Kevin J O’Leary, MD, MS

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

Several studies have examined variation in outcomes of patients with COVID-19, with emphasis on hospital-level factors such as geographic location, workforce and resource availability, and COVID-19 community prevalence.^1,2 Block et al¹ examine variation in COVID-19 mortality across 117 US hospitals, exploring whether COVID-19 admission volume was associated with mortality. While their results suggest that patients admitted to hospitals in the highest quintiles of COVID-19 caseload had higher odds of in-hospital death, the authors were not able to fully adjust for severity of illness, tempering our ability to draw conclusions. However, their finding is consistent with work showing that emergency department crowding and high hospital utilization are associated with excess mortality.

Block et al¹ also found variation within quintiles of COVID-19 burden, suggesting that other hospital-level factors are influencing their performance. In response to the initial surge of COVID-19 in the United States, hospitals and healthcare systems made rapid, often major, adjustments to provide care. Four interdependent components contribute to an effective surge response: system, space, staff, and supplies. Although all four components are important, effective systems are critical. Systems domains include command, or the creation of leadership teams throughout the organization; control, or management, of infrastructure; communication of rapid, comprehensible messages internally and externally; coordination of resources across departments and professions; and continuity of operations.³ Little is known about how well hospitals have implemented these systems components throughout the pandemic, and while Janke et al² examined the association of resources with outcomes, neither their study nor Block et al’s was able to account for other organizational or systems-based aspects of surge response.

Studies that help us understand the organizational factors and care-delivery adaptations associated with better outcomes for patients with COVID-19 are sorely needed, and could provide important insights for organizational adaptation and change more generally. Janke et al² and, in their accompanying editorial, Auerbach and Greysen,⁴ call for “innovative protocols” and “flexibility” to meet the needs of high-demand, novel situations. However, organizations’ ability to innovate and adapt relies on their relationships and teamwork capability.

The relational infrastructure within an organization provides the basis for effective teamwork, facilitating other aspects of an organization’s surge response and ability to adapt. Relationships characterized by trust and mindfulness create a context of psychological safety that encourages sharing new ideas, and enable teams to rapidly make sense of new situations and create shared understandings that facilitate effective action: improvising, adapting, and learning. Trust and psychological safety are especially important during crises, as decision-making tends to evolve toward top-down processes in times of crisis.

Hospitals currently collect few data that speak to relationships and teamwork, limiting our ability to study these vital organizational characteristics and their role in the larger COVID-19 response. Surveys related to patient safety culture or provider wellness and burnout are likely the only data regularly collected by hospitals. Expanding these data to include measures of relational infrastructure will create more robust data not only to conduct research regarding organizational factors that are associated with patient outcomes, but also to allow health systems to improve relationships and teaming as a means of improving outcomes. Examples include relational coordination,⁵ relationships,⁶and learning scales.⁷

The hospitals to which patients are admitted make a difference in patient survival. The study by Block et al¹ highlights the importance of assessing the factors that enable health systems to adapt and innovate so that we can better understand hospital-level variation in outcomes.

In their report celebrating the increase in the number of hospitalists from a few hundred in the 1990s to more than 50,000 in 2016, Drs Robert Wachter and Lee Goldman also noted the stunted growth of productive hospital medicine research programs, which presents a challenge to academic credibility in hospital medicine.¹ Given the substantial increase in the number of hospitalists over the past two decades, we surveyed adult academic hospital medicine groups to quantify the number of hospitalist clinician investigators and identify gaps in resources for researchers. The number of clinician investigators supported at academic medical centers (AMCs) remains disturbingly low despite the rapid growth of our specialty. Some programs also reported a lack of access to fundamental research services. We report selected results from our survey and provide recommendations to support and facilitate the development of clinician investigators in hospital medicine.

We performed a survey of hospital medicine programs at AMCs in the United States through the Hospital Medicine Reengineering Network (HOMERuN), a hospital medicine research collaborative that facilitates and conducts multisite research studies.² The purpose of this survey was to obtain a profile of adult academic hospital medicine groups. Surveys were distributed via email to directors and/or senior leaders of each hospital medicine group between January and August 2019. In the survey, a clinician investigator was defined as “faculty whose primary nonclinical focus is scientific papers and grant writing.”

We received responses from 43 of the 86 invitees (50%), each of whom represented a unique hospital medicine group; 41 of the representatives responded to the questions concerning available research services. Collectively, these 43 programs represented 2,503 hospitalists. There were 79 clinician investigators reported among all surveyed hospital medicine groups (3.1% of all hospitalists). The median number of clinician investigators per hospital medicine group was 0 (range 0-12) (Appendix Figure 1), and 22 of 43 (51.2%) hospital medicine groups reported having no clinician investigators. Two of the hospital medicine groups, however, reported having 12 clinician investigators at their respective institutions, comprising nearly one third of the total number of clinician investigators reported in the survey.

Many of the programs reported lack of access to resources such as research assistants (56.1%) and dedicated research fellowships (53.7%) (Appendix Figure 2). A number of groups reported a need for more support for various junior faculty development activities, including research mentoring (53.5%), networking with other researchers (60.5%), and access to clinical data from multiple sites (62.8%).

One of the limitations of this survey was the manner in which the participating hospital medicine groups were chosen. Selection was based on groups affiliated with HOMERuN; among those chosen were highly visible US AMCs, including 70% of the top 20 AMCs based on National Institutes of Health (NIH) funding.³ Therefore, our results likely overestimate the research presence of hospital medicine across all AMCs in the United States.

