Hospital Medicine

In this edition of the Journal of Hospital Medicine, Fenster and colleagues evaluate the trend of postdischarge intravenous (IV) antibiotic therapy for children with osteomyelitis, complicated pneumonia, and complicated appendicitis.¹ Children requiring prolonged antibiotic therapy were historically discharged home with a peripherally inserted central catheter (PICC) for IV antibiotics. Recent studies suggest that treatment failure occurs uncommonly, and that oral antibiotics are as effective as those administered intravenously.^2-4 Oral antibiotics also avoid the additional risk of PICC-related complications, such as line malfunction, infections, and thrombi, which all lead to increased re-visits to hospital.

New research seldom leads to rapid change in clinical practice.⁵ This is particularly the case when new evidence favors the abandonment of accepted medical practices or supports the deimplementation of low-value care. The mounting body of evidence suggests that postdischarge IV antibiotic therapy is low-value care for children with osteomyelitis, complicated pneumonia, and complicated appendicitis, and that overuse is associated with unnecessary harm. Fenster and colleagues sought to evaluate the extent to which the management of these conditions has changed over time in the United States. They conducted a retrospective cohort study of children discharged from hospitals contributing data to the Pediatric Health Information System (PHIS) database. Validated algorithms using discharge diagnosis and procedure codes were used to identify children with the three conditions who were discharged home with IV antibiotic therapy.

Between January 2000 and December 2018 and across 52 hospitals, there were 24,753 hospitalizations for osteomyelitis, 13,700 for complicated pneumonia, and 60,575 for complicated appendicitis. Rates of postdischarge IV antibiotic therapy decreased over time for all conditions, from 61% to 22% for osteomyelitis, from 29% to 19% for complicated pneumonia, and from 13% to 2% for complicated appendicitis. Rather than a gradual reduction over time, the authors used piecewise linear regression to identify an inflection point when the decrease started: the inflection points for all three occurred around 2009 or 2010. Despite the observed decrease over time, there was significant variation in practice patterns among hospitals in 2018. For example, while the median rate of postdischarge IV antibiotic therapy for osteomyelitis was 18%, the interquartile ranged from 9% to 40%.

The authors conducted several sensitivity analyses, with the exclusion of hospitals that provided data only for certain years, which supported the robustness of the findings. Yet there are important limitations, most notably the lack of data on outcomes related to overuse and efficiency: type of antibiotics used (narrow vs broad spectrum) and total duration of antibiotics or variation in length of stay. The validated algorithms were also based on older ICD-9 codes and may perform less well with ICD-10 or from 2015 onwards. Lastly, the findings are limited to children’s hospitals and may not apply to general hospitals that care for many children.

The authors suggest that the deimplementation of postdischarge IV antibiotic therapy for the three conditions occurred spontaneously. Yet it is worth considering the different levels of agents of change that may have influenced these observations, such as research evidence, national condition guidelines, national efforts at reducing overuse and improving safety, local hospital efforts, and shared decision-making.

Postdischarge antibiotic therapy options for osteomyelitis, complicated pneumonia, and complicated appendicitis are supported by weak research evidence. Oral and parenteral therapy are equally effective but based on observational data; a randomized controlled trial is unlikely to ever be conducted because of uncommon outcomes, such as treatment failures. For these scenarios, greater emphasis should be placed on factors other than effectiveness, such as harms, availability of alternative options, and cost.⁶ For postdischarge IV antibiotic therapy, one potential explanation for the observed deimplementation is the greater awareness of harm, with up to 20% of cases with IV antibiotics requiring PICC removal.⁷ There is also a readily available alternative (oral antibiotics) with a favorable cost and effectiveness profile.

National condition guidelines advocating early transition to oral antibiotic therapy began to appear before and during the observed inflection point of 2009 and 2010. The 2002 British Thoracic Society guidelines for community-acquired pneumonia suggested considering oral agents after clear evidence of improvement,⁸ and the 2010 Infectious Diseases Society of America guidelines recommended oral antibiotic options for children discharged home with intra-abdominal infections.⁹ A systematic review published in 2002 also questioned the need for prolonged IV antibiotic therapy compared with early transition to oral agents in osteomyelitis.¹⁰ While no targeted national interventions to drive practice change existed, widespread national efforts at reducing overuse (eg, Choosing Wisely^®) and improving safety (eg, reducing central line complications) have increased in the past decade.¹¹

An important agent of change that Fenster and colleagues were not able to tease out was the impact of local hospital level efforts. In parallel to national efforts, there has likely been targeted hospital-level interventions that are disease specific (eg, order sets, pathways/guidelines, shared–decision-making tools) or focused on reducing adverse events (eg, reducing inappropriate PICC use). For example, between 2010 and 2012, one US children’s hospital increased the number of children with osteomyelitis discharged on oral antibiotics from a median of 0% to 100% with a bundle of quality improvement interventions, including standardized treatment protocols and shared decision-making.¹²

Despite the encouraging results, up to 22% of children were discharged from hospitals with postdischarge IV antibiotic therapy, and significant variation persists in 2018. Evidence of harm or even strong recommendations to change practice are themselves inadequate for behavior change.¹³ While it is clear that some element of deimplementation may have occurred organically over the past two decades, it is time for concerted deimplementation strategies that focus on practitioners or hospitals with “entrenched practices.”⁶

Dr Gill has received grant funding from the Canadian Paediatric Society, the Hospital for Sick Children, and the Canadian Institutes of Health Research (CIHR) in the past 5 years. He is on editorial board of BMJ Evidence-Based Medicine (EBM) and on the Institute Advisory Board for the CIHR Institute of Human Development and Child and Youth Health (IHDCYH), for which he has expenses reimbursed to attend meetings. He is a member of the EBMLive steering committee, and he has expenses reimbursed to attend the conference. Dr Mahant has received grant funding from CIHR in the past 5 years and is a Senior Deputy Editor of Journal of Hospital Medicine. The authors reported no conflicts of interest or financial relationships relevant to this manuscript.

In this edition of the Journal of Hospital Medicine, Fenster and colleagues evaluate the trend of postdischarge intravenous (IV) antibiotic therapy for children with osteomyelitis, complicated pneumonia, and complicated appendicitis.¹ Children requiring prolonged antibiotic therapy were historically discharged home with a peripherally inserted central catheter (PICC) for IV antibiotics. Recent studies suggest that treatment failure occurs uncommonly, and that oral antibiotics are as effective as those administered intravenously.^2-4 Oral antibiotics also avoid the additional risk of PICC-related complications, such as line malfunction, infections, and thrombi, which all lead to increased re-visits to hospital.

New research seldom leads to rapid change in clinical practice.⁵ This is particularly the case when new evidence favors the abandonment of accepted medical practices or supports the deimplementation of low-value care. The mounting body of evidence suggests that postdischarge IV antibiotic therapy is low-value care for children with osteomyelitis, complicated pneumonia, and complicated appendicitis, and that overuse is associated with unnecessary harm. Fenster and colleagues sought to evaluate the extent to which the management of these conditions has changed over time in the United States. They conducted a retrospective cohort study of children discharged from hospitals contributing data to the Pediatric Health Information System (PHIS) database. Validated algorithms using discharge diagnosis and procedure codes were used to identify children with the three conditions who were discharged home with IV antibiotic therapy.

Between January 2000 and December 2018 and across 52 hospitals, there were 24,753 hospitalizations for osteomyelitis, 13,700 for complicated pneumonia, and 60,575 for complicated appendicitis. Rates of postdischarge IV antibiotic therapy decreased over time for all conditions, from 61% to 22% for osteomyelitis, from 29% to 19% for complicated pneumonia, and from 13% to 2% for complicated appendicitis. Rather than a gradual reduction over time, the authors used piecewise linear regression to identify an inflection point when the decrease started: the inflection points for all three occurred around 2009 or 2010. Despite the observed decrease over time, there was significant variation in practice patterns among hospitals in 2018. For example, while the median rate of postdischarge IV antibiotic therapy for osteomyelitis was 18%, the interquartile ranged from 9% to 40%.

The authors conducted several sensitivity analyses, with the exclusion of hospitals that provided data only for certain years, which supported the robustness of the findings. Yet there are important limitations, most notably the lack of data on outcomes related to overuse and efficiency: type of antibiotics used (narrow vs broad spectrum) and total duration of antibiotics or variation in length of stay. The validated algorithms were also based on older ICD-9 codes and may perform less well with ICD-10 or from 2015 onwards. Lastly, the findings are limited to children’s hospitals and may not apply to general hospitals that care for many children.

The authors suggest that the deimplementation of postdischarge IV antibiotic therapy for the three conditions occurred spontaneously. Yet it is worth considering the different levels of agents of change that may have influenced these observations, such as research evidence, national condition guidelines, national efforts at reducing overuse and improving safety, local hospital efforts, and shared decision-making.

Postdischarge antibiotic therapy options for osteomyelitis, complicated pneumonia, and complicated appendicitis are supported by weak research evidence. Oral and parenteral therapy are equally effective but based on observational data; a randomized controlled trial is unlikely to ever be conducted because of uncommon outcomes, such as treatment failures. For these scenarios, greater emphasis should be placed on factors other than effectiveness, such as harms, availability of alternative options, and cost.⁶ For postdischarge IV antibiotic therapy, one potential explanation for the observed deimplementation is the greater awareness of harm, with up to 20% of cases with IV antibiotics requiring PICC removal.⁷ There is also a readily available alternative (oral antibiotics) with a favorable cost and effectiveness profile.

National condition guidelines advocating early transition to oral antibiotic therapy began to appear before and during the observed inflection point of 2009 and 2010. The 2002 British Thoracic Society guidelines for community-acquired pneumonia suggested considering oral agents after clear evidence of improvement,⁸ and the 2010 Infectious Diseases Society of America guidelines recommended oral antibiotic options for children discharged home with intra-abdominal infections.⁹ A systematic review published in 2002 also questioned the need for prolonged IV antibiotic therapy compared with early transition to oral agents in osteomyelitis.¹⁰ While no targeted national interventions to drive practice change existed, widespread national efforts at reducing overuse (eg, Choosing Wisely^®) and improving safety (eg, reducing central line complications) have increased in the past decade.¹¹

An important agent of change that Fenster and colleagues were not able to tease out was the impact of local hospital level efforts. In parallel to national efforts, there has likely been targeted hospital-level interventions that are disease specific (eg, order sets, pathways/guidelines, shared–decision-making tools) or focused on reducing adverse events (eg, reducing inappropriate PICC use). For example, between 2010 and 2012, one US children’s hospital increased the number of children with osteomyelitis discharged on oral antibiotics from a median of 0% to 100% with a bundle of quality improvement interventions, including standardized treatment protocols and shared decision-making.¹²

Despite the encouraging results, up to 22% of children were discharged from hospitals with postdischarge IV antibiotic therapy, and significant variation persists in 2018. Evidence of harm or even strong recommendations to change practice are themselves inadequate for behavior change.¹³ While it is clear that some element of deimplementation may have occurred organically over the past two decades, it is time for concerted deimplementation strategies that focus on practitioners or hospitals with “entrenched practices.”⁶

Dr Gill has received grant funding from the Canadian Paediatric Society, the Hospital for Sick Children, and the Canadian Institutes of Health Research (CIHR) in the past 5 years. He is on editorial board of BMJ Evidence-Based Medicine (EBM) and on the Institute Advisory Board for the CIHR Institute of Human Development and Child and Youth Health (IHDCYH), for which he has expenses reimbursed to attend meetings. He is a member of the EBMLive steering committee, and he has expenses reimbursed to attend the conference. Dr Mahant has received grant funding from CIHR in the past 5 years and is a Senior Deputy Editor of Journal of Hospital Medicine. The authors reported no conflicts of interest or financial relationships relevant to this manuscript.

In this edition of the Journal of Hospital Medicine, Fenster and colleagues evaluate the trend of postdischarge intravenous (IV) antibiotic therapy for children with osteomyelitis, complicated pneumonia, and complicated appendicitis.¹ Children requiring prolonged antibiotic therapy were historically discharged home with a peripherally inserted central catheter (PICC) for IV antibiotics. Recent studies suggest that treatment failure occurs uncommonly, and that oral antibiotics are as effective as those administered intravenously.^2-4 Oral antibiotics also avoid the additional risk of PICC-related complications, such as line malfunction, infections, and thrombi, which all lead to increased re-visits to hospital.

New research seldom leads to rapid change in clinical practice.⁵ This is particularly the case when new evidence favors the abandonment of accepted medical practices or supports the deimplementation of low-value care. The mounting body of evidence suggests that postdischarge IV antibiotic therapy is low-value care for children with osteomyelitis, complicated pneumonia, and complicated appendicitis, and that overuse is associated with unnecessary harm. Fenster and colleagues sought to evaluate the extent to which the management of these conditions has changed over time in the United States. They conducted a retrospective cohort study of children discharged from hospitals contributing data to the Pediatric Health Information System (PHIS) database. Validated algorithms using discharge diagnosis and procedure codes were used to identify children with the three conditions who were discharged home with IV antibiotic therapy.

Between January 2000 and December 2018 and across 52 hospitals, there were 24,753 hospitalizations for osteomyelitis, 13,700 for complicated pneumonia, and 60,575 for complicated appendicitis. Rates of postdischarge IV antibiotic therapy decreased over time for all conditions, from 61% to 22% for osteomyelitis, from 29% to 19% for complicated pneumonia, and from 13% to 2% for complicated appendicitis. Rather than a gradual reduction over time, the authors used piecewise linear regression to identify an inflection point when the decrease started: the inflection points for all three occurred around 2009 or 2010. Despite the observed decrease over time, there was significant variation in practice patterns among hospitals in 2018. For example, while the median rate of postdischarge IV antibiotic therapy for osteomyelitis was 18%, the interquartile ranged from 9% to 40%.

The authors conducted several sensitivity analyses, with the exclusion of hospitals that provided data only for certain years, which supported the robustness of the findings. Yet there are important limitations, most notably the lack of data on outcomes related to overuse and efficiency: type of antibiotics used (narrow vs broad spectrum) and total duration of antibiotics or variation in length of stay. The validated algorithms were also based on older ICD-9 codes and may perform less well with ICD-10 or from 2015 onwards. Lastly, the findings are limited to children’s hospitals and may not apply to general hospitals that care for many children.

The authors suggest that the deimplementation of postdischarge IV antibiotic therapy for the three conditions occurred spontaneously. Yet it is worth considering the different levels of agents of change that may have influenced these observations, such as research evidence, national condition guidelines, national efforts at reducing overuse and improving safety, local hospital efforts, and shared decision-making.

Postdischarge antibiotic therapy options for osteomyelitis, complicated pneumonia, and complicated appendicitis are supported by weak research evidence. Oral and parenteral therapy are equally effective but based on observational data; a randomized controlled trial is unlikely to ever be conducted because of uncommon outcomes, such as treatment failures. For these scenarios, greater emphasis should be placed on factors other than effectiveness, such as harms, availability of alternative options, and cost.⁶ For postdischarge IV antibiotic therapy, one potential explanation for the observed deimplementation is the greater awareness of harm, with up to 20% of cases with IV antibiotics requiring PICC removal.⁷ There is also a readily available alternative (oral antibiotics) with a favorable cost and effectiveness profile.

