinical factors associated with known or suspected COVID-19 infection (Table 2). The number of non–critical care RIU beds among locations at that time ranged from 10 or less to more than 50. The mean number of hospitalist attendings caring for patients in the RIUs was 10.2, with a mean 4.1 advanced practice providers, 5.5 residents, and 0 medical students. The number of planned patients per attending was typically 5 to 15. Nurses and physicians typically rounded separately. Medical distancing (eg, reducing patient room entry) was accomplished most commonly by grouped timing of medication administration (76% of sites), video links to room outside of rounding times (54% of sites), the use of video or telemedicine during rounds (17%), and clustering of activities such as medication administration or phlebotomy. The most common criteria prompting discharge from the RIU were a negative COVID-19 test (59%) and hospital discharge (57%), though comments from many respondents suggested that discharge criteria were changing rapidly.

auerbach03660722e_t2.jpg

Effects of Isolation Measures on In-Room Encounters and Diagnostic Processes

More than 90% of sites reported decreases in in-room encounter frequency across all provider types whether as a result of policies in place or not. Reductions were reported among hospitalists, advanced practice providers, residents, consultants, and therapists (Table 3). Reduced room entry most often resulted from an established or developing policy, but many noted reduced room entry without formal policies in place. Nearly all sites reported moving specialty consultations to phone or video evaluations. Diagnostic error was commonly reported, with missed non–COVID-19 medical diagnoses among COVID-19 infected patients being reported by 22 sites (46%) and missed COVID-19 diagnoses in patients admitted for other reasons by 22 sites (45%).

auerbach03660722e_t3.jpg

In this study of medical wards at academic medical centers, we found that, in response to the COVID-19 pandemic, hospitals made several changes in a short period of time to adapt to the crisis. These included implementation and rapid expansion of dedicated RIUs, greatly expanded use of inpatient telehealth for patient assessments and consultation, implementation of other approaches to minimize room entry (such as grouping in-room activities), and deployment of ethics consultation services to help manage issues around potential scarcity of life-saving measures such as ventilators. We also found that availability of PPE and timely testing was limited. Finally, a large proportion of sites reported potential diagnostic problems in the assessment of both patients suspected and those not suspected of having COVID-19.

RIUs are emerging as a primary modality for caring for non-ICU COVID-19 patients, though they never involved medical students; we hope the role of students in particular will increase as new models of training emerge in response to the pandemic.⁷ In contrast, telemedicine evolved rapidly to hold a substantial role in RIUs, with both ward and specialty teams using video visit technology to communicate with patients. COVID-19 has been viewed as a perfect use case for outpatient telemedicine,⁸ and a growing number of studies are examining its outpatient use^9,10; however, to date, somewhat less attention has been paid to inpatient deployment. Although our data suggest telemedicine has found a prominent place in RIUs, it remains to be seen whether it is associated with differences in patient or provider outcomes. For example, deficiencies in the physical examination, limited face-to-face contact, and lack of physical presence could all affect the patient–provider relationship, patient engagement, and the accuracy of the diagnostic process.

Our data suggest the possibility of missing non–COVID-19 diagnoses in patients suspected of COVID-19 and missing COVID-19 in those admitted for nonrespiratory reasons. The latter may be addressed as routine COVID-19 screening of admitted patients becomes commonplace. For the former, however, it is possible that physicians are “anchoring” their thinking on COVID-19 to the exclusion of other diagnoses, that physicians are not fully aware of complications unique to COVID-19 infection (such as thromboembolism), and/or that the above-mentioned limitations of telemedicine have decreased diagnostic performance.

Although PPE stockpile data were not easily available for some sites, a distressingly large number reported stockpiles of 2 weeks or less, with reuse being the most common approach to extending PPE supply. We also found it concerning that 43% of hospital leaders did not know their stockpile data; we believe this is an important question that hospital leaders need to be asking. Most sites in our study reported test turnaround times of longer than 6 hours; lack of rapid COVID-19 testing further stresses PPE stockpile and may slow patients’ transition out of the RIU or discharge to home.

Our study has several limitations, including the evolving nature of the pandemic and rapid adaptations of care systems in the pandemic’s surge phase. However, we attempted to frame our questions in ways that provided a focused snapshot of care. Furthermore, respondents may not have had exhaustive knowledge of their institution’s COVID-19 response strategies, but most were the directors of their hospitalist services, and we encouraged the respondents to confer with others to gather high-fidelity data. Finally, as a survey of large academic medical centers, our results may not apply to nonacademic centers.