Despite the substantial growth of hospital medicine over the past 2 decades, there has been no proportional increase in the number of hospitalist clinician investigators, with earlier surveys also demonstrating low numbers.^4,5 Along with the survey by Chopra and colleagues published in 2019,⁶ our survey provides an additional contemporary appraisal of research activities for adult academic hospital medicine groups. In the survey by Chopra et al, only 54% (15 of 28) of responding programs reported having any faculty with research as their major activity (ie, >50% effort), and 3% of total faculty reported having funding for >50% effort toward research.⁶ Our study expands upon these findings by providing more detailed data on the number of clinician investigators per hospital medicine group. Results of our survey showed a concentration of hospitalists within a small number of programs, which may have contributed to the observed lack of growth. We also expand on prior work by identifying a lack of resources and services to support hospitalist researchers.

The findings of our survey have important implications for the field of hospital medicine. Without a critical mass of hospitalist clinician investigators, the quality of research that addresses important questions in our field will suffer. It will also limit academic credibility of the field, as well as individual academic achievement; previous studies have consistently demonstrated that few hospitalists at AMCs achieve the rank of associate or full professor.^5-9

The results of our study additionally offer possible explanations for the dearth of clinician investigators in hospital medicine. The limited access to research resources and fellowship training identified in our survey are critical domains that must be addressed in order to develop successful academic hospital medicine programs.^4,6,8,10

Regarding dedicated hospital medicine research fellowships, there are only a handful across the country. The small number of existing research fellowships only have one or two fellows per year, and these positions often go unfilled because of a lack of applicants and lower salaries compared to full-time clinical positions.¹¹ The lack of applicants for adult hospital medicine fellowship positions is also integrally linked to board certification requirements. Unlike pediatric hospital medicine where additional fellowship training is required to become board-certified, no such fellowship is required in adult hospital medicine. In pediatrics, this requirement has led to a rapid increase in the number of fellowships with scholarly work requirements (more than 60 fellowships, plus additional programs in development) and greater standardization among training experiences.^12,13

The lack of fellowship applicants may also stem from the fact that many trainees are not aware of a potential career as a hospitalist clinician investigator due to limited exposure to this career at most AMCs. Our results revealed that nearly half of sites in our survey had zero clinician investigators, depriving trainees at these programs of role models and thus perpetuating a negative feedback loop. Lastly, although unfilled fellowship positions may indicate that demand is a larger problem than supply, it is also true that fellowship programs generate their own demand through recruitment efforts and the gradual establishment of a positive reputation.

Another potential explanation could relate to the development of hospital medicine in response to rising clinical demands at hospitals: compared with other medical specialties, AMCs may regard hospitalists as being clinicians first and academicians second.^1,7,10 Also, hospitalists may be perceived as being beholden to hospitals and less engaged with their surrounding communities than other general medicine fields. With a small footprint in health equity research, academic hospital medicine may be less of a draw to generalists interested in pursuing this area of research. Further, there are very few underrepresented in medicine (URiM) hospital medicine research faculty.⁵

Another challenge to the career development of hospitalist researchers is the lack of available funding for the type of research typically conducted by hospitalists (eg, rigorous quality improvement implementation and evaluation, optimizing best evidence-based care delivery models, evaluation of patient safety in the hospital setting). As hospitalists tend to be system-level thinkers, this lack of funding may steer potential researchers away from externally funded research careers and into hospital operations and quality improvement positions. Also, unlike other medical specialties, there is no dedicated NIH funding source for hospital medicine research (eg, cardiology and the National Heart, Lung, and Blood Institute), placing hospitalists at a disadvantage in seeking funding compared to subspecialists.

We recommend several approaches—ones that should be pursued simultaneously—to increase the number of clinician investigators in hospital medicine. First, hospital medicine groups and their respective divisions, departments, and hospitals should allocate funding to support research resources; this includes investing in research assistants, data analysts, statisticians, and administrative support. Through the funding of such research infrastructure programs, AMCs could incentivize hospitalists to research best approaches to improve the value of healthcare delivery, ultimately leading to cost savings.

With 60% of respondents identifying the need for improved access to data across multiple sites, our survey also emphasizes the requirement for further collaboration among hospital medicine groups. Such collaboration could lead to high-powered observational studies and the evaluation of interventions across multiple sites, thus improving the generalizability of study findings.

The Society of Hospital Medicine (SHM) and its research committee can continue to expand the research footprint of hospital medicine. To date, the committee has achieved this by highlighting hospitalist research activity at the SHM Annual Conference Scientific Abstract and Poster Competition and developing a visiting professorship exchange program. In addition to these efforts, SHM could foster collaboration and networking between institutions, as well as take advantage of the current political push for expanded Medicare access by lobbying for robust funding for the Agency for Healthcare Research and Quality, which could provide more opportunities for hospitalists to study the effects of healthcare policy reform on the delivery of inpatient care.

Another strategy to increase the number of hospitalist clinician investigators is to expand hospital medicine research fellowships and recruit trainees for these programs. Fellowships could be internally funded wherein a fellow’s clinical productivity is used to offset the costs associated with obtaining advanced degrees. As an incentive to encourage applicants to temporarily forego a full-time clinical salary during fellowship, hospital medicine groups could offer expanded moonlighting opportunities and contribute to repayment of medical school loans. Hospital medicine groups should also advocate for NIH-funded T32 or K12 training grants for hospital medicine. (There are, however, challenges with this approach because the number of T32 spots per NIH institute is usually fixed). The success of academic emergency medicine offers a precedent for such efforts: After the development of a K12 research training program in emergency medicine, the number of NIH-sponsored principal investigators in this specialty increased by 40% in 6 years.¹⁴ Additionally, now that fellowships are required for the pediatric hospital medicine clinician investigators, it would be revealing to track the growth of this workforce.^12,13

Structured and formalized mentorship is an essential part of the development of clinician investigators in hospital medicine.^4,7,8,10 One successful strategy for mentorship has been the partnering of hospital medicine groups with faculty of general internal medicine and other subspecialty divisions with robust research programs.^7,8,15 In addition to developing sustainable mentorship programs, hospital medicine researchers must increase their visibility to trainees. Therefore, it is essential that the majority of academic hospital medicine groups not only hire clinician investigators but also invest in their development, rather than rely on the few programs that have several such faculty members. With this strategy, we could dramatically increase the number of hospitalist clinician investigators from a diverse background of training institutions.