National condition guidelines advocating early transition to oral antibiotic therapy began to appear before and during the observed inflection point of 2009 and 2010. The 2002 British Thoracic Society guidelines for community-acquired pneumonia suggested considering oral agents after clear evidence of improvement,⁸ and the 2010 Infectious Diseases Society of America guidelines recommended oral antibiotic options for children discharged home with intra-abdominal infections.⁹ A systematic review published in 2002 also questioned the need for prolonged IV antibiotic therapy compared with early transition to oral agents in osteomyelitis.¹⁰ While no targeted national interventions to drive practice change existed, widespread national efforts at reducing overuse (eg, Choosing Wisely^®) and improving safety (eg, reducing central line complications) have increased in the past decade.¹¹

An important agent of change that Fenster and colleagues were not able to tease out was the impact of local hospital level efforts. In parallel to national efforts, there has likely been targeted hospital-level interventions that are disease specific (eg, order sets, pathways/guidelines, shared–decision-making tools) or focused on reducing adverse events (eg, reducing inappropriate PICC use). For example, between 2010 and 2012, one US children’s hospital increased the number of children with osteomyelitis discharged on oral antibiotics from a median of 0% to 100% with a bundle of quality improvement interventions, including standardized treatment protocols and shared decision-making.¹²

Despite the encouraging results, up to 22% of children were discharged from hospitals with postdischarge IV antibiotic therapy, and significant variation persists in 2018. Evidence of harm or even strong recommendations to change practice are themselves inadequate for behavior change.¹³ While it is clear that some element of deimplementation may have occurred organically over the past two decades, it is time for concerted deimplementation strategies that focus on practitioners or hospitals with “entrenched practices.”⁶

Dr Gill has received grant funding from the Canadian Paediatric Society, the Hospital for Sick Children, and the Canadian Institutes of Health Research (CIHR) in the past 5 years. He is on editorial board of BMJ Evidence-Based Medicine (EBM) and on the Institute Advisory Board for the CIHR Institute of Human Development and Child and Youth Health (IHDCYH), for which he has expenses reimbursed to attend meetings. He is a member of the EBMLive steering committee, and he has expenses reimbursed to attend the conference. Dr Mahant has received grant funding from CIHR in the past 5 years and is a Senior Deputy Editor of Journal of Hospital Medicine. The authors reported no conflicts of interest or financial relationships relevant to this manuscript.

“Time is the coin of your life. It is the only coin you have, and only you can determine how it will be spent. Be careful lest you let other people spend it for you.”

—Carl Sandburg

No one went into the practice of medicine to check off boxes. Clinicians find joy and purpose by connecting with patients and interacting with colleagues. Unfortunately, our goal of practicing in an environment that allows these experiences is threatened by extreme levels of regulatory and administrative oversight.^1,2 Decreased enjoyment and meaning in work may follow and lead to burnout, poor performance, and for some, premature departure from medicine.³ The negative effects on individuals can erode the morale and productivity of the group.

Many administrative requirements add value to clinical care. For example, interdisciplinary rounds may include a mandatory review of urinary catheters that reduces catheter-associated infections. The usefulness of some requirements, however, may promote implementation of other requirements of lesser value that interfere with the positive impact of meaningful interventions. Best Practice Alerts (BPAs) that are “clicked through” sap enthusiasm. Perfunctory monthly meetings that are informational rather than productive and exhaustive e-learning modules on institutional requirements such as “Corporate Compliance” take time away from patient care. Despite being a prominent driver of burnout, the most common approach to nuisances is nihilism. It is unrealistic for anyone with a full clinical slate to tackle pervasive irritations. Similarly, leaders may not see decreasing administrative burdens as a priority; the excitement for decreasing hassles pales relative to the excitement for developing a new vision or strategic plan.

Rather than acceptance, leaders should take proactive steps to decrease wasteful tasks. Begin by explicitly assessing the burden of tasks through dialogue with administrators, such as the chief medical officer. Administrators may not realize the impact of seemingly small requests on hospitalist workflow. For example, even adding one required question for every patient at interdisciplinary rounds can meaningfully affect the flow of rounds. Hospitalist leaders are well situated to assess the yield to burden ratio (Y/B) of any requirement. High burden tasks should be justified by substantial benefit, and tasks in which the Y/B is uncertain should be limited in scope until the value proposition is established.

The electronic medical record (EMR) deserves specific attention because it is an established source of annoyance and burnout.³ Leaders should proactively collaborate with administrators to remove EMR requirements with low Y/B. The “Get Rid of Stupid Stuff” (GROSS) program demonstrated the benefits of an innovative approach to eliminating wasteful EMR tasks.⁴ Our own institution discontinued the BPA asking clinicians to add “Chronic Kidney Disease, Stage III” to the Problem List when an assessment revealed that the Problem List was rarely updated and this BPA was frequently presented; the BPA was low yield, high burden.

Lastly, leaders should not become part of the problem. For example, a hospitalist-led quality improvement project may require documentation that a primary care physician has been contacted for each newly admitted patient. Assuming four patients and 5 minutes per call, this ask requires 20 minutes; the burden has been estimated but the yield is unknown, producing an unclear Y/B. Therefore, items generated within the group need to be vetted with the same scrutiny as external tasks.

Explicitly addressing wasteful burdens provides leaders with the opportunity to shift the emphasis from processes that distract from to initiatives that enhance patient care. Promoting a sense of meaning and purpose is an essential component of group success.⁵ Outstanding performance, productivity, and retention can only be realized through a work environment that prioritizes patients and minimizes tasks not aligned with this mission.

The authors have nothing to disclose.

“Time is the coin of your life. It is the only coin you have, and only you can determine how it will be spent. Be careful lest you let other people spend it for you.”

—Carl Sandburg

No one went into the practice of medicine to check off boxes. Clinicians find joy and purpose by connecting with patients and interacting with colleagues. Unfortunately, our goal of practicing in an environment that allows these experiences is threatened by extreme levels of regulatory and administrative oversight.^1,2 Decreased enjoyment and meaning in work may follow and lead to burnout, poor performance, and for some, premature departure from medicine.³ The negative effects on individuals can erode the morale and productivity of the group.

Many administrative requirements add value to clinical care. For example, interdisciplinary rounds may include a mandatory review of urinary catheters that reduces catheter-associated infections. The usefulness of some requirements, however, may promote implementation of other requirements of lesser value that interfere with the positive impact of meaningful interventions. Best Practice Alerts (BPAs) that are “clicked through” sap enthusiasm. Perfunctory monthly meetings that are informational rather than productive and exhaustive e-learning modules on institutional requirements such as “Corporate Compliance” take time away from patient care. Despite being a prominent driver of burnout, the most common approach to nuisances is nihilism. It is unrealistic for anyone with a full clinical slate to tackle pervasive irritations. Similarly, leaders may not see decreasing administrative burdens as a priority; the excitement for decreasing hassles pales relative to the excitement for developing a new vision or strategic plan.

Rather than acceptance, leaders should take proactive steps to decrease wasteful tasks. Begin by explicitly assessing the burden of tasks through dialogue with administrators, such as the chief medical officer. Administrators may not realize the impact of seemingly small requests on hospitalist workflow. For example, even adding one required question for every patient at interdisciplinary rounds can meaningfully affect the flow of rounds. Hospitalist leaders are well situated to assess the yield to burden ratio (Y/B) of any requirement. High burden tasks should be justified by substantial benefit, and tasks in which the Y/B is uncertain should be limited in scope until the value proposition is established.

The electronic medical record (EMR) deserves specific attention because it is an established source of annoyance and burnout.³ Leaders should proactively collaborate with administrators to remove EMR requirements with low Y/B. The “Get Rid of Stupid Stuff” (GROSS) program demonstrated the benefits of an innovative approach to eliminating wasteful EMR tasks.⁴ Our own institution discontinued the BPA asking clinicians to add “Chronic Kidney Disease, Stage III” to the Problem List when an assessment revealed that the Problem List was rarely updated and this BPA was frequently presented; the BPA was low yield, high burden.

Lastly, leaders should not become part of the problem. For example, a hospitalist-led quality improvement project may require documentation that a primary care physician has been contacted for each newly admitted patient. Assuming four patients and 5 minutes per call, this ask requires 20 minutes; the burden has been estimated but the yield is unknown, producing an unclear Y/B. Therefore, items generated within the group need to be vetted with the same scrutiny as external tasks.

Explicitly addressing wasteful burdens provides leaders with the opportunity to shift the emphasis from processes that distract from to initiatives that enhance patient care. Promoting a sense of meaning and purpose is an essential component of group success.⁵ Outstanding performance, productivity, and retention can only be realized through a work environment that prioritizes patients and minimizes tasks not aligned with this mission.

The authors have nothing to disclose.

“Time is the coin of your life. It is the only coin you have, and only you can determine how it will be spent. Be careful lest you let other people spend it for you.”

—Carl Sandburg

No one went into the practice of medicine to check off boxes. Clinicians find joy and purpose by connecting with patients and interacting with colleagues. Unfortunately, our goal of practicing in an environment that allows these experiences is threatened by extreme levels of regulatory and administrative oversight.^1,2 Decreased enjoyment and meaning in work may follow and lead to burnout, poor performance, and for some, premature departure from medicine.³ The negative effects on individuals can erode the morale and productivity of the group.

Many administrative requirements add value to clinical care. For example, interdisciplinary rounds may include a mandatory review of urinary catheters that reduces catheter-associated infections. The usefulness of some requirements, however, may promote implementation of other requirements of lesser value that interfere with the positive impact of meaningful interventions. Best Practice Alerts (BPAs) that are “clicked through” sap enthusiasm. Perfunctory monthly meetings that are informational rather than productive and exhaustive e-learning modules on institutional requirements such as “Corporate Compliance” take time away from patient care. Despite being a prominent driver of burnout, the most common approach to nuisances is nihilism. It is unrealistic for anyone with a full clinical slate to tackle pervasive irritations. Similarly, leaders may not see decreasing administrative burdens as a priority; the excitement for decreasing hassles pales relative to the excitement for developing a new vision or strategic plan.

Rather than acceptance, leaders should take proactive steps to decrease wasteful tasks. Begin by explicitly assessing the burden of tasks through dialogue with administrators, such as the chief medical officer. Administrators may not realize the impact of seemingly small requests on hospitalist workflow. For example, even adding one required question for every patient at interdisciplinary rounds can meaningfully affect the flow of rounds. Hospitalist leaders are well situated to assess the yield to burden ratio (Y/B) of any requirement. High burden tasks should be justified by substantial benefit, and tasks in which the Y/B is uncertain should be limited in scope until the value proposition is established.

The electronic medical record (EMR) deserves specific attention because it is an established source of annoyance and burnout.³ Leaders should proactively collaborate with administrators to remove EMR requirements with low Y/B. The “Get Rid of Stupid Stuff” (GROSS) program demonstrated the benefits of an innovative approach to eliminating wasteful EMR tasks.⁴ Our own institution discontinued the BPA asking clinicians to add “Chronic Kidney Disease, Stage III” to the Problem List when an assessment revealed that the Problem List was rarely updated and this BPA was frequently presented; the BPA was low yield, high burden.

Lastly, leaders should not become part of the problem. For example, a hospitalist-led quality improvement project may require documentation that a primary care physician has been contacted for each newly admitted patient. Assuming four patients and 5 minutes per call, this ask requires 20 minutes; the burden has been estimated but the yield is unknown, producing an unclear Y/B. Therefore, items generated within the group need to be vetted with the same scrutiny as external tasks.

Explicitly addressing wasteful burdens provides leaders with the opportunity to shift the emphasis from processes that distract from to initiatives that enhance patient care. Promoting a sense of meaning and purpose is an essential component of group success.⁵ Outstanding performance, productivity, and retention can only be realized through a work environment that prioritizes patients and minimizes tasks not aligned with this mission.

The authors have nothing to disclose.

The Accreditation Council for Graduate Medical Education (ACGME) first mandated residency work hour restrictions in 2003.¹ In 2011, revised work hour requirements were issued, further limiting the maximum duration of a shift and extending the duration of time off between scheduled shifts.² Academic medical centers have been forced to adapt to work hour restrictions, and cuts in funding to research and educational missions have pressured institutions to restructure with a greater focus on high-quality, lower-cost care.^3,4 In response, many academic hospitals have added hospitalist teams, or incorporated advanced practice clinicians (APCs) (nurse practitioners [NPs] and physician assistants [PAs]) to accommodate resident physician duty hour restrictions on their inpatient general medicine services.^5,6 More recently, the COVID-19 pandemic has created unanticipated physician shortages forcing medical centers to rapidly expand and broaden the scope of their existing APC workforce.⁷

Several comparisons of clinical outcomes, cost, and patient satisfaction between different combinations of hospitalist-based, resident-based, or APC-based inpatient teams have been reported with conflicting observations.^6,8-14 Roy et al reported no significant differences in mortality, length of stay (LOS), or readmissions between PA and resident teams.⁶ Timmermans et al reported similar cost-effectiveness, LOS, and quality of care between PA and physician teams that included a hybrid of attending only and resident teams.^13,14 Alternatively, Singh et al and Iannuzzi et al reported increased LOS among PA teams,^10,12 whereas Chin et al observed an increased LOS and reduced 30-day readmissions among hospitalist teams.⁸ While these observed differences may be attributable to heterogeneous patient populations or institution-specific team structure, the exact reasons remain unknown. Furthermore, understanding the value of alternate staffing models is essential for medical centers to prepare for potential COVID-19 related physician shortages. To our knowledge, no study to date has directly compared outcomes between resident, APC, and hospitalist team structures within an academic medical center.

We believe our institution provides a unique environment to study the differences in inpatient general medicine team structure with respect to quality and efficiency of care delivery. The objective of our study is to directly compare clinical outcomes and resource utilization among three distinct team structures: APC, resident, and solo hospitalist. We hypothesize that clinical outcomes, cost, and utilization of consult services will be similar across all team structures and hospitalist teams will discharge patients earlier than resident and APC teams.

Study Design and Setting

We conducted a retrospective observational cohort study at the University of Utah Medical Center, a 548-bed academic medical center in Salt Lake City. An electronic database query was used to identify all patients discharged from the inpatient general internal medicine service between July 1, 2015, and July 1, 2018. Baseline patient characteristics were collected including age, gender, and Charlson comorbidity index (CCI).¹⁵ Case-mix index was determined for admissions where a Medicare Severity Diagnosis Related Group (MS-DRG) and corresponding weight was assigned.^16,17 Source of admission was collected to identify patients transferred from an outside hospital, typically due to increased medical complexity or need for specialty care not available at the referring center. Time of admission was collected to classify whether a patient was admitted during the day or at night. Length of stay was calculated as the difference between discharge date/time and admission date/time. Discharge order time was collected as a measure of clinician efficiency. The number of consults per admission was determined by the number of different medical or surgical subspecialty services that wrote at least one consultation or progress note after the time of admission and were not the primary service at the time the note was written. The project was reviewed and deemed exempt by the University of Utah Institutional Review Board (IRB 00104884).

Inpatient Care Team Structure

Patients were assigned to one of three cohorts dependent on the assigned treatment team at the time of discharge. The three inpatient team structures were as follows: (1) a “resident team” composed of a senior resident (postgraduate year [PGY] 2 or PGY3) and one to two medical students or one senior resident, two interns (PGY1), and one to two medical students supervised by a hospitalist physician; (2) an “APC team” composed of one to two APCs supervised by a hospitalist physician; and (3) a “hospitalist team” composed of one attending hospitalist independently managing all patients.

Advanced Practice Clinicians

The APC service included 10 APCs (8 PAs and 2 NPs), with a combined workforce of nine APC full-time equivalents during the study period. Their experience ranged from new graduate to 11 years of clinical experience, with an average of 4.2 years. Among the 6 APCs with prior clinical experience, the majority (86%) of their years of clinical experience were within inpatient medicine, oncology, or cardiology. Recognizing the variability in clinical experience, we employed a rigorous onboarding program that entailed an average of 80 hours of didactic sessions including 1:1 teaching of the inpatient Society of Hospital Medicine core lecture series combined with initial intense clinical oversight.¹⁸ This program ranged from 2 weeks to 6 weeks depending on the individual APC’s clinical experience, progress, and comfort working independently. This onboarding program has subsequently been formalized into a 1-year APC fellowship that began after the study period concluded.