Approaches to caring for non-ICU patients during the COVID-19 pandemic are rapidly evolving. Expansion of RIUs and developing the workforce to support them has been a primary focus, with rapid innovation in use of technology emerging as a critical adaptation while PPE limitations persist and needs for “medical distancing” continue to grow. Although rates of missed COVID-19 diagnoses will likely be reduced with testing and systems improvements, physicians and systems will also need to consider how to utilize emerging technology in ways that can improve clinical care and provider safety while aiding diagnostic thinking. This survey illustrates the rapid adaptations made by our hospitals in response to the pandemic; ongoing adaptation will likely be needed to optimally care for hospitalized patients with COVID-19 while the pandemic continues to evolve.

Thanks to members of the HOMERuN COVID-19 Collaborative Group: Baylor Scott & White Medical Center – Temple, Texas - Tresa McNeal MD; Beth Israel Deaconess Medical Center - Shani Herzig MD MPH, Joseph Li MD, Julius Yang MD PhD; Brigham and Women’s Hospital - Christopher Roy MD, Jeffrey Schnipper MD MPH; Cedars-Sinai Medical Center - Ed Seferian MD, ; ChristianaCare - Surekha Bhamidipati MD; Cleveland Clinic - Matthew Pappas MD MPH; Dartmouth-Hitchcock Medical Center - Jonathan Lurie MD MS; Dell Medical School at The University of Texas at Austin - Chris Moriates MD, Luci Leykum MD MBA MSc; Denver Health and Hospitals Authority - Diana Mancini MD; Emory University Hospital - Dan Hunt MD; Johns Hopkins Hospital - Daniel J Brotman MD, Zishan K Siddiqui MD, Shaker Eid MD MBA; Maine Medical Center - Daniel A Meyer MD, Robert Trowbridge MD; Massachusetts General Hospital - Melissa Mattison MD; Mayo Clinic Rochester – Caroline Burton MD, Sagar Dugani MD PhD; Medical College of Wisconsin - Sanjay Bhandari MD; Miriam Hospital - Kwame Dapaah-Afriyie MD MBA; Mount Sinai Hospital - Andrew Dunn MD; NorthShore - David Lovinger MD; Northwestern Memorial Hospital - Kevin O’Leary MD MS; Ohio State University Wexner Medical Center - Eric Schumacher DO; Oregon Health & Science University - Angela Alday MD; Penn Medicine - Ryan Greysen MD MHS MA; Rutgers- Robert Wood Johnson University Hospital - Michael Steinberg MD MPH; Stanford University School of Medicine - Neera Ahuja MD; Tulane Hospital and University Medical Center - Geraldine Ménard MD; UC San Diego Health - Ian Jenkins MD; UC Los Angeles Health - Michael Lazarus MD, Magdalena E. Ptaszny, MD; UC San Francisco Health - Bradley A Sharpe, MD, Margaret Fang MD MPH; UK HealthCare - Mark Williams MD MHM, John Romond MD; University of Chicago – David Meltzer MD PhD, Gregory Ruhnke MD; University of Colorado - Marisha Burden MD; University of Florida - Nila Radhakrishnan MD; University of Iowa Hospitals and Clinics - Kevin Glenn MD MS; University of Miami - Efren Manjarrez MD; University of Michigan - Vineet Chopra MD MSc, Valerie Vaughn MD MSc; University of Missouri-Columbia Hospital - Hasan Naqvi MD; University of Nebraska Medical Center - Chad Vokoun MD; University of North Carolina at Chapel Hill - David Hemsey MD; University of Pittsburgh Medical Center - Gena Marie Walker MD; University of Vermont Medical Center - Steven Grant MD; University of Washington Medical Center - Christopher Kim MD MBA, Andrew White MD; University of Washington-Harborview Medical Center - Maralyssa Bann MD; University of Wisconsin Hospital and Clinics - David Sterken MD, Farah Kaiksow MD MPP, Ann Sheehy MD MS, Jordan Kenik MD MPH; UW Northwest Campus - Ben Wolpaw MD; Vanderbilt University Medical Center - Sunil Kripalani MD MSc, Eduard E Vasilevskis MD, Kathleene T Wooldridge MD MPH; Wake Forest Baptist Health - Erik Summers MD; Washington University St. Louis - Michael Lin MD; Weill Cornell - Justin Choi MD; Yale New Haven Hospital - William Cushing MA, Chris Sankey MD; Zuckerberg San Francisco General Hospital - Sumant Ranji MD.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

mueller0666-0408e_t1.jpg

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

mueller0666-0408e_t2.jpg

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

mueller0666-0408e_f1.jpg

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

mueller0666-0408e_t1.jpg

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

mueller0666-0408e_t2.jpg

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

mueller0666-0408e_f1.jpg

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

mueller0666-0408e_t1.jpg

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

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In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

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Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.