SHM could also play a greater role in organizing events for networking and mentoring for trainees and medical students interested in pursuing a career in hospital medicine research. It is also critically important that hospital medicine groups actively recruit, retain, and develop URiM hospital medicine research faculty in order to attract talented researchers and actively participate in the necessary effort to mitigate the inequities prevalent throughout our healthcare system.

Despite the growth of hospital medicine over the past decade, there remains a dearth of hospitalist clinician investigators at major AMCs in the United States. This may be due in part to lack of research resources and mentorship within hospital medicine groups. We believe that investment in these resources, expanded funding opportunities, mentorship development, research fellowship programs, and greater exposure of trainees to hospitalist researchers are solutions that should be strongly considered to develop hospitalist clinician investigators.

The authors thank HOMERuN executive committee members, including Grant Fletcher, MD, James Harrison, PhD, BSC, MPH, Peter K. Lindenauer, MD, Melissa Mattison, MD, David Meltzer, MD, PhD, Joshua Metlay, MD, PhD, Jennifer Myers, MD, Sumant Ranji, MD, Gregory Ruhnke, MD, MPH, Edmondo Robinson, MD, MBA, and Neil Sehgal, MPH PhD, for their assistance in developing the survey. They also thank Tiffany Lee, MA, for her project management assistance for HOMERuN.

In their report celebrating the increase in the number of hospitalists from a few hundred in the 1990s to more than 50,000 in 2016, Drs Robert Wachter and Lee Goldman also noted the stunted growth of productive hospital medicine research programs, which presents a challenge to academic credibility in hospital medicine.¹ Given the substantial increase in the number of hospitalists over the past two decades, we surveyed adult academic hospital medicine groups to quantify the number of hospitalist clinician investigators and identify gaps in resources for researchers. The number of clinician investigators supported at academic medical centers (AMCs) remains disturbingly low despite the rapid growth of our specialty. Some programs also reported a lack of access to fundamental research services. We report selected results from our survey and provide recommendations to support and facilitate the development of clinician investigators in hospital medicine.

We performed a survey of hospital medicine programs at AMCs in the United States through the Hospital Medicine Reengineering Network (HOMERuN), a hospital medicine research collaborative that facilitates and conducts multisite research studies.² The purpose of this survey was to obtain a profile of adult academic hospital medicine groups. Surveys were distributed via email to directors and/or senior leaders of each hospital medicine group between January and August 2019. In the survey, a clinician investigator was defined as “faculty whose primary nonclinical focus is scientific papers and grant writing.”

We received responses from 43 of the 86 invitees (50%), each of whom represented a unique hospital medicine group; 41 of the representatives responded to the questions concerning available research services. Collectively, these 43 programs represented 2,503 hospitalists. There were 79 clinician investigators reported among all surveyed hospital medicine groups (3.1% of all hospitalists). The median number of clinician investigators per hospital medicine group was 0 (range 0-12) (Appendix Figure 1), and 22 of 43 (51.2%) hospital medicine groups reported having no clinician investigators. Two of the hospital medicine groups, however, reported having 12 clinician investigators at their respective institutions, comprising nearly one third of the total number of clinician investigators reported in the survey.

Many of the programs reported lack of access to resources such as research assistants (56.1%) and dedicated research fellowships (53.7%) (Appendix Figure 2). A number of groups reported a need for more support for various junior faculty development activities, including research mentoring (53.5%), networking with other researchers (60.5%), and access to clinical data from multiple sites (62.8%).

One of the limitations of this survey was the manner in which the participating hospital medicine groups were chosen. Selection was based on groups affiliated with HOMERuN; among those chosen were highly visible US AMCs, including 70% of the top 20 AMCs based on National Institutes of Health (NIH) funding.³ Therefore, our results likely overestimate the research presence of hospital medicine across all AMCs in the United States.

Despite the substantial growth of hospital medicine over the past 2 decades, there has been no proportional increase in the number of hospitalist clinician investigators, with earlier surveys also demonstrating low numbers.^4,5 Along with the survey by Chopra and colleagues published in 2019,⁶ our survey provides an additional contemporary appraisal of research activities for adult academic hospital medicine groups. In the survey by Chopra et al, only 54% (15 of 28) of responding programs reported having any faculty with research as their major activity (ie, >50% effort), and 3% of total faculty reported having funding for >50% effort toward research.⁶ Our study expands upon these findings by providing more detailed data on the number of clinician investigators per hospital medicine group. Results of our survey showed a concentration of hospitalists within a small number of programs, which may have contributed to the observed lack of growth. We also expand on prior work by identifying a lack of resources and services to support hospitalist researchers.