The degree of autonomy for each APC was individualized based on their clinical experience and ability to recognize limitations such as medical decision-making, clinical knowledge, and effective use of interprofessional team members (eg, peers, nursing, ancillary staff, consultants, and support personnel). Those APCs who demonstrated a sufficient level of clinical competence functioned with a high level of autonomy. During the day, APCs were expected to be the first point of contact for interprofessional team members, to respond to acute clinical changes in a patient’s condition, and to discuss active issues with the supervising attending, all with the majority of medical decision-making, direct patient communication, documentation, and care coordination performed by the APC. An experienced subset of the APC service was responsible for overnight coverage. Nocturnist APCs independently managed all cross-cover issues on patients assigned to APC and hospitalist teams and performed admissions with very little to no direct supervision of the overnight attending physician.

Patient Admission and Redistribution Process

During the study period, resident teams performed all daytime admissions (6 am to 6 pm) on a rotating basis. On any given day, three of four resident teams performed daytime admissions with the fourth team designated as “golden” and free from admitting duties. Patients admitted during the day remained assigned to the resident team for continuity. The APC and hospitalist teams did not accept new admissions during the day. Nighttime admissions (6 pm to 6 am) were performed by a separate team composed of two senior residents, two interns, one APC, occasional APC and medical students, and one supervising attending hospitalist. This team functioned as a single unit. Nighttime admissions were performed in a sequential and rotating fashion (eg, Intern A > Intern B > Resident A > Resident B > APC > student(s) > Intern A > Intern B, etc). Patients admitted overnight were randomly redistributed the following morning, with the majority reassigned to an APC team or hospitalist team in order to offset the workload of the resident teams performing daytime admissions. Following redistribution, a patient would remain assigned to the daytime APC or hospitalist team for the duration of their hospitalization. The redistribution decisions were based on individual team census, without systematic consideration of an individual patient’s diagnosis, medical complexity, socioeconomic status, or perceived quality of learning potential (eg, good teaching case).

Study Outcomes

We divided study outcomes into two categories, clinical outcomes and resource utilization. Clinical outcomes included LOS, unplanned readmission within 30-days, and inpatient mortality and were designed to measure patient-related outcomes as a reflection of the quality of care delivered by different team structures. Resource utilization included discharge order time, discharge time, consults per admission, and total direct cost, which were designed to measure provider-related differences in efficiency and cost of care.

Statistical analysis

Baseline characteristics and unadjusted outcomes are reported as frequency and percent, normally distributed variables as mean with SD, and nonnormally distributed variables as median with interquartile range (IQR). Baseline characteristics and unadjusted outcomes were compared using the chi-square test or the t test, where appropriate. Multivariable regression analysis using generalized linear models with a log link function and gamma distribution was used for continuous outcomes. Multivariable logistic regression was used for binary outcomes.¹⁰ Covariates included in regression models were age, gender, CCI, transfer from an outside hospital, and nighttime admission. In a sensitivity analysis, we included MS-DRG weight as a covariate for 85% of hospitalizations in our cohort exclusive of observation stays, and our findings were qualitatively similar (data not reported but available on request). Adjusted continuous outcomes were estimated using marginal effects at the means.¹⁹ Due to the sensitivity of cost data and an institutional policy against disclosing cost figures, total direct costs were normalized using the unadjusted median and adjusted mean total direct cost of an admission to an APC team as the normalizing value. A P value cutoff of .05 was used to determine statistical significance. Stata/IC version 16.1 (StataCorp) was used for all analyses.

Study Population

A total of 12,716 hospital admissions were identified during the study period. Of these, 7,943 (62.5%) admissions were assigned to a resident team, 3,519 (27.7%) admissions were assigned to an APC team, and the remaining 1,254 (9.9%) were assigned to a hospitalist team. Baseline patient characteristics are reported in Table 1. Patients admitted to resident teams (mean age [SD], 56.9 [19.1] years) were younger than those admitted to an APC team (58.0 [19.3] years; P = .004) or a hospitalist team (58.2 [19.3] years; P = .026). The case-mix index (mean MS-DRG weight [SD], 1.44 [0.87]) was slightly lower for resident teams than that for APC teams (1.49 [0.90]; P = .025).Resident teams had a significantly lower proportion of night admissions than did APC teams (32.0% vs 49.5%; P < .001) and hospitalist teams (48.6%; P < .001). APC teams were assigned more patients transferred from an outside hospital (19.1%), compared with resident teams (15.0%; P < .001) and hospitalist teams (16.0%; P = .015). No other significant differences were observed in baseline characteristics between cohorts.

Clinical Outcomes

Unadjusted analysis demonstrated the LOS was similar among resident, APC, and hospitalist teams with a median (IQR) LOS of 2.90 (1.86, 4.26) days, 2.93 (1.89, 4.66) days, and 2.86 (1.84, 4.67) days, respectively. No significant differences were observed in unadjusted 30-day readmissions or inpatient mortality among the team structures (Table 2). Following multivariable adjustment for differences in baseline characteristics, no significant differences were observed in LOS, 30-day readmission, or inpatient mortality among teams (Table 3).

Resource Utilization

In unadjusted comparisons, hospitalist teams were observed to place discharge orders more than 30 minutes earlier than APC teams (median hours after midnight [IQR], 11.20 [9.63, 13.60] vs 11.73 [10.00, 13.87]; P < .001) and 54 minutes earlier than resident teams (12.10 [10.38, 13.90]; P < .001) (Table 2). Consistent with the earlier placement of discharge orders, hospitalist patients were also discharged from the hospital 26 and 32 minutes earlier than APC and resident patients, respectively. APC teams also discharged patients slightly earlier (6 minutes) than resident teams (median hours after midnight [IQR], 14.97 [13.23, 16.72] vs 15.07 [13.42, 16.73]; P = .045). Median consultation use among teams was similar, although statistically significant differences were present. Normalized total direct cost was 8% higher (P < .001) for admissions to APC teams than that for resident teams and 7% higher (P = .008) than that for hospitalist teams in unadjusted analysis (Table 2).

Following multivariable adjustment, the mean differences in discharge order time and discharge time remained significant with hospitalist teams discharging patients an average of 20 to 30 minutes earlier than APC and resident teams (Table 3). Consultant utilization remained significantly different between teams, with APC teams utilizing consultants on average 15% more than hospitalist teams (P < .001) and 7% more than resident teams (P = .001). The differences in total direct costs were not significant after adjusted analysis.

Many academic medical centers have expanded their workforce with APC or nonteaching hospitalist teams to accommodate the increasing volume of hospital admissions, resident work hour restrictions,^1,2 and medical complexity of an aging population. Several hospitals have reported comparative outcomes between different care delivery models, with conflicting results.^6,8,10-12 In our study, we directly evaluated three inpatient care delivery models and found that hospitalist teams discharged patients more efficiently and utilized fewer consultants, compared with APC and resident teams. In spite of this improved efficiency, no significant differences were observed in cost or other clinical outcomes.

Our findings are important and further strengthen the evidence supporting the use of APCs on inpatient general medicine services and are of particular interest to academic centers struggling to expand staffing in order to offset the growth in patient volume and reduction in resident workforce. We believe several findings from our study warrant further discussion.

First, although hospitalist teams were able to discharge patients more efficiently, this observation may be influenced by factors of workflow rather than caused by significant disparities in efficiency between provider types (ie, APC vs hospitalist vs resident physician). As with most academic centers, patients assigned to resident teams are presented by house staff to an attending physician who is ultimately responsible for patient care decisions. Therefore, it is conceivable that delays in the discharge process are in part related to the convention of bedside rounding and discussing the care plan prior to discharge.²⁰ In fact, we recognized this as a bottleneck and changed our discharge process for resident teams in June 2017, with a measurable improvement in discharge times. In the absence of this intervention, our observed differences in discharge times among teams may have been even greater.

Second, no significant differences in clinical outcomes were observed in our adjusted analyses, which suggests that a similar quality of care is delivered to patients regardless of team structure, an important observation when considering different staffing models.

Third, we observed a significant increase in consultation use among resident and APC teams, compared with hospitalists. While we are not able to precisely identify the basis for this variation, we believe it could reflect differences in clinical experience, comfort with diagnostic uncertainty, or the unequal distribution of patients transferred from outside hospitals for tertiary care. Interestingly, the greater consultation use did not correlate with higher healthcare costs, a finding recently reported by Stevens et al.²¹

Fourth, we believe the lack of differences in cost and clinical outcomes among team structures may be of particular interest to academic centers when considering physician burnout, salaries, and clinical education. The relationship between clerical burden, such as completing clinical documentation and computerized physician order entry, has been implicated as a risk factor for physician burnout.²² Incorporating APCs into roles similar to those performed by resident physicians may reduce the clerical burden on hospitalists, thereby reducing the risk of physician burnout. The addition of APCs may also represent opportunities for cost savings for healthcare centers when comparing the median salary of an APC to that of an internal medicine hospitalist.^23,24 Moreover, academic hospitalists have been shown to be excellent medical educators and report increased job satisfaction with a variety of duties beyond direct patient care.^24,25 Unforeseen benefits of adding APC teams within our institution has been the added teaching opportunities for APCs and APC students, increased collegiality with the APCs, and the creation of an APC fellowship program with a focus on inpatient medicine. Similar postgraduate training programs have been reported and serve as effective models to train APCs for hospital-based practice.²⁶

Lastly, although this project was conceived and completed prior to the COVID-19 pandemic, our observations may be informative for medical centers experiencing a workforce shortage caused by a surge of COVID-19 patients. During a physician shortage we believe our APC team model could be rapidly expanded to accommodate a large influx of patients. This expansion could be accomplished through a single attending physician overseeing multiple APC teams. In this model, the supervising physician would only evaluate the most complex patients with most patients being managed solely by an APC from admission to discharge. Such changes may require temporary suspension of state laws restricting APC independent practice.^27,28

Our findings contrast those of previous reports in that we did not observe significant differences in clinical outcomes (ie, LOS, inpatient mortality, and 30-day readmissions) or total direct cost.^8,10,21 Other institutions have noted an increased LOS among APC teams and hospitalist teams, compared with resident teams.^8,10 Furthermore, Chin et al and Iannuzzi et al reported reductions in healthcare cost for resident teams, whereas our study did not identify significant cost differences among team structures. Although we cannot pinpoint the exact reason(s) for these dissimilarities, it is plausible that unmeasured factors such as institutional differences in APC training, direct physician supervision, admission processes, or inpatient team census may play a role.

Several study limitations should be recognized. First, the retrospective, nonrandomized design is one of the largest limitations of our study. Administrative data was obtained via an electronic query of our data warehouse, and although we aimed to identify as many patient characteristics as possible to adjust for cofounding effects, undetected differences among cohorts may exist. Second, our inpatient admission process may have placed undue burden on resident teams to perform all daytime admissions, inadvertently affecting study outcomes. It is possible the observed benefits of a solo hospitalist team are attributable to the lack of admitting duties rather than inherent advantages of the team structure. If this were the case, we would expect similar benefits among APC teams, which we did not note. Third, the study was performed at a single academic center, which may limit the generalizability of our results. Fourth, it is possible the outcomes are similar among teams because our hospitalist faculty rotate proportionately between the different teams. Lastly, the study was underpowered to detect a significant difference in mortality between hospitalist and APC teams. A post hoc power calculation based on our observed sample and effect sizes estimated 75% power to detect a mortality difference between hospitalists and APCs; other mortality comparisons were adequately powered.

We observed similar total direct costs, LOS, 30-day readmission, and inpatient mortality between hospitalist, APC, and resident teams. APC and resident teams utilized more consultants and discharged patient later than hospitalists. Our analysis suggests clinical outcomes are not significantly affected by inpatient team structure, and the addition of general medicine inpatient APC or hospitalist teams represent safe and efficient alternatives to traditional resident teams within an academic medical center.

All authors declare they have no conflicts of interest.

The Accreditation Council for Graduate Medical Education (ACGME) first mandated residency work hour restrictions in 2003.¹ In 2011, revised work hour requirements were issued, further limiting the maximum duration of a shift and extending the duration of time off between scheduled shifts.² Academic medical centers have been forced to adapt to work hour restrictions, and cuts in funding to research and educational missions have pressured institutions to restructure with a greater focus on high-quality, lower-cost care.^3,4 In response, many academic hospitals have added hospitalist teams, or incorporated advanced practice clinicians (APCs) (nurse practitioners [NPs] and physician assistants [PAs]) to accommodate resident physician duty hour restrictions on their inpatient general medicine services.^5,6 More recently, the COVID-19 pandemic has created unanticipated physician shortages forcing medical centers to rapidly expand and broaden the scope of their existing APC workforce.⁷

Several comparisons of clinical outcomes, cost, and patient satisfaction between different combinations of hospitalist-based, resident-based, or APC-based inpatient teams have been reported with conflicting observations.^6,8-14 Roy et al reported no significant differences in mortality, length of stay (LOS), or readmissions between PA and resident teams.⁶ Timmermans et al reported similar cost-effectiveness, LOS, and quality of care between PA and physician teams that included a hybrid of attending only and resident teams.^13,14 Alternatively, Singh et al and Iannuzzi et al reported increased LOS among PA teams,^10,12 whereas Chin et al observed an increased LOS and reduced 30-day readmissions among hospitalist teams.⁸ While these observed differences may be attributable to heterogeneous patient populations or institution-specific team structure, the exact reasons remain unknown. Furthermore, understanding the value of alternate staffing models is essential for medical centers to prepare for potential COVID-19 related physician shortages. To our knowledge, no study to date has directly compared outcomes between resident, APC, and hospitalist team structures within an academic medical center.

We believe our institution provides a unique environment to study the differences in inpatient general medicine team structure with respect to quality and efficiency of care delivery. The objective of our study is to directly compare clinical outcomes and resource utilization among three distinct team structures: APC, resident, and solo hospitalist. We hypothesize that clinical outcomes, cost, and utilization of consult services will be similar across all team structures and hospitalist teams will discharge patients earlier than resident and APC teams.

Study Design and Setting

We conducted a retrospective observational cohort study at the University of Utah Medical Center, a 548-bed academic medical center in Salt Lake City. An electronic database query was used to identify all patients discharged from the inpatient general internal medicine service between July 1, 2015, and July 1, 2018. Baseline patient characteristics were collected including age, gender, and Charlson comorbidity index (CCI).¹⁵ Case-mix index was determined for admissions where a Medicare Severity Diagnosis Related Group (MS-DRG) and corresponding weight was assigned.^16,17 Source of admission was collected to identify patients transferred from an outside hospital, typically due to increased medical complexity or need for specialty care not available at the referring center. Time of admission was collected to classify whether a patient was admitted during the day or at night. Length of stay was calculated as the difference between discharge date/time and admission date/time. Discharge order time was collected as a measure of clinician efficiency. The number of consults per admission was determined by the number of different medical or surgical subspecialty services that wrote at least one consultation or progress note after the time of admission and were not the primary service at the time the note was written. The project was reviewed and deemed exempt by the University of Utah Institutional Review Board (IRB 00104884).

Inpatient Care Team Structure

Patients were assigned to one of three cohorts dependent on the assigned treatment team at the time of discharge. The three inpatient team structures were as follows: (1) a “resident team” composed of a senior resident (postgraduate year [PGY] 2 or PGY3) and one to two medical students or one senior resident, two interns (PGY1), and one to two medical students supervised by a hospitalist physician; (2) an “APC team” composed of one to two APCs supervised by a hospitalist physician; and (3) a “hospitalist team” composed of one attending hospitalist independently managing all patients.