The findings of our survey have important implications for the field of hospital medicine. Without a critical mass of hospitalist clinician investigators, the quality of research that addresses important questions in our field will suffer. It will also limit academic credibility of the field, as well as individual academic achievement; previous studies have consistently demonstrated that few hospitalists at AMCs achieve the rank of associate or full professor.^5-9

The results of our study additionally offer possible explanations for the dearth of clinician investigators in hospital medicine. The limited access to research resources and fellowship training identified in our survey are critical domains that must be addressed in order to develop successful academic hospital medicine programs.^4,6,8,10

Regarding dedicated hospital medicine research fellowships, there are only a handful across the country. The small number of existing research fellowships only have one or two fellows per year, and these positions often go unfilled because of a lack of applicants and lower salaries compared to full-time clinical positions.¹¹ The lack of applicants for adult hospital medicine fellowship positions is also integrally linked to board certification requirements. Unlike pediatric hospital medicine where additional fellowship training is required to become board-certified, no such fellowship is required in adult hospital medicine. In pediatrics, this requirement has led to a rapid increase in the number of fellowships with scholarly work requirements (more than 60 fellowships, plus additional programs in development) and greater standardization among training experiences.^12,13

The lack of fellowship applicants may also stem from the fact that many trainees are not aware of a potential career as a hospitalist clinician investigator due to limited exposure to this career at most AMCs. Our results revealed that nearly half of sites in our survey had zero clinician investigators, depriving trainees at these programs of role models and thus perpetuating a negative feedback loop. Lastly, although unfilled fellowship positions may indicate that demand is a larger problem than supply, it is also true that fellowship programs generate their own demand through recruitment efforts and the gradual establishment of a positive reputation.

Another potential explanation could relate to the development of hospital medicine in response to rising clinical demands at hospitals: compared with other medical specialties, AMCs may regard hospitalists as being clinicians first and academicians second.^1,7,10 Also, hospitalists may be perceived as being beholden to hospitals and less engaged with their surrounding communities than other general medicine fields. With a small footprint in health equity research, academic hospital medicine may be less of a draw to generalists interested in pursuing this area of research. Further, there are very few underrepresented in medicine (URiM) hospital medicine research faculty.⁵

Another challenge to the career development of hospitalist researchers is the lack of available funding for the type of research typically conducted by hospitalists (eg, rigorous quality improvement implementation and evaluation, optimizing best evidence-based care delivery models, evaluation of patient safety in the hospital setting). As hospitalists tend to be system-level thinkers, this lack of funding may steer potential researchers away from externally funded research careers and into hospital operations and quality improvement positions. Also, unlike other medical specialties, there is no dedicated NIH funding source for hospital medicine research (eg, cardiology and the National Heart, Lung, and Blood Institute), placing hospitalists at a disadvantage in seeking funding compared to subspecialists.

We recommend several approaches—ones that should be pursued simultaneously—to increase the number of clinician investigators in hospital medicine. First, hospital medicine groups and their respective divisions, departments, and hospitals should allocate funding to support research resources; this includes investing in research assistants, data analysts, statisticians, and administrative support. Through the funding of such research infrastructure programs, AMCs could incentivize hospitalists to research best approaches to improve the value of healthcare delivery, ultimately leading to cost savings.

With 60% of respondents identifying the need for improved access to data across multiple sites, our survey also emphasizes the requirement for further collaboration among hospital medicine groups. Such collaboration could lead to high-powered observational studies and the evaluation of interventions across multiple sites, thus improving the generalizability of study findings.

The Society of Hospital Medicine (SHM) and its research committee can continue to expand the research footprint of hospital medicine. To date, the committee has achieved this by highlighting hospitalist research activity at the SHM Annual Conference Scientific Abstract and Poster Competition and developing a visiting professorship exchange program. In addition to these efforts, SHM could foster collaboration and networking between institutions, as well as take advantage of the current political push for expanded Medicare access by lobbying for robust funding for the Agency for Healthcare Research and Quality, which could provide more opportunities for hospitalists to study the effects of healthcare policy reform on the delivery of inpatient care.

Another strategy to increase the number of hospitalist clinician investigators is to expand hospital medicine research fellowships and recruit trainees for these programs. Fellowships could be internally funded wherein a fellow’s clinical productivity is used to offset the costs associated with obtaining advanced degrees. As an incentive to encourage applicants to temporarily forego a full-time clinical salary during fellowship, hospital medicine groups could offer expanded moonlighting opportunities and contribute to repayment of medical school loans. Hospital medicine groups should also advocate for NIH-funded T32 or K12 training grants for hospital medicine. (There are, however, challenges with this approach because the number of T32 spots per NIH institute is usually fixed). The success of academic emergency medicine offers a precedent for such efforts: After the development of a K12 research training program in emergency medicine, the number of NIH-sponsored principal investigators in this specialty increased by 40% in 6 years.¹⁴ Additionally, now that fellowships are required for the pediatric hospital medicine clinician investigators, it would be revealing to track the growth of this workforce.^12,13

Structured and formalized mentorship is an essential part of the development of clinician investigators in hospital medicine.^4,7,8,10 One successful strategy for mentorship has been the partnering of hospital medicine groups with faculty of general internal medicine and other subspecialty divisions with robust research programs.^7,8,15 In addition to developing sustainable mentorship programs, hospital medicine researchers must increase their visibility to trainees. Therefore, it is essential that the majority of academic hospital medicine groups not only hire clinician investigators but also invest in their development, rather than rely on the few programs that have several such faculty members. With this strategy, we could dramatically increase the number of hospitalist clinician investigators from a diverse background of training institutions.

SHM could also play a greater role in organizing events for networking and mentoring for trainees and medical students interested in pursuing a career in hospital medicine research. It is also critically important that hospital medicine groups actively recruit, retain, and develop URiM hospital medicine research faculty in order to attract talented researchers and actively participate in the necessary effort to mitigate the inequities prevalent throughout our healthcare system.

Despite the growth of hospital medicine over the past decade, there remains a dearth of hospitalist clinician investigators at major AMCs in the United States. This may be due in part to lack of research resources and mentorship within hospital medicine groups. We believe that investment in these resources, expanded funding opportunities, mentorship development, research fellowship programs, and greater exposure of trainees to hospitalist researchers are solutions that should be strongly considered to develop hospitalist clinician investigators.

The authors thank HOMERuN executive committee members, including Grant Fletcher, MD, James Harrison, PhD, BSC, MPH, Peter K. Lindenauer, MD, Melissa Mattison, MD, David Meltzer, MD, PhD, Joshua Metlay, MD, PhD, Jennifer Myers, MD, Sumant Ranji, MD, Gregory Ruhnke, MD, MPH, Edmondo Robinson, MD, MBA, and Neil Sehgal, MPH PhD, for their assistance in developing the survey. They also thank Tiffany Lee, MA, for her project management assistance for HOMERuN.

In their report celebrating the increase in the number of hospitalists from a few hundred in the 1990s to more than 50,000 in 2016, Drs Robert Wachter and Lee Goldman also noted the stunted growth of productive hospital medicine research programs, which presents a challenge to academic credibility in hospital medicine.¹ Given the substantial increase in the number of hospitalists over the past two decades, we surveyed adult academic hospital medicine groups to quantify the number of hospitalist clinician investigators and identify gaps in resources for researchers. The number of clinician investigators supported at academic medical centers (AMCs) remains disturbingly low despite the rapid growth of our specialty. Some programs also reported a lack of access to fundamental research services. We report selected results from our survey and provide recommendations to support and facilitate the development of clinician investigators in hospital medicine.

We performed a survey of hospital medicine programs at AMCs in the United States through the Hospital Medicine Reengineering Network (HOMERuN), a hospital medicine research collaborative that facilitates and conducts multisite research studies.² The purpose of this survey was to obtain a profile of adult academic hospital medicine groups. Surveys were distributed via email to directors and/or senior leaders of each hospital medicine group between January and August 2019. In the survey, a clinician investigator was defined as “faculty whose primary nonclinical focus is scientific papers and grant writing.”

We received responses from 43 of the 86 invitees (50%), each of whom represented a unique hospital medicine group; 41 of the representatives responded to the questions concerning available research services. Collectively, these 43 programs represented 2,503 hospitalists. There were 79 clinician investigators reported among all surveyed hospital medicine groups (3.1% of all hospitalists). The median number of clinician investigators per hospital medicine group was 0 (range 0-12) (Appendix Figure 1), and 22 of 43 (51.2%) hospital medicine groups reported having no clinician investigators. Two of the hospital medicine groups, however, reported having 12 clinician investigators at their respective institutions, comprising nearly one third of the total number of clinician investigators reported in the survey.

Many of the programs reported lack of access to resources such as research assistants (56.1%) and dedicated research fellowships (53.7%) (Appendix Figure 2). A number of groups reported a need for more support for various junior faculty development activities, including research mentoring (53.5%), networking with other researchers (60.5%), and access to clinical data from multiple sites (62.8%).

One of the limitations of this survey was the manner in which the participating hospital medicine groups were chosen. Selection was based on groups affiliated with HOMERuN; among those chosen were highly visible US AMCs, including 70% of the top 20 AMCs based on National Institutes of Health (NIH) funding.³ Therefore, our results likely overestimate the research presence of hospital medicine across all AMCs in the United States.

Despite the substantial growth of hospital medicine over the past 2 decades, there has been no proportional increase in the number of hospitalist clinician investigators, with earlier surveys also demonstrating low numbers.^4,5 Along with the survey by Chopra and colleagues published in 2019,⁶ our survey provides an additional contemporary appraisal of research activities for adult academic hospital medicine groups. In the survey by Chopra et al, only 54% (15 of 28) of responding programs reported having any faculty with research as their major activity (ie, >50% effort), and 3% of total faculty reported having funding for >50% effort toward research.⁶ Our study expands upon these findings by providing more detailed data on the number of clinician investigators per hospital medicine group. Results of our survey showed a concentration of hospitalists within a small number of programs, which may have contributed to the observed lack of growth. We also expand on prior work by identifying a lack of resources and services to support hospitalist researchers.

The findings of our survey have important implications for the field of hospital medicine. Without a critical mass of hospitalist clinician investigators, the quality of research that addresses important questions in our field will suffer. It will also limit academic credibility of the field, as well as individual academic achievement; previous studies have consistently demonstrated that few hospitalists at AMCs achieve the rank of associate or full professor.^5-9

The results of our study additionally offer possible explanations for the dearth of clinician investigators in hospital medicine. The limited access to research resources and fellowship training identified in our survey are critical domains that must be addressed in order to develop successful academic hospital medicine programs.^4,6,8,10

Regarding dedicated hospital medicine research fellowships, there are only a handful across the country. The small number of existing research fellowships only have one or two fellows per year, and these positions often go unfilled because of a lack of applicants and lower salaries compared to full-time clinical positions.¹¹ The lack of applicants for adult hospital medicine fellowship positions is also integrally linked to board certification requirements. Unlike pediatric hospital medicine where additional fellowship training is required to become board-certified, no such fellowship is required in adult hospital medicine. In pediatrics, this requirement has led to a rapid increase in the number of fellowships with scholarly work requirements (more than 60 fellowships, plus additional programs in development) and greater standardization among training experiences.^12,13

The lack of fellowship applicants may also stem from the fact that many trainees are not aware of a potential career as a hospitalist clinician investigator due to limited exposure to this career at most AMCs. Our results revealed that nearly half of sites in our survey had zero clinician investigators, depriving trainees at these programs of role models and thus perpetuating a negative feedback loop. Lastly, although unfilled fellowship positions may indicate that demand is a larger problem than supply, it is also true that fellowship programs generate their own demand through recruitment efforts and the gradual establishment of a positive reputation.