Advanced Practice Clinicians

The APC service included 10 APCs (8 PAs and 2 NPs), with a combined workforce of nine APC full-time equivalents during the study period. Their experience ranged from new graduate to 11 years of clinical experience, with an average of 4.2 years. Among the 6 APCs with prior clinical experience, the majority (86%) of their years of clinical experience were within inpatient medicine, oncology, or cardiology. Recognizing the variability in clinical experience, we employed a rigorous onboarding program that entailed an average of 80 hours of didactic sessions including 1:1 teaching of the inpatient Society of Hospital Medicine core lecture series combined with initial intense clinical oversight.¹⁸ This program ranged from 2 weeks to 6 weeks depending on the individual APC’s clinical experience, progress, and comfort working independently. This onboarding program has subsequently been formalized into a 1-year APC fellowship that began after the study period concluded.

The degree of autonomy for each APC was individualized based on their clinical experience and ability to recognize limitations such as medical decision-making, clinical knowledge, and effective use of interprofessional team members (eg, peers, nursing, ancillary staff, consultants, and support personnel). Those APCs who demonstrated a sufficient level of clinical competence functioned with a high level of autonomy. During the day, APCs were expected to be the first point of contact for interprofessional team members, to respond to acute clinical changes in a patient’s condition, and to discuss active issues with the supervising attending, all with the majority of medical decision-making, direct patient communication, documentation, and care coordination performed by the APC. An experienced subset of the APC service was responsible for overnight coverage. Nocturnist APCs independently managed all cross-cover issues on patients assigned to APC and hospitalist teams and performed admissions with very little to no direct supervision of the overnight attending physician.

Patient Admission and Redistribution Process

During the study period, resident teams performed all daytime admissions (6 am to 6 pm) on a rotating basis. On any given day, three of four resident teams performed daytime admissions with the fourth team designated as “golden” and free from admitting duties. Patients admitted during the day remained assigned to the resident team for continuity. The APC and hospitalist teams did not accept new admissions during the day. Nighttime admissions (6 pm to 6 am) were performed by a separate team composed of two senior residents, two interns, one APC, occasional APC and medical students, and one supervising attending hospitalist. This team functioned as a single unit. Nighttime admissions were performed in a sequential and rotating fashion (eg, Intern A > Intern B > Resident A > Resident B > APC > student(s) > Intern A > Intern B, etc). Patients admitted overnight were randomly redistributed the following morning, with the majority reassigned to an APC team or hospitalist team in order to offset the workload of the resident teams performing daytime admissions. Following redistribution, a patient would remain assigned to the daytime APC or hospitalist team for the duration of their hospitalization. The redistribution decisions were based on individual team census, without systematic consideration of an individual patient’s diagnosis, medical complexity, socioeconomic status, or perceived quality of learning potential (eg, good teaching case).

Study Outcomes

We divided study outcomes into two categories, clinical outcomes and resource utilization. Clinical outcomes included LOS, unplanned readmission within 30-days, and inpatient mortality and were designed to measure patient-related outcomes as a reflection of the quality of care delivered by different team structures. Resource utilization included discharge order time, discharge time, consults per admission, and total direct cost, which were designed to measure provider-related differences in efficiency and cost of care.

Statistical analysis

Baseline characteristics and unadjusted outcomes are reported as frequency and percent, normally distributed variables as mean with SD, and nonnormally distributed variables as median with interquartile range (IQR). Baseline characteristics and unadjusted outcomes were compared using the chi-square test or the t test, where appropriate. Multivariable regression analysis using generalized linear models with a log link function and gamma distribution was used for continuous outcomes. Multivariable logistic regression was used for binary outcomes.¹⁰ Covariates included in regression models were age, gender, CCI, transfer from an outside hospital, and nighttime admission. In a sensitivity analysis, we included MS-DRG weight as a covariate for 85% of hospitalizations in our cohort exclusive of observation stays, and our findings were qualitatively similar (data not reported but available on request). Adjusted continuous outcomes were estimated using marginal effects at the means.¹⁹ Due to the sensitivity of cost data and an institutional policy against disclosing cost figures, total direct costs were normalized using the unadjusted median and adjusted mean total direct cost of an admission to an APC team as the normalizing value. A P value cutoff of .05 was used to determine statistical significance. Stata/IC version 16.1 (StataCorp) was used for all analyses.

Study Population

A total of 12,716 hospital admissions were identified during the study period. Of these, 7,943 (62.5%) admissions were assigned to a resident team, 3,519 (27.7%) admissions were assigned to an APC team, and the remaining 1,254 (9.9%) were assigned to a hospitalist team. Baseline patient characteristics are reported in Table 1. Patients admitted to resident teams (mean age [SD], 56.9 [19.1] years) were younger than those admitted to an APC team (58.0 [19.3] years; P = .004) or a hospitalist team (58.2 [19.3] years; P = .026). The case-mix index (mean MS-DRG weight [SD], 1.44 [0.87]) was slightly lower for resident teams than that for APC teams (1.49 [0.90]; P = .025).Resident teams had a significantly lower proportion of night admissions than did APC teams (32.0% vs 49.5%; P < .001) and hospitalist teams (48.6%; P < .001). APC teams were assigned more patients transferred from an outside hospital (19.1%), compared with resident teams (15.0%; P < .001) and hospitalist teams (16.0%; P = .015). No other significant differences were observed in baseline characteristics between cohorts.

Clinical Outcomes

Unadjusted analysis demonstrated the LOS was similar among resident, APC, and hospitalist teams with a median (IQR) LOS of 2.90 (1.86, 4.26) days, 2.93 (1.89, 4.66) days, and 2.86 (1.84, 4.67) days, respectively. No significant differences were observed in unadjusted 30-day readmissions or inpatient mortality among the team structures (Table 2). Following multivariable adjustment for differences in baseline characteristics, no significant differences were observed in LOS, 30-day readmission, or inpatient mortality among teams (Table 3).

Resource Utilization

In unadjusted comparisons, hospitalist teams were observed to place discharge orders more than 30 minutes earlier than APC teams (median hours after midnight [IQR], 11.20 [9.63, 13.60] vs 11.73 [10.00, 13.87]; P < .001) and 54 minutes earlier than resident teams (12.10 [10.38, 13.90]; P < .001) (Table 2). Consistent with the earlier placement of discharge orders, hospitalist patients were also discharged from the hospital 26 and 32 minutes earlier than APC and resident patients, respectively. APC teams also discharged patients slightly earlier (6 minutes) than resident teams (median hours after midnight [IQR], 14.97 [13.23, 16.72] vs 15.07 [13.42, 16.73]; P = .045). Median consultation use among teams was similar, although statistically significant differences were present. Normalized total direct cost was 8% higher (P < .001) for admissions to APC teams than that for resident teams and 7% higher (P = .008) than that for hospitalist teams in unadjusted analysis (Table 2).

Following multivariable adjustment, the mean differences in discharge order time and discharge time remained significant with hospitalist teams discharging patients an average of 20 to 30 minutes earlier than APC and resident teams (Table 3). Consultant utilization remained significantly different between teams, with APC teams utilizing consultants on average 15% more than hospitalist teams (P < .001) and 7% more than resident teams (P = .001). The differences in total direct costs were not significant after adjusted analysis.

Many academic medical centers have expanded their workforce with APC or nonteaching hospitalist teams to accommodate the increasing volume of hospital admissions, resident work hour restrictions,^1,2 and medical complexity of an aging population. Several hospitals have reported comparative outcomes between different care delivery models, with conflicting results.^6,8,10-12 In our study, we directly evaluated three inpatient care delivery models and found that hospitalist teams discharged patients more efficiently and utilized fewer consultants, compared with APC and resident teams. In spite of this improved efficiency, no significant differences were observed in cost or other clinical outcomes.

Our findings are important and further strengthen the evidence supporting the use of APCs on inpatient general medicine services and are of particular interest to academic centers struggling to expand staffing in order to offset the growth in patient volume and reduction in resident workforce. We believe several findings from our study warrant further discussion.

First, although hospitalist teams were able to discharge patients more efficiently, this observation may be influenced by factors of workflow rather than caused by significant disparities in efficiency between provider types (ie, APC vs hospitalist vs resident physician). As with most academic centers, patients assigned to resident teams are presented by house staff to an attending physician who is ultimately responsible for patient care decisions. Therefore, it is conceivable that delays in the discharge process are in part related to the convention of bedside rounding and discussing the care plan prior to discharge.²⁰ In fact, we recognized this as a bottleneck and changed our discharge process for resident teams in June 2017, with a measurable improvement in discharge times. In the absence of this intervention, our observed differences in discharge times among teams may have been even greater.

Second, no significant differences in clinical outcomes were observed in our adjusted analyses, which suggests that a similar quality of care is delivered to patients regardless of team structure, an important observation when considering different staffing models.

Third, we observed a significant increase in consultation use among resident and APC teams, compared with hospitalists. While we are not able to precisely identify the basis for this variation, we believe it could reflect differences in clinical experience, comfort with diagnostic uncertainty, or the unequal distribution of patients transferred from outside hospitals for tertiary care. Interestingly, the greater consultation use did not correlate with higher healthcare costs, a finding recently reported by Stevens et al.²¹

Fourth, we believe the lack of differences in cost and clinical outcomes among team structures may be of particular interest to academic centers when considering physician burnout, salaries, and clinical education. The relationship between clerical burden, such as completing clinical documentation and computerized physician order entry, has been implicated as a risk factor for physician burnout.²² Incorporating APCs into roles similar to those performed by resident physicians may reduce the clerical burden on hospitalists, thereby reducing the risk of physician burnout. The addition of APCs may also represent opportunities for cost savings for healthcare centers when comparing the median salary of an APC to that of an internal medicine hospitalist.^23,24 Moreover, academic hospitalists have been shown to be excellent medical educators and report increased job satisfaction with a variety of duties beyond direct patient care.^24,25 Unforeseen benefits of adding APC teams within our institution has been the added teaching opportunities for APCs and APC students, increased collegiality with the APCs, and the creation of an APC fellowship program with a focus on inpatient medicine. Similar postgraduate training programs have been reported and serve as effective models to train APCs for hospital-based practice.²⁶

Lastly, although this project was conceived and completed prior to the COVID-19 pandemic, our observations may be informative for medical centers experiencing a workforce shortage caused by a surge of COVID-19 patients. During a physician shortage we believe our APC team model could be rapidly expanded to accommodate a large influx of patients. This expansion could be accomplished through a single attending physician overseeing multiple APC teams. In this model, the supervising physician would only evaluate the most complex patients with most patients being managed solely by an APC from admission to discharge. Such changes may require temporary suspension of state laws restricting APC independent practice.^27,28

Our findings contrast those of previous reports in that we did not observe significant differences in clinical outcomes (ie, LOS, inpatient mortality, and 30-day readmissions) or total direct cost.^8,10,21 Other institutions have noted an increased LOS among APC teams and hospitalist teams, compared with resident teams.^8,10 Furthermore, Chin et al and Iannuzzi et al reported reductions in healthcare cost for resident teams, whereas our study did not identify significant cost differences among team structures. Although we cannot pinpoint the exact reason(s) for these dissimilarities, it is plausible that unmeasured factors such as institutional differences in APC training, direct physician supervision, admission processes, or inpatient team census may play a role.

Several study limitations should be recognized. First, the retrospective, nonrandomized design is one of the largest limitations of our study. Administrative data was obtained via an electronic query of our data warehouse, and although we aimed to identify as many patient characteristics as possible to adjust for cofounding effects, undetected differences among cohorts may exist. Second, our inpatient admission process may have placed undue burden on resident teams to perform all daytime admissions, inadvertently affecting study outcomes. It is possible the observed benefits of a solo hospitalist team are attributable to the lack of admitting duties rather than inherent advantages of the team structure. If this were the case, we would expect similar benefits among APC teams, which we did not note. Third, the study was performed at a single academic center, which may limit the generalizability of our results. Fourth, it is possible the outcomes are similar among teams because our hospitalist faculty rotate proportionately between the different teams. Lastly, the study was underpowered to detect a significant difference in mortality between hospitalist and APC teams. A post hoc power calculation based on our observed sample and effect sizes estimated 75% power to detect a mortality difference between hospitalists and APCs; other mortality comparisons were adequately powered.

We observed similar total direct costs, LOS, 30-day readmission, and inpatient mortality between hospitalist, APC, and resident teams. APC and resident teams utilized more consultants and discharged patient later than hospitalists. Our analysis suggests clinical outcomes are not significantly affected by inpatient team structure, and the addition of general medicine inpatient APC or hospitalist teams represent safe and efficient alternatives to traditional resident teams within an academic medical center.

All authors declare they have no conflicts of interest.

The Accreditation Council for Graduate Medical Education (ACGME) first mandated residency work hour restrictions in 2003.¹ In 2011, revised work hour requirements were issued, further limiting the maximum duration of a shift and extending the duration of time off between scheduled shifts.² Academic medical centers have been forced to adapt to work hour restrictions, and cuts in funding to research and educational missions have pressured institutions to restructure with a greater focus on high-quality, lower-cost care.^3,4 In response, many academic hospitals have added hospitalist teams, or incorporated advanced practice clinicians (APCs) (nurse practitioners [NPs] and physician assistants [PAs]) to accommodate resident physician duty hour restrictions on their inpatient general medicine services.^5,6 More recently, the COVID-19 pandemic has created unanticipated physician shortages forcing medical centers to rapidly expand and broaden the scope of their existing APC workforce.⁷

Several comparisons of clinical outcomes, cost, and patient satisfaction between different combinations of hospitalist-based, resident-based, or APC-based inpatient teams have been reported with conflicting observations.^6,8-14 Roy et al reported no significant differences in mortality, length of stay (LOS), or readmissions between PA and resident teams.⁶ Timmermans et al reported similar cost-effectiveness, LOS, and quality of care between PA and physician teams that included a hybrid of attending only and resident teams.^13,14 Alternatively, Singh et al and Iannuzzi et al reported increased LOS among PA teams,^10,12 whereas Chin et al observed an increased LOS and reduced 30-day readmissions among hospitalist teams.⁸ While these observed differences may be attributable to heterogeneous patient populations or institution-specific team structure, the exact reasons remain unknown. Furthermore, understanding the value of alternate staffing models is essential for medical centers to prepare for potential COVID-19 related physician shortages. To our knowledge, no study to date has directly compared outcomes between resident, APC, and hospitalist team structures within an academic medical center.

We believe our institution provides a unique environment to study the differences in inpatient general medicine team structure with respect to quality and efficiency of care delivery. The objective of our study is to directly compare clinical outcomes and resource utilization among three distinct team structures: APC, resident, and solo hospitalist. We hypothesize that clinical outcomes, cost, and utilization of consult services will be similar across all team structures and hospitalist teams will discharge patients earlier than resident and APC teams.

Study Design and Setting

We conducted a retrospective observational cohort study at the University of Utah Medical Center, a 548-bed academic medical center in Salt Lake City. An electronic database query was used to identify all patients discharged from the inpatient general internal medicine service between July 1, 2015, and July 1, 2018. Baseline patient characteristics were collected including age, gender, and Charlson comorbidity index (CCI).¹⁵ Case-mix index was determined for admissions where a Medicare Severity Diagnosis Related Group (MS-DRG) and corresponding weight was assigned.^16,17 Source of admission was collected to identify patients transferred from an outside hospital, typically due to increased medical complexity or need for specialty care not available at the referring center. Time of admission was collected to classify whether a patient was admitted during the day or at night. Length of stay was calculated as the difference between discharge date/time and admission date/time. Discharge order time was collected as a measure of clinician efficiency. The number of consults per admission was determined by the number of different medical or surgical subspecialty services that wrote at least one consultation or progress note after the time of admission and were not the primary service at the time the note was written. The project was reviewed and deemed exempt by the University of Utah Institutional Review Board (IRB 00104884).

Inpatient Care Team Structure

Patients were assigned to one of three cohorts dependent on the assigned treatment team at the time of discharge. The three inpatient team structures were as follows: (1) a “resident team” composed of a senior resident (postgraduate year [PGY] 2 or PGY3) and one to two medical students or one senior resident, two interns (PGY1), and one to two medical students supervised by a hospitalist physician; (2) an “APC team” composed of one to two APCs supervised by a hospitalist physician; and (3) a “hospitalist team” composed of one attending hospitalist independently managing all patients.

Advanced Practice Clinicians

The APC service included 10 APCs (8 PAs and 2 NPs), with a combined workforce of nine APC full-time equivalents during the study period. Their experience ranged from new graduate to 11 years of clinical experience, with an average of 4.2 years. Among the 6 APCs with prior clinical experience, the majority (86%) of their years of clinical experience were within inpatient medicine, oncology, or cardiology. Recognizing the variability in clinical experience, we employed a rigorous onboarding program that entailed an average of 80 hours of didactic sessions including 1:1 teaching of the inpatient Society of Hospital Medicine core lecture series combined with initial intense clinical oversight.¹⁸ This program ranged from 2 weeks to 6 weeks depending on the individual APC’s clinical experience, progress, and comfort working independently. This onboarding program has subsequently been formalized into a 1-year APC fellowship that began after the study period concluded.

The degree of autonomy for each APC was individualized based on their clinical experience and ability to recognize limitations such as medical decision-making, clinical knowledge, and effective use of interprofessional team members (eg, peers, nursing, ancillary staff, consultants, and support personnel). Those APCs who demonstrated a sufficient level of clinical competence functioned with a high level of autonomy. During the day, APCs were expected to be the first point of contact for interprofessional team members, to respond to acute clinical changes in a patient’s condition, and to discuss active issues with the supervising attending, all with the majority of medical decision-making, direct patient communication, documentation, and care coordination performed by the APC. An experienced subset of the APC service was responsible for overnight coverage. Nocturnist APCs independently managed all cross-cover issues on patients assigned to APC and hospitalist teams and performed admissions with very little to no direct supervision of the overnight attending physician.

Patient Admission and Redistribution Process

During the study period, resident teams performed all daytime admissions (6 am to 6 pm) on a rotating basis. On any given day, three of four resident teams performed daytime admissions with the fourth team designated as “golden” and free from admitting duties. Patients admitted during the day remained assigned to the resident team for continuity. The APC and hospitalist teams did not accept new admissions during the day. Nighttime admissions (6 pm to 6 am) were performed by a separate team composed of two senior residents, two interns, one APC, occasional APC and medical students, and one supervising attending hospitalist. This team functioned as a single unit. Nighttime admissions were performed in a sequential and rotating fashion (eg, Intern A > Intern B > Resident A > Resident B > APC > student(s) > Intern A > Intern B, etc). Patients admitted overnight were randomly redistributed the following morning, with the majority reassigned to an APC team or hospitalist team in order to offset the workload of the resident teams performing daytime admissions. Following redistribution, a patient would remain assigned to the daytime APC or hospitalist team for the duration of their hospitalization. The redistribution decisions were based on individual team census, without systematic consideration of an individual patient’s diagnosis, medical complexity, socioeconomic status, or perceived quality of learning potential (eg, good teaching case).

Study Outcomes

We divided study outcomes into two categories, clinical outcomes and resource utilization. Clinical outcomes included LOS, unplanned readmission within 30-days, and inpatient mortality and were designed to measure patient-related outcomes as a reflection of the quality of care delivered by different team structures. Resource utilization included discharge order time, discharge time, consults per admission, and total direct cost, which were designed to measure provider-related differences in efficiency and cost of care.

Statistical analysis

Baseline characteristics and unadjusted outcomes are reported as frequency and percent, normally distributed variables as mean with SD, and nonnormally distributed variables as median with interquartile range (IQR). Baseline characteristics and unadjusted outcomes were compared using the chi-square test or the t test, where appropriate. Multivariable regression analysis using generalized linear models with a log link function and gamma distribution was used for continuous outcomes. Multivariable logistic regression was used for binary outcomes.¹⁰ Covariates included in regression models were age, gender, CCI, transfer from an outside hospital, and nighttime admission. In a sensitivity analysis, we included MS-DRG weight as a covariate for 85% of hospitalizations in our cohort exclusive of observation stays, and our findings were qualitatively similar (data not reported but available on request). Adjusted continuous outcomes were estimated using marginal effects at the means.¹⁹ Due to the sensitivity of cost data and an institutional policy against disclosing cost figures, total direct costs were normalized using the unadjusted median and adjusted mean total direct cost of an admission to an APC team as the normalizing value. A P value cutoff of .05 was used to determine statistical significance. Stata/IC version 16.1 (StataCorp) was used for all analyses.

Study Population

A total of 12,716 hospital admissions were identified during the study period. Of these, 7,943 (62.5%) admissions were assigned to a resident team, 3,519 (27.7%) admissions were assigned to an APC team, and the remaining 1,254 (9.9%) were assigned to a hospitalist team. Baseline patient characteristics are reported in Table 1. Patients admitted to resident teams (mean age [SD], 56.9 [19.1] years) were younger than those admitted to an APC team (58.0 [19.3] years; P = .004) or a hospitalist team (58.2 [19.3] years; P = .026). The case-mix index (mean MS-DRG weight [SD], 1.44 [0.87]) was slightly lower for resident teams than that for APC teams (1.49 [0.90]; P = .025).Resident teams had a significantly lower proportion of night admissions than did APC teams (32.0% vs 49.5%; P < .001) and hospitalist teams (48.6%; P < .001). APC teams were assigned more patients transferred from an outside hospital (19.1%), compared with resident teams (15.0%; P < .001) and hospitalist teams (16.0%; P = .015). No other significant differences were observed in baseline characteristics between cohorts.

Clinical Outcomes

Unadjusted analysis demonstrated the LOS was similar among resident, APC, and hospitalist teams with a median (IQR) LOS of 2.90 (1.86, 4.26) days, 2.93 (1.89, 4.66) days, and 2.86 (1.84, 4.67) days, respectively. No significant differences were observed in unadjusted 30-day readmissions or inpatient mortality among the team structures (Table 2). Following multivariable adjustment for differences in baseline characteristics, no significant differences were observed in LOS, 30-day readmission, or inpatient mortality among teams (Table 3).

Resource Utilization

In unadjusted comparisons, hospitalist teams were observed to place discharge orders more than 30 minutes earlier than APC teams (median hours after midnight [IQR], 11.20 [9.63, 13.60] vs 11.73 [10.00, 13.87]; P < .001) and 54 minutes earlier than resident teams (12.10 [10.38, 13.90]; P < .001) (Table 2). Consistent with the earlier placement of discharge orders, hospitalist patients were also discharged from the hospital 26 and 32 minutes earlier than APC and resident patients, respectively. APC teams also discharged patients slightly earlier (6 minutes) than resident teams (median hours after midnight [IQR], 14.97 [13.23, 16.72] vs 15.07 [13.42, 16.73]; P = .045). Median consultation use among teams was similar, although statistically significant differences were present. Normalized total direct cost was 8% higher (P < .001) for admissions to APC teams than that for resident teams and 7% higher (P = .008) than that for hospitalist teams in unadjusted analysis (Table 2).

Following multivariable adjustment, the mean differences in discharge order time and discharge time remained significant with hospitalist teams discharging patients an average of 20 to 30 minutes earlier than APC and resident teams (Table 3). Consultant utilization remained significantly different between teams, with APC teams utilizing consultants on average 15% more than hospitalist teams (P < .001) and 7% more than resident teams (P = .001). The differences in total direct costs were not significant after adjusted analysis.

Many academic medical centers have expanded their workforce with APC or nonteaching hospitalist teams to accommodate the increasing volume of hospital admissions, resident work hour restrictions,^1,2 and medical complexity of an aging population. Several hospitals have reported comparative outcomes between different care delivery models, with conflicting results.^6,8,10-12 In our study, we directly evaluated three inpatient care delivery models and found that hospitalist teams discharged patients more efficiently and utilized fewer consultants, compared with APC and resident teams. In spite of this improved efficiency, no significant differences were observed in cost or other clinical outcomes.

Our findings are important and further strengthen the evidence supporting the use of APCs on inpatient general medicine services and are of particular interest to academic centers struggling to expand staffing in order to offset the growth in patient volume and reduction in resident workforce. We believe several findings from our study warrant further discussion.

First, although hospitalist teams were able to discharge patients more efficiently, this observation may be influenced by factors of workflow rather than caused by significant disparities in efficiency between provider types (ie, APC vs hospitalist vs resident physician). As with most academic centers, patients assigned to resident teams are presented by house staff to an attending physician who is ultimately responsible for patient care decisions. Therefore, it is conceivable that delays in the discharge process are in part related to the convention of bedside rounding and discussing the care plan prior to discharge.²⁰ In fact, we recognized this as a bottleneck and changed our discharge process for resident teams in June 2017, with a measurable improvement in discharge times. In the absence of this intervention, our observed differences in discharge times among teams may have been even greater.

Second, no significant differences in clinical outcomes were observed in our adjusted analyses, which suggests that a similar quality of care is delivered to patients regardless of team structure, an important observation when considering different staffing models.

Third, we observed a significant increase in consultation use among resident and APC teams, compared with hospitalists. While we are not able to precisely identify the basis for this variation, we believe it could reflect differences in clinical experience, comfort with diagnostic uncertainty, or the unequal distribution of patients transferred from outside hospitals for tertiary care. Interestingly, the greater consultation use did not correlate with higher healthcare costs, a finding recently reported by Stevens et al.²¹

Fourth, we believe the lack of differences in cost and clinical outcomes among team structures may be of particular interest to academic centers when considering physician burnout, salaries, and clinical education. The relationship between clerical burden, such as completing clinical documentation and computerized physician order entry, has been implicated as a risk factor for physician burnout.²² Incorporating APCs into roles similar to those performed by resident physicians may reduce the clerical burden on hospitalists, thereby reducing the risk of physician burnout. The addition of APCs may also represent opportunities for cost savings for healthcare centers when comparing the median salary of an APC to that of an internal medicine hospitalist.^23,24 Moreover, academic hospitalists have been shown to be excellent medical educators and report increased job satisfaction with a variety of duties beyond direct patient care.^24,25 Unforeseen benefits of adding APC teams within our institution has been the added teaching opportunities for APCs and APC students, increased collegiality with the APCs, and the creation of an APC fellowship program with a focus on inpatient medicine. Similar postgraduate training programs have been reported and serve as effective models to train APCs for hospital-based practice.²⁶

Lastly, although this project was conceived and completed prior to the COVID-19 pandemic, our observations may be informative for medical centers experiencing a workforce shortage caused by a surge of COVID-19 patients. During a physician shortage we believe our APC team model could be rapidly expanded to accommodate a large influx of patients. This expansion could be accomplished through a single attending physician overseeing multiple APC teams. In this model, the supervising physician would only evaluate the most complex patients with most patients being managed solely by an APC from admission to discharge. Such changes may require temporary suspension of state laws restricting APC independent practice.^27,28

Our findings contrast those of previous reports in that we did not observe significant differences in clinical outcomes (ie, LOS, inpatient mortality, and 30-day readmissions) or total direct cost.^8,10,21 Other institutions have noted an increased LOS among APC teams and hospitalist teams, compared with resident teams.^8,10 Furthermore, Chin et al and Iannuzzi et al reported reductions in healthcare cost for resident teams, whereas our study did not identify significant cost differences among team structures. Although we cannot pinpoint the exact reason(s) for these dissimilarities, it is plausible that unmeasured factors such as institutional differences in APC training, direct physician supervision, admission processes, or inpatient team census may play a role.

Several study limitations should be recognized. First, the retrospective, nonrandomized design is one of the largest limitations of our study. Administrative data was obtained via an electronic query of our data warehouse, and although we aimed to identify as many patient characteristics as possible to adjust for cofounding effects, undetected differences among cohorts may exist. Second, our inpatient admission process may have placed undue burden on resident teams to perform all daytime admissions, inadvertently affecting study outcomes. It is possible the observed benefits of a solo hospitalist team are attributable to the lack of admitting duties rather than inherent advantages of the team structure. If this were the case, we would expect similar benefits among APC teams, which we did not note. Third, the study was performed at a single academic center, which may limit the generalizability of our results. Fourth, it is possible the outcomes are similar among teams because our hospitalist faculty rotate proportionately between the different teams. Lastly, the study was underpowered to detect a significant difference in mortality between hospitalist and APC teams. A post hoc power calculation based on our observed sample and effect sizes estimated 75% power to detect a mortality difference between hospitalists and APCs; other mortality comparisons were adequately powered.

We observed similar total direct costs, LOS, 30-day readmission, and inpatient mortality between hospitalist, APC, and resident teams. APC and resident teams utilized more consultants and discharged patient later than hospitalists. Our analysis suggests clinical outcomes are not significantly affected by inpatient team structure, and the addition of general medicine inpatient APC or hospitalist teams represent safe and efficient alternatives to traditional resident teams within an academic medical center.

All authors declare they have no conflicts of interest.

Readmission rates are frequently used as a hospital quality metric, with use including payment incentive at the hospital level,¹ specific condition quality measurement,² balancing measures for quality improvement projects,^3-5 transition success,^6,7 and use in public hospital rankings.⁸ Currently, four methods are commonly used to evaluate pediatric readmissions, each with strengths and limitations, including the following (Appendix Table 1):

1. All-cause readmissions: A measure of any readmission within a given time period regardless of the reason for readmission.⁹

2. Unplanned readmission/time flag: A measure intended to identify unplanned readmissions. This measure relies on time designations within the electronic health record. The time between hospital registration and admission is calculated, and if the readmission is registered more than 24 hours prior to admission, the readmission is considered planned.¹⁰ Hereafter, this measure will be referred to as the time flag measure.

3. Pediatric all-condition readmission (PACR): A measure intended to identify unplanned readmission through the exclusion of certain procedures and diagnoses.¹¹

4. Potentially preventable readmission (PPR): A method to identify preventable readmissions based on a proprietary algorithm developed by 3M Health Information Systems.^12,13

While all four of these measures are used to assess quality, there is little known about these measures’ ability to exclude planned readmissions and identify only preventable pediatric readmission, which conceptually is most relevant to the quality of care. However, many of these measures were not intended to capture preventability, but instead capture the related issue of whether the readmission was planned. Therefore, we sought to evaluate the four readmission measures as they relate to both preventability and unplanned status as determined through medical record review with multidisciplinary care provider input.

As part of a hospital-wide readmission reduction quality improvement collaborative at a free-standing tertiary care children’s hospital, clinicians from hospital medicine, cardiology, neonatology, and neurology teams reviewed 30-day readmissions using a standardized abstraction tool. All readmission events (observation or inpatient encounter) after any discharge (observation or inpatient encounter) from eligible units were reviewed; therefore, each hospitalization was a potential index hospitalization. We classified the preventability of each readmission with use of a previously described Likert scale with high interrater reliability.¹⁴ For these analyses, readmissions were considered preventable if the reviewing team rated them as either “more likely preventable” or “preventable in most circumstances.” Each readmission was also evaluated as planned or unplanned. Methods for readmission review and classification are in the Appendix.