Another potential explanation could relate to the development of hospital medicine in response to rising clinical demands at hospitals: compared with other medical specialties, AMCs may regard hospitalists as being clinicians first and academicians second.^1,7,10 Also, hospitalists may be perceived as being beholden to hospitals and less engaged with their surrounding communities than other general medicine fields. With a small footprint in health equity research, academic hospital medicine may be less of a draw to generalists interested in pursuing this area of research. Further, there are very few underrepresented in medicine (URiM) hospital medicine research faculty.⁵

Another challenge to the career development of hospitalist researchers is the lack of available funding for the type of research typically conducted by hospitalists (eg, rigorous quality improvement implementation and evaluation, optimizing best evidence-based care delivery models, evaluation of patient safety in the hospital setting). As hospitalists tend to be system-level thinkers, this lack of funding may steer potential researchers away from externally funded research careers and into hospital operations and quality improvement positions. Also, unlike other medical specialties, there is no dedicated NIH funding source for hospital medicine research (eg, cardiology and the National Heart, Lung, and Blood Institute), placing hospitalists at a disadvantage in seeking funding compared to subspecialists.

We recommend several approaches—ones that should be pursued simultaneously—to increase the number of clinician investigators in hospital medicine. First, hospital medicine groups and their respective divisions, departments, and hospitals should allocate funding to support research resources; this includes investing in research assistants, data analysts, statisticians, and administrative support. Through the funding of such research infrastructure programs, AMCs could incentivize hospitalists to research best approaches to improve the value of healthcare delivery, ultimately leading to cost savings.

With 60% of respondents identifying the need for improved access to data across multiple sites, our survey also emphasizes the requirement for further collaboration among hospital medicine groups. Such collaboration could lead to high-powered observational studies and the evaluation of interventions across multiple sites, thus improving the generalizability of study findings.

The Society of Hospital Medicine (SHM) and its research committee can continue to expand the research footprint of hospital medicine. To date, the committee has achieved this by highlighting hospitalist research activity at the SHM Annual Conference Scientific Abstract and Poster Competition and developing a visiting professorship exchange program. In addition to these efforts, SHM could foster collaboration and networking between institutions, as well as take advantage of the current political push for expanded Medicare access by lobbying for robust funding for the Agency for Healthcare Research and Quality, which could provide more opportunities for hospitalists to study the effects of healthcare policy reform on the delivery of inpatient care.

Another strategy to increase the number of hospitalist clinician investigators is to expand hospital medicine research fellowships and recruit trainees for these programs. Fellowships could be internally funded wherein a fellow’s clinical productivity is used to offset the costs associated with obtaining advanced degrees. As an incentive to encourage applicants to temporarily forego a full-time clinical salary during fellowship, hospital medicine groups could offer expanded moonlighting opportunities and contribute to repayment of medical school loans. Hospital medicine groups should also advocate for NIH-funded T32 or K12 training grants for hospital medicine. (There are, however, challenges with this approach because the number of T32 spots per NIH institute is usually fixed). The success of academic emergency medicine offers a precedent for such efforts: After the development of a K12 research training program in emergency medicine, the number of NIH-sponsored principal investigators in this specialty increased by 40% in 6 years.¹⁴ Additionally, now that fellowships are required for the pediatric hospital medicine clinician investigators, it would be revealing to track the growth of this workforce.^12,13

Structured and formalized mentorship is an essential part of the development of clinician investigators in hospital medicine.^4,7,8,10 One successful strategy for mentorship has been the partnering of hospital medicine groups with faculty of general internal medicine and other subspecialty divisions with robust research programs.^7,8,15 In addition to developing sustainable mentorship programs, hospital medicine researchers must increase their visibility to trainees. Therefore, it is essential that the majority of academic hospital medicine groups not only hire clinician investigators but also invest in their development, rather than rely on the few programs that have several such faculty members. With this strategy, we could dramatically increase the number of hospitalist clinician investigators from a diverse background of training institutions.

SHM could also play a greater role in organizing events for networking and mentoring for trainees and medical students interested in pursuing a career in hospital medicine research. It is also critically important that hospital medicine groups actively recruit, retain, and develop URiM hospital medicine research faculty in order to attract talented researchers and actively participate in the necessary effort to mitigate the inequities prevalent throughout our healthcare system.

Despite the growth of hospital medicine over the past decade, there remains a dearth of hospitalist clinician investigators at major AMCs in the United States. This may be due in part to lack of research resources and mentorship within hospital medicine groups. We believe that investment in these resources, expanded funding opportunities, mentorship development, research fellowship programs, and greater exposure of trainees to hospitalist researchers are solutions that should be strongly considered to develop hospitalist clinician investigators.

The authors thank HOMERuN executive committee members, including Grant Fletcher, MD, James Harrison, PhD, BSC, MPH, Peter K. Lindenauer, MD, Melissa Mattison, MD, David Meltzer, MD, PhD, Joshua Metlay, MD, PhD, Jennifer Myers, MD, Sumant Ranji, MD, Gregory Ruhnke, MD, MPH, Edmondo Robinson, MD, MBA, and Neil Sehgal, MPH PhD, for their assistance in developing the survey. They also thank Tiffany Lee, MA, for her project management assistance for HOMERuN.

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.

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.

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%).

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.

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.

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%).

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.

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.

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%).

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.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.

In recent years, hospitals have made considerable efforts to improve transitions of care, in part due to financial incentives from the Medicare Hospital Readmission Reduction Program (HRRP).¹ Initially focusing on three medical conditions, the HRRP has been associated with significant reductions in readmission rates.² Importantly, a small proportion of patients accounts for a very large proportion of hospital readmissions and hospital use.^3,4 Frequently hospitalized patients often have multiple chronic conditions and unique needs which may not be met by conventional approaches to healthcare delivery, including those influenced by the HRRP.^4-6 In light of this challenge, some hospitals have developed programs specifically focused on frequently hospitalized patients. A recent systematic review of these programs found relatively few studies of high quality, providing only limited insight in designing interventions to support this population.⁷ Moreover, no studies appear to have incorporated the patients’ perspectives into the design or adaptation of the model. Members of our research team developed and implemented the Complex High Admission Management Program (CHAMP) in January 2016 to address the needs of frequently hospitalized patients in our hospital. To enhance CHAMP and inform the design of programs serving similar populations in other health systems, we sought to identify factors associated with the onset and continuation of high hospital use. Our research question was, from the patients’ perspective, what factors contribute to patients’ becoming and continuing to be high users of hospital care.