We included all readmissions between July 2014 and June 2016. We compared the medical record review classifications with the assessments from each of the four measures of pediatric readmission. We calculated sensitivity and specificity for both outcomes (planned/unplanned and preventable/not preventable) for all four measures. For standardization of discussion, we categorized description of measure performance as “very poor” as less than 50%, “poor” between 50%-75%, “fair” as 75%-85%, “good” as 85%-90%, “very good” as 90%-95% and excellent as greater than 95%. We also calculated positive and negative predictive value (PPV and NPV) over plausible ranges of prevalence using the sensitivity and specificity of each comparison (Appendix).

Of note, certain exclusions are outlined by the PACR and PPR algorithms. The PACR evaluates only readmission events that occur in children younger than 18 years. The PPR algorithm does not assign preventability if either the index or readmission event is classified as an observation stay or if it is part of a larger chain of readmissions.

Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68.5%). The median time to readmission was 7 days (interquartile range [IQR], 4-18). Most children were non-Hispanic White (71%) or Black (20%). The median age at hospitalization was 2.3 years (IQR 0.4-12.1). Most children had Medicaid (56%) or private (41%) insurance. Most of the reviews were performed in cardiology (43%) and hospital medicine (37%) with patients in neurology (13%) and neonatology (7%) constituting the remaining reviews. Uncontrolled advancement of chronic disease was the most common readmission category on medical record review (25.1%), followed by unrelated readmission (20.7%), scheduled readmission (20.4%), and progression of acute disease (16.6%) (Appendix Table 2).

Assessment of Preventable and Unplanned Readmissions

On multidisciplinary medical record review, most readmissions were classified as not preventable (84.5%). Specifically, 64% were not preventable and unplanned; 20% were deemed not preventable and planned. Only 15% were classified as unplanned and preventable and 1% as planned and preventable (Appendix Figure: Population A/B).

Matching Chart Review to the Four Algorithms

All 1,125 readmissions were assessed by the all-cause and time flag readmission measures (Appendix Figure: Population A/B). After applying algorithm exclusions (details in Appendix), only 804 of the 1,125 (71.5%) reviewed readmissions matched for PACR readmission comparison (Appendix Figure: Population C); 487 of the 1,125 (43.3%) of the reviewed readmissions matched for PPR comparison (Appendix Figure: Population D).

All-Cause

Because all-cause determines only if a readmission occurs, the measure is by definition 100% sensitive and 0% specific in both assessment of preventability and unplanned readmission (Table: Section A).

Time Flag

The time flag measure identified 80% (866/1,112) of the readmissions as unplanned. This measure had very good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and good to excellent NPV. In terms of identifying unplanned readmissions, the time flag measure had excellent sensitivity and very good specificity, which corresponded to very good to excellent PPV and good to very good NPV (Table: Section B).

PACR

The PACR algorithm identified 75% (599/796) of readmissions as unplanned. The PACR has good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PACR had fair sensitivity but poor specificity, which corresponded to fair PPV and poor NPV (Table: Section C).

PPR

The PPR algorithm identified 53% (257/487) of admissions as potentially preventable. The PPR algorithm had poor sensitivity and specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PPR algorithm had poor sensitivity and fair specificity in identifying unplanned readmissions, which corresponded to fair to good PPV and very poor to poor NPV (Table: Section D).

Evaluation of Excluded Readmission Events

Because both the PACR and PPR had large numbers of algorithm exclusions, we describe the preventability and unplanned assessment of the excluded readmission events. Both algorithms excluded preventable events. Of the 321 readmissions excluded by the PACR algorithm, 13.4% were classified as preventable by chart review. Likewise, 14.9% of 638 readmissions excluded by PPR were classified as preventable by chart review.

The ability to accurately capture preventable pediatric readmission is a goal for hospital quality experts and health policymakers alike. Of the four commonly used readmission measures to assess readmission, only PPR is designed to focus on preventability. Unfortunately, none of these four measures is adequately sensitive or specific to identify preventable readmissions; all measures had very poor PPV for preventability. Of the four measures, the time flag measure had the best sensitivity, specificity, PPV, and NPV for identifying unplanned readmissions.

The overall percentage of unplanned readmissions identified by both the time flag and by PACR measures match the overall percentage of unplanned readmissions identified in chart review: The time flag measure identified 80% of admissions as unplanned versus 79% identified by chart review (Appendix Figure: Population A/B); PACR classified 75% as unplanned versus 81% identified by chart review for PACR-eligible readmissions (Appendix Figure: Population C). In contrast, the PPR algorithm classified many more readmissions as potentially preventable (53%) than were identified by chart review at only 16% (Appendix Figure: Population D). The PACR and PPR algorithms also exclude a significant number of readmissions that are unplanned and a smaller, but not trivial, number of readmissions that are preventable; these exclusions limit their accuracy.

The ability to apply these four measures in real time during a hospitalization varies by metric. Two of the measures, the all-cause and time flag, can be applied during a readmission event, which is appealing for quality improvement initiatives. These measures allow for notification of providers that a current hospitalization is a readmission event, which allows providers the opportunity to learn from these events as they occur (Appendix Table 1). While “unplanned” is not the same as “potentially preventable,” almost all potentially preventable readmissions are unplanned; therefore, accurately identifying unplanned readmissions is more beneficial than all-cause. Additionally, a low all-cause readmission rate can be indicative of poor access to scheduled procedures. Nevertheless, all-cause readmission is sometimes used to measure quality.^1,8 While the time flag measure may be more useful for quality improvement initiatives and hospital providers, it relies on hospital registration time, which is not widely available in administrative data sources and, therefore, has limited usefulness to policymakers.

Both PACR and PPR require administrative claims analysis, which is appealing from a policy standpoint. However, the reliance on claims data means the inclusion/exclusion of events can occur only retrospectively, which limits the usefulness of these measures in learning and intervening in real time. When the two measures are compared, PACR offers better sensitivity and PPR offers better specificity with regard to identifying unplanned readmission. The PPR software overcalls preventable readmissions, identifying more readmissions as preventable than there actually are. Nevertheless, Medicaid in several states uses PPR for payment incentive.^1,15-17 Given the poor performance of PPR in assessing both preventable and unplanned pediatric readmission, the use of this measure as a quality metric should be limited.

This study should be considered in the context of several limitations. Because the assessment of preventability was determined as part of a learning quality improvement collaborative and not as a planned research endeavor, not all readmission reviews were completed nor were other existent tools¹⁸ that allow for preventability assessment via more structured medical record review used. Second, we reviewed cases only from certain clinical services, which would limit generalizability of these findings to all pediatric admissions. However, given the low sensitivity and specificity of some of the metrics, we would not anticipate that the addition of other types of admissions would improve the sensitivity and specificity enough to ensure reliability. Third, while we relied on an established method to determine preventability, prior work has demonstrated that additional information gathered from families may change preventability.¹⁹ Finally, due to the exclusions required by the PPR and PACR algorithms, not all readmission events were reviewed. However, these exclusions reflect the actual specifications of use for both measures.

The PPR software has poor fidelity in identifying preventable and unplanned pediatric readmission; this finding has broad policy implications given how widely it is used by state Medicaid offices to assess financial penalties. Among the four pediatric readmission measures used, the time flag metric best identifies unplanned readmissions.

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

Dr Auger’s research is supported by a grant from the Agency for Healthcare Research and Quality (1K08HS204735-01A1). The project described was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health, under Award Number 5UL1TR001425-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Readmission rates are frequently used as a hospital quality metric, with use including payment incentive at the hospital level,¹ specific condition quality measurement,² balancing measures for quality improvement projects,^3-5 transition success,^6,7 and use in public hospital rankings.⁸ Currently, four methods are commonly used to evaluate pediatric readmissions, each with strengths and limitations, including the following (Appendix Table 1):

1. All-cause readmissions: A measure of any readmission within a given time period regardless of the reason for readmission.⁹

2. Unplanned readmission/time flag: A measure intended to identify unplanned readmissions. This measure relies on time designations within the electronic health record. The time between hospital registration and admission is calculated, and if the readmission is registered more than 24 hours prior to admission, the readmission is considered planned.¹⁰ Hereafter, this measure will be referred to as the time flag measure.

3. Pediatric all-condition readmission (PACR): A measure intended to identify unplanned readmission through the exclusion of certain procedures and diagnoses.¹¹

4. Potentially preventable readmission (PPR): A method to identify preventable readmissions based on a proprietary algorithm developed by 3M Health Information Systems.^12,13

While all four of these measures are used to assess quality, there is little known about these measures’ ability to exclude planned readmissions and identify only preventable pediatric readmission, which conceptually is most relevant to the quality of care. However, many of these measures were not intended to capture preventability, but instead capture the related issue of whether the readmission was planned. Therefore, we sought to evaluate the four readmission measures as they relate to both preventability and unplanned status as determined through medical record review with multidisciplinary care provider input.

As part of a hospital-wide readmission reduction quality improvement collaborative at a free-standing tertiary care children’s hospital, clinicians from hospital medicine, cardiology, neonatology, and neurology teams reviewed 30-day readmissions using a standardized abstraction tool. All readmission events (observation or inpatient encounter) after any discharge (observation or inpatient encounter) from eligible units were reviewed; therefore, each hospitalization was a potential index hospitalization. We classified the preventability of each readmission with use of a previously described Likert scale with high interrater reliability.¹⁴ For these analyses, readmissions were considered preventable if the reviewing team rated them as either “more likely preventable” or “preventable in most circumstances.” Each readmission was also evaluated as planned or unplanned. Methods for readmission review and classification are in the Appendix.

We included all readmissions between July 2014 and June 2016. We compared the medical record review classifications with the assessments from each of the four measures of pediatric readmission. We calculated sensitivity and specificity for both outcomes (planned/unplanned and preventable/not preventable) for all four measures. For standardization of discussion, we categorized description of measure performance as “very poor” as less than 50%, “poor” between 50%-75%, “fair” as 75%-85%, “good” as 85%-90%, “very good” as 90%-95% and excellent as greater than 95%. We also calculated positive and negative predictive value (PPV and NPV) over plausible ranges of prevalence using the sensitivity and specificity of each comparison (Appendix).

Of note, certain exclusions are outlined by the PACR and PPR algorithms. The PACR evaluates only readmission events that occur in children younger than 18 years. The PPR algorithm does not assign preventability if either the index or readmission event is classified as an observation stay or if it is part of a larger chain of readmissions.

Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68.5%). The median time to readmission was 7 days (interquartile range [IQR], 4-18). Most children were non-Hispanic White (71%) or Black (20%). The median age at hospitalization was 2.3 years (IQR 0.4-12.1). Most children had Medicaid (56%) or private (41%) insurance. Most of the reviews were performed in cardiology (43%) and hospital medicine (37%) with patients in neurology (13%) and neonatology (7%) constituting the remaining reviews. Uncontrolled advancement of chronic disease was the most common readmission category on medical record review (25.1%), followed by unrelated readmission (20.7%), scheduled readmission (20.4%), and progression of acute disease (16.6%) (Appendix Table 2).

Assessment of Preventable and Unplanned Readmissions

On multidisciplinary medical record review, most readmissions were classified as not preventable (84.5%). Specifically, 64% were not preventable and unplanned; 20% were deemed not preventable and planned. Only 15% were classified as unplanned and preventable and 1% as planned and preventable (Appendix Figure: Population A/B).

Matching Chart Review to the Four Algorithms

All 1,125 readmissions were assessed by the all-cause and time flag readmission measures (Appendix Figure: Population A/B). After applying algorithm exclusions (details in Appendix), only 804 of the 1,125 (71.5%) reviewed readmissions matched for PACR readmission comparison (Appendix Figure: Population C); 487 of the 1,125 (43.3%) of the reviewed readmissions matched for PPR comparison (Appendix Figure: Population D).

All-Cause

Because all-cause determines only if a readmission occurs, the measure is by definition 100% sensitive and 0% specific in both assessment of preventability and unplanned readmission (Table: Section A).

Time Flag

The time flag measure identified 80% (866/1,112) of the readmissions as unplanned. This measure had very good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and good to excellent NPV. In terms of identifying unplanned readmissions, the time flag measure had excellent sensitivity and very good specificity, which corresponded to very good to excellent PPV and good to very good NPV (Table: Section B).

PACR

The PACR algorithm identified 75% (599/796) of readmissions as unplanned. The PACR has good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PACR had fair sensitivity but poor specificity, which corresponded to fair PPV and poor NPV (Table: Section C).

PPR

The PPR algorithm identified 53% (257/487) of admissions as potentially preventable. The PPR algorithm had poor sensitivity and specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PPR algorithm had poor sensitivity and fair specificity in identifying unplanned readmissions, which corresponded to fair to good PPV and very poor to poor NPV (Table: Section D).

Evaluation of Excluded Readmission Events

Because both the PACR and PPR had large numbers of algorithm exclusions, we describe the preventability and unplanned assessment of the excluded readmission events. Both algorithms excluded preventable events. Of the 321 readmissions excluded by the PACR algorithm, 13.4% were classified as preventable by chart review. Likewise, 14.9% of 638 readmissions excluded by PPR were classified as preventable by chart review.

The ability to accurately capture preventable pediatric readmission is a goal for hospital quality experts and health policymakers alike. Of the four commonly used readmission measures to assess readmission, only PPR is designed to focus on preventability. Unfortunately, none of these four measures is adequately sensitive or specific to identify preventable readmissions; all measures had very poor PPV for preventability. Of the four measures, the time flag measure had the best sensitivity, specificity, PPV, and NPV for identifying unplanned readmissions.

The overall percentage of unplanned readmissions identified by both the time flag and by PACR measures match the overall percentage of unplanned readmissions identified in chart review: The time flag measure identified 80% of admissions as unplanned versus 79% identified by chart review (Appendix Figure: Population A/B); PACR classified 75% as unplanned versus 81% identified by chart review for PACR-eligible readmissions (Appendix Figure: Population C). In contrast, the PPR algorithm classified many more readmissions as potentially preventable (53%) than were identified by chart review at only 16% (Appendix Figure: Population D). The PACR and PPR algorithms also exclude a significant number of readmissions that are unplanned and a smaller, but not trivial, number of readmissions that are preventable; these exclusions limit their accuracy.

The ability to apply these four measures in real time during a hospitalization varies by metric. Two of the measures, the all-cause and time flag, can be applied during a readmission event, which is appealing for quality improvement initiatives. These measures allow for notification of providers that a current hospitalization is a readmission event, which allows providers the opportunity to learn from these events as they occur (Appendix Table 1). While “unplanned” is not the same as “potentially preventable,” almost all potentially preventable readmissions are unplanned; therefore, accurately identifying unplanned readmissions is more beneficial than all-cause. Additionally, a low all-cause readmission rate can be indicative of poor access to scheduled procedures. Nevertheless, all-cause readmission is sometimes used to measure quality.^1,8 While the time flag measure may be more useful for quality improvement initiatives and hospital providers, it relies on hospital registration time, which is not widely available in administrative data sources and, therefore, has limited usefulness to policymakers.

Both PACR and PPR require administrative claims analysis, which is appealing from a policy standpoint. However, the reliance on claims data means the inclusion/exclusion of events can occur only retrospectively, which limits the usefulness of these measures in learning and intervening in real time. When the two measures are compared, PACR offers better sensitivity and PPR offers better specificity with regard to identifying unplanned readmission. The PPR software overcalls preventable readmissions, identifying more readmissions as preventable than there actually are. Nevertheless, Medicaid in several states uses PPR for payment incentive.^1,15-17 Given the poor performance of PPR in assessing both preventable and unplanned pediatric readmission, the use of this measure as a quality metric should be limited.