Setting, Study Design, and Participants

This qualitative study took place at Northwestern Memorial Hospital (NMH), an 894-bed urban academic hospital located in Chicago, Illinois. Between December 2016 and September 2017, we recruited adult patients admitted to the general medicine services. Eligible participants were identified with the assistance of a daily Northwestern Medicine Electronic Data Warehouse (EDW) search and included patients with two unplanned 30-day inpatient readmissions to NMH within the prior 12 months, in addition to one or more of the following criteria: (1) at least one readmission in the last six months; (2) a referral from one of the patient’s medical providers; or (3) at least three observation visits. We excluded patients whose preferred language was not English and those disoriented to person, place, or time. Considering NMH data showing that approximately one-third of high-utilizer patients have sickle cell disease, we used purposive sampling with the goal to compare findings within and between two groups of participants; those with and those without sickle cell disease. Our study was deemed exempt by the Northwestern University Institutional Review Board.

Participant Enrollment and Data Collection

We created an interview guide based on the research team’s experience with this population, a literature review, and our research question (See Appendix).^8,9 A research coordinator approached eligible participants during their hospital stay. The coordinator explained the study to eligible participants and obtained verbal consent for participation. The research coordinator then conducted one-on-one semi-structured interviews. Interviews were audio recorded for subsequent transcription and coding. Each interview lasted approximately 45 minutes. Participants were compensated with a $20 gift card for their time.

Analysis

Digital audio recordings from interviews were transcribed verbatim, deidentified, and analyzed using an iterative inductive team-based approach to coding.¹⁰ In our first cycle coding, all coders (KJO, SF, MMC, LO, KAC) independently reviewed and coded three transcripts using descriptive coding and subcoding to generate a preliminary codebook with code definitions.^10,11 Following the meetings to compare and compile our initial coding, each researcher then independently recoded the three transcripts with the developed codebook. The researchers met again to triangulate perspectives and reach a consensus on the final codebook. Using multiple coders is a standard process to control for subjective bias that one coder could bring to the coding process.¹² Following this meeting, the coders split into two teams of two (KJO, SF, and MMC, LO) to complete the coding of the remaining transcripts. Each team member independently coded the assigned transcripts and reconciled their codes with their counterpart; any discrepancies were resolved through discussion. Using this strategy, every transcript was coded by at least two team members. Our second coding cycle utilized pattern coding and involved identifying consistency both within and between transcripts; discovering associations between codes.^10,11,13 Constant comparison was used to compare responses among all participants, as well as between sickle-cell and nonsickle-cell participants.^13,14 Following team coding and reconciling, the analyses were presented to a broader research team for additional feedback and critique. All analyses were conducted using Dedoose version 8.0.35 (Los Angeles, California). Participant recruitment, interviews, and analysis of the transcripts continued until no new codes emerged and thematic saturation was achieved.

Participant Characteristics

Overall, we invited 34 patients to be interviewed; 26 consented and completed interviews (76.5%). Six (17.6%) patients declined participation, one (2.9%) was unable to complete the interview before hospital discharge, and one (2.9%) was excluded due to disorientation. Demographic characteristics of the 26 participants are shown in Table 1.

Four main themes emerged from our analysis. Table 2 summarizes these themes, subthemes, and provides representative quotes.

Major Medical Problem(s) are Universal, but High Hospital Use Varies in Onset

Not surprisingly, all participants described having at least one major medical problem. Some participants, such as those with genetic disorders, had experienced periods of high hospital use throughout their entire lifetime, while other participants experienced an onset of high hospital use as an adult after being previously healthy. Though most participants with genetic disorders had sickle cell anemia; one had a rare genetic disorder which caused chronic gastrointestinal symptoms. Participants typically described having a significant medical condition as well as other medical problems or complications from past surgery. Some participants described having a major medical problem which did not require frequent hospitalization until a complication or other medical problem arose, suggesting these new issues pushed them over a threshold beyond which self-management at home was less successful.

Course Fluctuates over Time and is Related to Psychological, Social, and Economic Factors

Participants identified psychological stress, social support, and financial constraints as factors which influence the course of their illness over time. Deaths in the family, breakups, and concerns about other family members were mentioned as specific forms of psychological stress and directly linked by participants to worsening of symptoms. Social support was present for most, but not all, participants, with no appreciable difference based on whether the participant had sickle cell disease. Social support was generally perceived as helpful, and several participants indicated a benefit to their own health when providing social support to others. Financial pressures also served as stressors and often impeded care due to lack of access to medications, other treatments, and housing.

Onset and Progression of Episodes Vary, but Generally Seem Uncontrollable

Regarding the onset of illness episodes, some participants described the sudden, unpredictable onset of symptoms, others described a more gradual onset which allowed them to attempt self-management. Regardless of the timing, episodes of illness were often perceived as spontaneous or triggered by factors outside of the participant’s control. Several participants, especially those with sickle cell disease, mentioned a relationship between their symptoms and the weather. Participants also noted the inconsistency in factors which may trigger an episode (ie, sometimes the factor exacerbated symptoms, while other times it did not). Participants also described having a high symptom burden with significant limitations in activities of daily living during episodes of illness. Pain was a very common component of symptoms regardless of whether or not the participant had sickle cell disease.

Individuals Seek Care after Self-Management Fails and Prefer to Avoid Hospitalization

Participants tried to control their symptoms with medications and typically sought care only when it was clear that this approach was not working, or they ran out of medications. This finding was consistent across both groups of participants (ie, those with and those without sickle cell disease). Many participants described very strong preferences not to come to the hospital; no participant described being in the hospital as a favorable or positive experience. Some participants mentioned that they had spent major holidays in the hospital and that they missed their family. No participant had a desire to come to the hospital.

In this study of frequently hospitalized patients, we found four major themes that illuminate patient perspectives about factors that contribute to high hospital use. While some of our findings corroborate those of previous studies, other emerging patterns were novel. Herein, we summarize key findings, provide context, and describe implications for the design of models of care for frequently hospitalized patients.

Similar to the findings of previous quantitative research, participants in our study described having a significant medical condition and typically had multiple medical conditions or complications.^4-6 Importantly, some participants described having a major medical problem which did not require frequent hospitalization until another medical problem or complication arose. This finding suggests that there may be an opportunity to identify patients with significant medical problems who are at elevated risk before the onset of high hospital use. Early identification of these high-risk patients could allow for the provision of additional support to prevent potential complications or address other factors which may contribute to the need for frequent hospitalization.

Participants in our study directly linked psychological stress to fluctuations in their course of illness. Previous research by Mautner and colleagues queried participants about childhood experiences and early life stressors and reported that early life instabilities and traumas were prevalent among patients with high levels of emergency and hospital-based healthcare utilization.¹⁵ Our participants identified more recent traumatic events (eg, the death of a loved one and breakups) when reflecting on factors contributing to illness exacerbations; early life trauma did not emerge as an identified contributor. Of note, unlike Mautner et al., we did not ask participants to reflect on childhood determinants of disease and illness specifically. Our findings suggest that psychological stress contributes to illness exacerbation, even for those patients without other significant psychiatric conditions (eg, depressive disorder, schizophrenia). Incorporating mental health professionals into programs for this patient population may improve health by teaching specific coping strategies, including cognitive-behavioral therapy for an acute stress disorder.^16,17

Social support was also a factor related to illness fluctuations over time. Notably, several participants indicated a benefit to their own health when providing social support to others, suggesting a role for peer support that may be reciprocally beneficial. This approach is supported by the literature. Williams and colleagues found that patients with sickle cell anemia experienced symptom improvement with peer support;¹⁸ while Johnson and colleagues recently reported a reduction in readmissions to acute care with the use of peer support for patients with severe mental illness.¹⁹

Financial constraints impeded care for some patients and served as a barrier to accessing medications, other treatments, and housing. Similar to the findings of prior quantitative research, our frequently hospitalized patients had a high proportion of patients with Medicaid and low proportion with private insurance, suggesting low socioeconomic status.^9,20 We did not formally collect data on income or economic status. Interestingly, prior qualitative studies have not identified financial constraints as a major theme, though this may be explained by differences in study populations and the overall objectives of the studies.^15,21 Importantly, the overwhelming majority of programs for frequently hospitalized patients identified in a recent systematic review included social workers.⁷ Our findings support the need to address financial constraints and the use of social workers in models of care for frequently hospitalized patients.

Many participants in our study felt that the factors contributing to exacerbations of illness were either inconsistent in their effect or out of their control. These findings have similarities to those from a qualitative study by Liu and colleagues in which they interviewed 20 “hospital-dependent” patients over 65 years of age.²¹ Though not explicitly focused on factors contributing to exacerbations, participants in their study felt that hospitalizations were generally inevitable. In our study, participants with sickle cell disease often identified changes in the weather as contributing to illness exacerbations. The relationship between weather and sickle cell disease remains incompletely understood, with an inconsistent association found in prior studies.²²

Participants in our study strongly desired to avoid hospitalization and typically sought hospital care when symptoms could not be controlled at home. This finding is in contrast to that from the study by Liu and colleagues where they found that hospital-dependent patients over 65 years had favorable perspectives of hospitalization because they felt safer and more secure in the hospital.²¹ Our participants were younger than those from the study by Liu and colleagues, had a high symptom burden, and may have been more concerned about control of those symptoms than the risk for clinical deterioration. Programs should aim to strengthen their support of patients’ self-management efforts early in the episode of illness and potentially offer home visits or a day hospital to avoid hospitalization. A recent systematic review found evidence that alternatives to inpatient care (eg, hospital-at-home) for low risk medical patients can achieve comparable outcomes at lower costs.²³ Similarly, some health systems have implemented day hospitals to treat low risk patients with uncomplicated sickle cell pain.^24,25

The heavy symptom burden experienced by participants in our study is notable. Pain was especially common. Programs may wish to partner with palliative care and addiction specialists to balance symptom relief with the simultaneous need to address comorbid substance and opioid use disorders when they are present.^4,9

Our study has several limitations. First, participants were recruited from the medicine service at a single academic hospital using criteria we developed to identify frequently hospitalized patients. Populations differ across hospitals and definitions of frequently hospitalized patients vary, limiting the generalizability of our findings. Second, we excluded patients whose preferred language was not English, as well as those disoriented to person, place, or time. It is possible that factors contributing to high hospital use differ for non-English speaking patients and those with cognitive deficits.

In this qualitative study, we identified factors associated with the onset and continuation of high hospital use. Emergent themes pointed to factors which influence patients’ onset of high hospital use, fluctuations in their illness over time, and triggers to seek care during an illness episode. These findings represent an important contribution to the literature because they allow patients’ perspectives to be incorporated into the design and adaptation of programs serving similar populations in other health systems. Programs that integrate patients’ perspectives into their design are likely to be better prepared to address patients’ needs and improve patient outcomes.

Acknowledgments

The authors thank the participants for their time and willingness to share their stories. The authors also thank Claire A. Knoten PhD and Erin Lambers PhD, former research team members who helped in the initial stages of the study.

Disclosures

The authors have nothing to disclose.

Funding

This project was funded by Northwestern Memorial Hospital and the Northwestern Medical Group.