This study should be considered in the context of several limitations. Because the assessment of preventability was determined as part of a learning quality improvement collaborative and not as a planned research endeavor, not all readmission reviews were completed nor were other existent tools¹⁸ that allow for preventability assessment via more structured medical record review used. Second, we reviewed cases only from certain clinical services, which would limit generalizability of these findings to all pediatric admissions. However, given the low sensitivity and specificity of some of the metrics, we would not anticipate that the addition of other types of admissions would improve the sensitivity and specificity enough to ensure reliability. Third, while we relied on an established method to determine preventability, prior work has demonstrated that additional information gathered from families may change preventability.¹⁹ Finally, due to the exclusions required by the PPR and PACR algorithms, not all readmission events were reviewed. However, these exclusions reflect the actual specifications of use for both measures.

The PPR software has poor fidelity in identifying preventable and unplanned pediatric readmission; this finding has broad policy implications given how widely it is used by state Medicaid offices to assess financial penalties. Among the four pediatric readmission measures used, the time flag metric best identifies unplanned readmissions.

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

Dr Auger’s research is supported by a grant from the Agency for Healthcare Research and Quality (1K08HS204735-01A1). The project described was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health, under Award Number 5UL1TR001425-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Readmission rates are frequently used as a hospital quality metric, with use including payment incentive at the hospital level,¹ specific condition quality measurement,² balancing measures for quality improvement projects,^3-5 transition success,^6,7 and use in public hospital rankings.⁸ Currently, four methods are commonly used to evaluate pediatric readmissions, each with strengths and limitations, including the following (Appendix Table 1):

1. All-cause readmissions: A measure of any readmission within a given time period regardless of the reason for readmission.⁹

2. Unplanned readmission/time flag: A measure intended to identify unplanned readmissions. This measure relies on time designations within the electronic health record. The time between hospital registration and admission is calculated, and if the readmission is registered more than 24 hours prior to admission, the readmission is considered planned.¹⁰ Hereafter, this measure will be referred to as the time flag measure.

3. Pediatric all-condition readmission (PACR): A measure intended to identify unplanned readmission through the exclusion of certain procedures and diagnoses.¹¹

4. Potentially preventable readmission (PPR): A method to identify preventable readmissions based on a proprietary algorithm developed by 3M Health Information Systems.^12,13

While all four of these measures are used to assess quality, there is little known about these measures’ ability to exclude planned readmissions and identify only preventable pediatric readmission, which conceptually is most relevant to the quality of care. However, many of these measures were not intended to capture preventability, but instead capture the related issue of whether the readmission was planned. Therefore, we sought to evaluate the four readmission measures as they relate to both preventability and unplanned status as determined through medical record review with multidisciplinary care provider input.

As part of a hospital-wide readmission reduction quality improvement collaborative at a free-standing tertiary care children’s hospital, clinicians from hospital medicine, cardiology, neonatology, and neurology teams reviewed 30-day readmissions using a standardized abstraction tool. All readmission events (observation or inpatient encounter) after any discharge (observation or inpatient encounter) from eligible units were reviewed; therefore, each hospitalization was a potential index hospitalization. We classified the preventability of each readmission with use of a previously described Likert scale with high interrater reliability.¹⁴ For these analyses, readmissions were considered preventable if the reviewing team rated them as either “more likely preventable” or “preventable in most circumstances.” Each readmission was also evaluated as planned or unplanned. Methods for readmission review and classification are in the Appendix.

We included all readmissions between July 2014 and June 2016. We compared the medical record review classifications with the assessments from each of the four measures of pediatric readmission. We calculated sensitivity and specificity for both outcomes (planned/unplanned and preventable/not preventable) for all four measures. For standardization of discussion, we categorized description of measure performance as “very poor” as less than 50%, “poor” between 50%-75%, “fair” as 75%-85%, “good” as 85%-90%, “very good” as 90%-95% and excellent as greater than 95%. We also calculated positive and negative predictive value (PPV and NPV) over plausible ranges of prevalence using the sensitivity and specificity of each comparison (Appendix).

Of note, certain exclusions are outlined by the PACR and PPR algorithms. The PACR evaluates only readmission events that occur in children younger than 18 years. The PPR algorithm does not assign preventability if either the index or readmission event is classified as an observation stay or if it is part of a larger chain of readmissions.

Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68.5%). The median time to readmission was 7 days (interquartile range [IQR], 4-18). Most children were non-Hispanic White (71%) or Black (20%). The median age at hospitalization was 2.3 years (IQR 0.4-12.1). Most children had Medicaid (56%) or private (41%) insurance. Most of the reviews were performed in cardiology (43%) and hospital medicine (37%) with patients in neurology (13%) and neonatology (7%) constituting the remaining reviews. Uncontrolled advancement of chronic disease was the most common readmission category on medical record review (25.1%), followed by unrelated readmission (20.7%), scheduled readmission (20.4%), and progression of acute disease (16.6%) (Appendix Table 2).

Assessment of Preventable and Unplanned Readmissions

On multidisciplinary medical record review, most readmissions were classified as not preventable (84.5%). Specifically, 64% were not preventable and unplanned; 20% were deemed not preventable and planned. Only 15% were classified as unplanned and preventable and 1% as planned and preventable (Appendix Figure: Population A/B).

Matching Chart Review to the Four Algorithms

All 1,125 readmissions were assessed by the all-cause and time flag readmission measures (Appendix Figure: Population A/B). After applying algorithm exclusions (details in Appendix), only 804 of the 1,125 (71.5%) reviewed readmissions matched for PACR readmission comparison (Appendix Figure: Population C); 487 of the 1,125 (43.3%) of the reviewed readmissions matched for PPR comparison (Appendix Figure: Population D).

All-Cause

Because all-cause determines only if a readmission occurs, the measure is by definition 100% sensitive and 0% specific in both assessment of preventability and unplanned readmission (Table: Section A).

Time Flag

The time flag measure identified 80% (866/1,112) of the readmissions as unplanned. This measure had very good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and good to excellent NPV. In terms of identifying unplanned readmissions, the time flag measure had excellent sensitivity and very good specificity, which corresponded to very good to excellent PPV and good to very good NPV (Table: Section B).

PACR

The PACR algorithm identified 75% (599/796) of readmissions as unplanned. The PACR has good sensitivity but very poor specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PACR had fair sensitivity but poor specificity, which corresponded to fair PPV and poor NPV (Table: Section C).

PPR

The PPR algorithm identified 53% (257/487) of admissions as potentially preventable. The PPR algorithm had poor sensitivity and specificity in identifying preventable readmissions, which corresponded to very poor PPV and fair to very good NPV. In terms of identifying unplanned readmissions, the PPR algorithm had poor sensitivity and fair specificity in identifying unplanned readmissions, which corresponded to fair to good PPV and very poor to poor NPV (Table: Section D).

Evaluation of Excluded Readmission Events

Because both the PACR and PPR had large numbers of algorithm exclusions, we describe the preventability and unplanned assessment of the excluded readmission events. Both algorithms excluded preventable events. Of the 321 readmissions excluded by the PACR algorithm, 13.4% were classified as preventable by chart review. Likewise, 14.9% of 638 readmissions excluded by PPR were classified as preventable by chart review.

The ability to accurately capture preventable pediatric readmission is a goal for hospital quality experts and health policymakers alike. Of the four commonly used readmission measures to assess readmission, only PPR is designed to focus on preventability. Unfortunately, none of these four measures is adequately sensitive or specific to identify preventable readmissions; all measures had very poor PPV for preventability. Of the four measures, the time flag measure had the best sensitivity, specificity, PPV, and NPV for identifying unplanned readmissions.

The overall percentage of unplanned readmissions identified by both the time flag and by PACR measures match the overall percentage of unplanned readmissions identified in chart review: The time flag measure identified 80% of admissions as unplanned versus 79% identified by chart review (Appendix Figure: Population A/B); PACR classified 75% as unplanned versus 81% identified by chart review for PACR-eligible readmissions (Appendix Figure: Population C). In contrast, the PPR algorithm classified many more readmissions as potentially preventable (53%) than were identified by chart review at only 16% (Appendix Figure: Population D). The PACR and PPR algorithms also exclude a significant number of readmissions that are unplanned and a smaller, but not trivial, number of readmissions that are preventable; these exclusions limit their accuracy.

The ability to apply these four measures in real time during a hospitalization varies by metric. Two of the measures, the all-cause and time flag, can be applied during a readmission event, which is appealing for quality improvement initiatives. These measures allow for notification of providers that a current hospitalization is a readmission event, which allows providers the opportunity to learn from these events as they occur (Appendix Table 1). While “unplanned” is not the same as “potentially preventable,” almost all potentially preventable readmissions are unplanned; therefore, accurately identifying unplanned readmissions is more beneficial than all-cause. Additionally, a low all-cause readmission rate can be indicative of poor access to scheduled procedures. Nevertheless, all-cause readmission is sometimes used to measure quality.^1,8 While the time flag measure may be more useful for quality improvement initiatives and hospital providers, it relies on hospital registration time, which is not widely available in administrative data sources and, therefore, has limited usefulness to policymakers.

Both PACR and PPR require administrative claims analysis, which is appealing from a policy standpoint. However, the reliance on claims data means the inclusion/exclusion of events can occur only retrospectively, which limits the usefulness of these measures in learning and intervening in real time. When the two measures are compared, PACR offers better sensitivity and PPR offers better specificity with regard to identifying unplanned readmission. The PPR software overcalls preventable readmissions, identifying more readmissions as preventable than there actually are. Nevertheless, Medicaid in several states uses PPR for payment incentive.^1,15-17 Given the poor performance of PPR in assessing both preventable and unplanned pediatric readmission, the use of this measure as a quality metric should be limited.

This study should be considered in the context of several limitations. Because the assessment of preventability was determined as part of a learning quality improvement collaborative and not as a planned research endeavor, not all readmission reviews were completed nor were other existent tools¹⁸ that allow for preventability assessment via more structured medical record review used. Second, we reviewed cases only from certain clinical services, which would limit generalizability of these findings to all pediatric admissions. However, given the low sensitivity and specificity of some of the metrics, we would not anticipate that the addition of other types of admissions would improve the sensitivity and specificity enough to ensure reliability. Third, while we relied on an established method to determine preventability, prior work has demonstrated that additional information gathered from families may change preventability.¹⁹ Finally, due to the exclusions required by the PPR and PACR algorithms, not all readmission events were reviewed. However, these exclusions reflect the actual specifications of use for both measures.

The PPR software has poor fidelity in identifying preventable and unplanned pediatric readmission; this finding has broad policy implications given how widely it is used by state Medicaid offices to assess financial penalties. Among the four pediatric readmission measures used, the time flag metric best identifies unplanned readmissions.

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

Dr Auger’s research is supported by a grant from the Agency for Healthcare Research and Quality (1K08HS204735-01A1). The project described was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health, under Award Number 5UL1TR001425-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Discharges against medical advice (DAMAs), in which a patient leaves the hospital prior to a physician-recommended endpoint, represent approximately 1% to 2% of inpatient discharges in the United States.¹ When compared with routine discharges, a DAMA is associated with adverse clinical consequences, including an increased risk of all-cause mortality.^2,3 Additionally, due to incomplete care, a DAMA may result in increased healthcare resource utilization (HcRU), including the use of inpatient, emergency department (ED), and outpatient services in the postdischarge period. Quantifying these relationships can provide important information regarding an individual’s healthcare-seeking behavior following a DAMA.

Prior literature has focused on the association between a DAMA and the risk of inpatient readmission. Relative to routine discharges, a DAMA is associated with a 1.5 to 2 times increased risk of a 30-day readmission.^3-9 However, these estimates are based on mixed-payer populations primarily composed (65%-80%) of individuals with public (Medicaid, Medicare) or no insurance. Further, they do not differentiate this association by payer type. It is unclear if prior results apply to commercially insured adults. These individuals represent a small but nonnegligible proportion (19%) of all DAMAs in the United States.¹⁰ Quantifying relationships among commercially insured adults can help advance our understanding of readmission patterns in the DAMA population.

There is limited evidence regarding the relationship between a DAMA and outpatient HcRU in the postdischarge period. Use of ED services after a DAMA has been explored only in specific disease populations such as asthma.⁴ Additionally, prior studies have reported a reduced frequency in the receipt of medication prescriptions and outpatient follow-up plans among individuals with a DAMA at the time of discharge.^11,12 Whether these practices translate to altered patterns of postdischarge prescription drug fills or use of outpatient services is not known.

To address these substantive gaps in the literature, the present study evaluates the association between a DAMA and all-cause HcRU in the postdischarge period among commercially insured adults. We examined HcRU across all points of service including inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters, and prescription drug fills. These results can serve as a benchmark for comparison to future studies on DAMAs among publicly insured or uninsured individuals. Furthermore, such knowledge can help providers, payers, and policy planners make evidence-based decisions regarding postdischarge healthcare delivery.

Data Source

This retrospective study used a 10% random sample of enrollees in the IQVIA PharMetrics^® Plus database (purchased by University of Maryland, Baltimore, under license from IQVIA). The database is composed of fully adjudicated claims and enrollment information from over 70 contributing US health plans and self-insured employer groups for over 140 million unique enrollees from 2006 onward. The enrollee population is generally representative of the commercially insured population that is younger than 65 years of age (with a subset of commercial Medicare and Medicaid) with respect to age and gender.

The database allows longitudinal follow-up for individuals using three files: medical claims, pharmacy claims, and insurance eligibility. The average length of enrollment is 39 months. The claims data represent payments to providers for services rendered to individuals covered by health plans. The medical claims file contains information on diagnostic and therapeutic services rendered in the inpatient and outpatient settings. The pharmacy claims file captures data on prescription drugs dispensed in retail and mail-order settings. The eligibility file contains demographic and insurance eligibility information for individuals.

Study Population

We identified all individuals aged 18 to 64 years with an inpatient admission record between January 1, 2007, and December 31, 2015. All individuals with continuous medical and prescription drug coverage from 6 months prior to the hospital admission date (baseline period) through 30 days following the discharge date (follow-up period) were included. Inpatient admissions with a missing discharge disposition or those that resulted in in-hospital death, discharge to a short-term hospital, skilled nursing facility, intermediate care facility, or any other type of facility were not considered for analysis. Only the first eligible inpatient admission was considered for analysis.

Main Predictor Variable

Individuals with a DAMA were analyzed as the case group. A DAMA was identified using the “Patient Status Code” variable, which represents the discharge disposition of each individual. Individuals who were discharged to home/self-care or discharged to a home health organization formed the control group (hereafter referred to as routine discharge).

Demographic, Clinical, and Hospitalization Characteristics

An individual’s age, sex, and region of residence were determined at the date of hospital admission. The Elixhauser algorithm was used to categorize comorbid conditions (as scores of 0, 1-2, ≥3 depending on number of comorbidities) based on International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis codes during the baseline period.^13,14 The following characteristics of each individual’s eligible inpatient admission were captured: year, timing (weekday or weekend), length of stay (LOS, measured in days), and receipt of a surgical procedure.

Outcomes

All-cause HcRU was identified during the 30-day postdischarge period. Specifically, we identified inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters (for example, pathology, radiology, outpatient surgical services), and prescription drug fills. Binary variables (yes or no) were created for inpatient readmissions and ED visits while the remaining HcRU categories (ie, physician office visits, nonphysician outpatient encounters, and prescription drug fills) were analyzed as count variables. In the sensitivity analyses, we provide results for HcRU outcomes among a subgroup of individuals who had at least 90 days of continuous medical and prescription drug benefits following the hospital discharge.

Statistical Analysis

Descriptive Analysis

Measures of interest were reported using summary statistics depending on the nature of the variable. Continuous variables were described using t tests, and categorical variables were described using chi-square tests.

Propensity Score Matching

Cases and controls were matched using a 1:1 greedy matching algorithm based on propensity scores.¹⁵ We developed propensity scores based on confounders that we hypothesized would be associated with a DAMA and postdischarge HcRU. The propensity score model included the following variables: age, sex, region of residence, Elixhauser comorbidity index score, year of admission, timing of admission, LOS, and presence of any surgical procedure during the inpatient admission. The best match between cases and controls was determined based on the absolute difference in their propensity scores, which allowed for a maximal caliper width of 0.2 of the standard deviation of the logit of the propensity score.¹⁶ A standardized difference value of less than 0.1 was used to assess balance in baseline patient and hospital characteristics between cases and controls consistent with prior literature.^17,18 Proportions and balance, as measured by standardized differences between baseline covariates across cases and controls in the matched sample, are displayed in tabular format (Appendix Table 1).

Healthcare Resource Utilization

We estimated the adjusted odds ratio (AOR) using a logistic regression model. The AOR quantified the association between a DAMA and the prevalence of all-cause inpatient readmissions and ED visits during the 30-day postdischarge period. We estimated incident rate ratios (IRR) for count outcomes. Given the large number of individuals with no physician office visits, nonphysician outpatient encounters, or prescription drug fills, we estimated model parameters for IRRs using a finite mixture negative binomial hurdle model.¹⁹ We considered the data to represent a mixture of a constant distribution (which always generates zero counts) and a zero-truncated distribution (which always generates nonzero counts). The finite mixture count models include two outcomes: the mixing probabilities and the count distribution. The mixing probabilities quantify the probability that an observation for the HcRU category will be drawn from either the constant distribution (with mass at zero) or the count distribution. Conditional on having positive values, a zero-truncated generalized linear model (GLM) governs the count variable. Compared with other GLM specifications (eg, Poisson, negative binomial, zero-inflated), the negative binomial hurdle model presented the best-fitting model across several information criteria statistics (Appendix Figures 1-3 and Appendix Tables 2-4).

The GLM results provided IRR for the counts of HcRU. Ratios were interpreted as evidence of increased HcRU (IRR ≥ 1.0) or decreased HcRU (IRR < 1.0) among individuals with a DAMA compared with those discharged routinely. For all HcRU analyses, we reported results for the matched sample. All analyses were conducted using SAS version 9.4 (SAS Institute), and statistical significance was determined at α= .05. The study received the University of Maryland, Baltimore, Institutional Review Board approval (HP-00081497).

The unmatched sample included 457,530 individuals, of whom 0.5% had a DAMA. A consort diagram illustrating cohort inclusion and exclusion criteria is presented in Appendix Figure 4. Demographic, clinical, and inpatient admission characteristics of the unmatched sample and for subgroups defined by discharge status are displayed in Table 1. In the unmatched sample, the median age at admission was higher for individuals with a DAMA than it was for those discharged routinely (43 vs 42 years, respectively), and the proportion of males was higher among those with a DAMA (58.4% vs 33.1%). There were statistically significant differences based on the geographic region of residence and the comorbidity burden across both groups. The median LOS was shorter (1 day vs 2 days), the proportion of weekend admissions was higher (22.2% vs 16.3%), and the proportion of inpatient surgical procedures was lower (12.9% vs 59.2%) among those with a DAMA compared with that among those with routine discharges. The propensity score-matched sample included 2,245 cases and 2,245 controls (Appendix Table 1). Standardized differences for all baseline factors were less than 0.1, indicating that cases and controls were matched on the included baseline factors.

Summary Statistics: Proportions and Counts

Across the DAMA and routine discharge groups, the proportion of individuals with a 30-day inpatient readmission was similar (19.5% vs 18.7%; P = .47), whereas the proportion with an ED visit was higher (18.6% vs 9.1%; P < .01). There were no differences in the median number of inpatient readmissions (median, 0) and ED visits (median, 0) across both groups. Individuals with a DAMA and those discharged routinely displayed similar median counts of 30-day physician office (median, 1) and nonphysician outpatient encounters (median, 1) (Table 2). Individuals with a DAMA displayed a lower median number of prescription drug fills (median, 2 vs 3) than that among those with a routine discharge (Table 2).

Main Analysis: Thirty-Day Healthcare Resource Utilization

The associations between a DAMA and 30-day inpatient readmissions and ED visits based on the matched sample are presented in Table 3. Individuals with a DAMA had increased odds for an ED visit (AOR, 2.28; 95% CI, 1.90-2.72) but no significant difference in the odds of a 30-day inpatient readmission (AOR, 1.06; 95% CI, 0.91-1.23) compared with those discharged routinely.

The association between a DAMA and count HcRU outcomes is presented in Table 4. Compared with those discharged routinely, individuals with a DAMA displayed no significant difference in rates for physician office visits (IRR, 1.01; 95% CI, 0.91-1.11), nonphysician outpatient encounters (IRR, 0.89; 95% CI, 0.78-1.00), and prescription drug fills (IRR, 1.03; 95% CI, 0.97-1.09) during the 30-day postdischarge period.

Sensitivity Analysis: Ninety-Day Healthcare Resource Utilization

Relative to those discharged routinely, individuals with a DAMA had statistically significant increased odds of 90-day inpatient readmissions (AOR, 1.18; 95% CI, 1.02-1.36), odds of ED visits (AOR, 2.16; 95% CI, 1.85-2.51), and rates of prescription drug fills (IRR, 1.32; 95% CI, 1.29-1.35). No statistically significant differences were observed in the rates of physician office visits and nonphysician outpatient encounters across both groups.

In this commercially insured sample of working age individuals, we identified an association between a DAMA and the likelihood and intensity of postdischarge HcRU. The direction of the association varied across categories of HcRU and the duration of follow-up. A DAMA was associated with increased odds of 30-day ED visits but not 30-day readmissions compared with routine discharges. No significant differences were observed in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups. To our knowledge, this is the first study on DAMAs that examines postdischarge HcRU outside the inpatient setting.

The 0.5% prevalence of DAMAs in our study was lower than the approximate 1% to 2% value that is typically reported in the literature. Prior studies have typically reported results based on mixed-payer populations.^3-10 These mixed-payer populations include publicly insured (Medicare or Medicaid) or uninsured stays, which account for a disproportionate share of all DAMAs. In contrast, commercially insured stays account for the lowest proportion of all DAMAs.¹⁰ Similar to prior literature,⁵ the DAMA group in our study was younger, had a higher proportion of males, had a higher comorbidity burden, and had a shorter LOS than the routinely discharged group.

We observed a greater likelihood of ED utilization after a DAMA. Similar findings have been reported, which may indicate that patients with a DAMA receive inadequate treatment at the time of discharge and may require further acute treatment. For example, a prior study reported that, after a DAMA, individuals with asthma were four times more likely to have an ED visit within 14 days compared with those discharged routinely.⁴

Contrary to prior findings,^3-9 we found no significant difference in the odds of a 30-day inpatient readmission across the DAMA and routine discharge groups, which may be attributable to differences in the populations studied. Those previous studies used mixed payer populations and did not differentiate results by payer type. The mixed payer populations in these studies were older (mean ages, 55 years and above) and had an increased comorbidity burden compared with our commercially insured population. Furthermore, some of these studies were either limited to single sites,⁸ single state hospital systems,^3,4,9 or focused on specific medical populations.^3,4,6-9 Our national sample of commercially insured adults is considerably younger, with a mean age of 43 years. Thirty days may be too brief to observe enough inpatient readmissions for the purpose of comparative analyses. This is suggested by our results, which indicated that there is an association between DAMA and 90-day inpatient readmission. Additionally, nonsignificant findings for 30-day inpatient readmissions may also be due to the small sample size of the DAMA group in our study, which may have limited robust statistical inference. Future studies in a larger population of commercially insured individuals with a DAMA are required to confirm these findings.

Nonsignificant differences in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups may explain the null association with 30-day inpatient readmissions. Prior literature on specific medical populations or individuals with general hospital admissions report that early outpatient follow-up can help prevent 30-day readmissions.^20-25 In our sample, we observed similar rates of outpatient follow-up across the DAMA and routinely discharged groups. Prior studies based on single hospital sites have reported that, at the time of discharge, a lower proportion of individuals with a DAMA received medication prescriptions and outpatient follow-up plans compared with those discharged routinely.^11,12 In contrast, we evaluated prescription drug fills and outpatient visits during the postdischarge period, which may explain the difference in findings.

The present study has several strengths. To the best of our knowledge, our study represents the first and largest retrospective analysis of DAMAs in a national sample of commercially insured adults. In addition to a large generalizable sample, we examine HcRU after a DAMA across major points of service over a longitudinal postdischarge period. Our results provide a comprehensive understanding of utilization outcomes in this population including those outside the inpatient setting, which has been the focus of prior literature. These findings can help guide the implementation of appropriate patient- and system-level interventions to optimize DAMA prevention and mitigate the associated utilization burden on the healthcare system in the postdischarge period.^26,27

Our findings should be interpreted with certain limitations in mind. First, this study used data based on a commercially insured sample of patients and may not be generalizable to publicly insured or uninsured samples. Second, like prior DAMA studies that used the Nationwide Readmissions Database instead,^5-7 our study was unable to account for individual-level factors such as race, marital status, family social support, income, health literacy, and activation in self-care. Further, given the limitations of our data, we were unable to control for hospital characteristics such as bed size, urban-rural designation, teaching status, and control (eg, private or government ownership). Despite the use of propensity score methods to balance both comparison groups on observable sources of confounding, we cannot rule out the possibility of residual confounding. Lastly, due to a lack of data on postdischarge mortality outcomes, we could not control for competing risk of death in our analysis. However, in a population with an average age of 43 years, we did not expect high or differential 30- or 90-day postdischarge mortality rates across both groups.

Our findings suggest several important directions for future research. First, it will be useful to examine these associations among publicly insured and uninsured samples in which a DAMA is more prevalent and in which the associations with HcRU may be more pronounced than they are in the commercially insured population. Secondly, future research should identify subgroups of DAMA patients with an increased propensity for postdischarge HcRU. This can help in the design of individualized outpatient follow-up plans that address patient-specific medical and social needs. Finally, our findings highlight the need for education, practice guidelines, and suitable interventions to help providers in the prevention and management of a DAMA.

Using data from a commercially insured population, we identified associations between a DAMA and postdischarge HcRU. The associations differed by category of HcRU. We identified a positive association with the likelihood of ED utilization but no association with the likelihood of 30-day inpatient readmission or general outpatient utilization. Our results indicate that the examination of inpatient readmissions after a DAMA should not be considered in isolation. The identification of the full range of outpatient and inpatient HcRU after a DAMA in a broad population of patients can improve our understanding of outcomes following a DAMA and support appropriate system-level interventions designed to reduce their prevalence.

The statements, findings, conclusions, views, and opinions contained and expressed in this manuscript are based in part on data obtained under license from IQVIA. Source: IQVIA PharMetrics^® Plus January 2006 – December 2015, IQVIA. All Rights Reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IQVIA or any of its affiliated or subsidiary entities.

Dr Onukwugha reports grants from Bayer Healthcare Pharmaceuticals, grants from Pfizer, Inc, and personal fees from Novo Nordisk outside the submitted work. The other authors have nothing to disclose. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the US Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

The authors acknowledge the support of the University of Maryland, Baltimore Institute for Clinical & Translational Research (ICTR) through the ICTR Voucher Program.

Discharges against medical advice (DAMAs), in which a patient leaves the hospital prior to a physician-recommended endpoint, represent approximately 1% to 2% of inpatient discharges in the United States.¹ When compared with routine discharges, a DAMA is associated with adverse clinical consequences, including an increased risk of all-cause mortality.^2,3 Additionally, due to incomplete care, a DAMA may result in increased healthcare resource utilization (HcRU), including the use of inpatient, emergency department (ED), and outpatient services in the postdischarge period. Quantifying these relationships can provide important information regarding an individual’s healthcare-seeking behavior following a DAMA.

Prior literature has focused on the association between a DAMA and the risk of inpatient readmission. Relative to routine discharges, a DAMA is associated with a 1.5 to 2 times increased risk of a 30-day readmission.^3-9 However, these estimates are based on mixed-payer populations primarily composed (65%-80%) of individuals with public (Medicaid, Medicare) or no insurance. Further, they do not differentiate this association by payer type. It is unclear if prior results apply to commercially insured adults. These individuals represent a small but nonnegligible proportion (19%) of all DAMAs in the United States.¹⁰ Quantifying relationships among commercially insured adults can help advance our understanding of readmission patterns in the DAMA population.

There is limited evidence regarding the relationship between a DAMA and outpatient HcRU in the postdischarge period. Use of ED services after a DAMA has been explored only in specific disease populations such as asthma.⁴ Additionally, prior studies have reported a reduced frequency in the receipt of medication prescriptions and outpatient follow-up plans among individuals with a DAMA at the time of discharge.^11,12 Whether these practices translate to altered patterns of postdischarge prescription drug fills or use of outpatient services is not known.

To address these substantive gaps in the literature, the present study evaluates the association between a DAMA and all-cause HcRU in the postdischarge period among commercially insured adults. We examined HcRU across all points of service including inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters, and prescription drug fills. These results can serve as a benchmark for comparison to future studies on DAMAs among publicly insured or uninsured individuals. Furthermore, such knowledge can help providers, payers, and policy planners make evidence-based decisions regarding postdischarge healthcare delivery.

Data Source

This retrospective study used a 10% random sample of enrollees in the IQVIA PharMetrics^® Plus database (purchased by University of Maryland, Baltimore, under license from IQVIA). The database is composed of fully adjudicated claims and enrollment information from over 70 contributing US health plans and self-insured employer groups for over 140 million unique enrollees from 2006 onward. The enrollee population is generally representative of the commercially insured population that is younger than 65 years of age (with a subset of commercial Medicare and Medicaid) with respect to age and gender.

The database allows longitudinal follow-up for individuals using three files: medical claims, pharmacy claims, and insurance eligibility. The average length of enrollment is 39 months. The claims data represent payments to providers for services rendered to individuals covered by health plans. The medical claims file contains information on diagnostic and therapeutic services rendered in the inpatient and outpatient settings. The pharmacy claims file captures data on prescription drugs dispensed in retail and mail-order settings. The eligibility file contains demographic and insurance eligibility information for individuals.

Study Population

We identified all individuals aged 18 to 64 years with an inpatient admission record between January 1, 2007, and December 31, 2015. All individuals with continuous medical and prescription drug coverage from 6 months prior to the hospital admission date (baseline period) through 30 days following the discharge date (follow-up period) were included. Inpatient admissions with a missing discharge disposition or those that resulted in in-hospital death, discharge to a short-term hospital, skilled nursing facility, intermediate care facility, or any other type of facility were not considered for analysis. Only the first eligible inpatient admission was considered for analysis.

Main Predictor Variable

Individuals with a DAMA were analyzed as the case group. A DAMA was identified using the “Patient Status Code” variable, which represents the discharge disposition of each individual. Individuals who were discharged to home/self-care or discharged to a home health organization formed the control group (hereafter referred to as routine discharge).

Demographic, Clinical, and Hospitalization Characteristics

An individual’s age, sex, and region of residence were determined at the date of hospital admission. The Elixhauser algorithm was used to categorize comorbid conditions (as scores of 0, 1-2, ≥3 depending on number of comorbidities) based on International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis codes during the baseline period.^13,14 The following characteristics of each individual’s eligible inpatient admission were captured: year, timing (weekday or weekend), length of stay (LOS, measured in days), and receipt of a surgical procedure.

Outcomes

All-cause HcRU was identified during the 30-day postdischarge period. Specifically, we identified inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters (for example, pathology, radiology, outpatient surgical services), and prescription drug fills. Binary variables (yes or no) were created for inpatient readmissions and ED visits while the remaining HcRU categories (ie, physician office visits, nonphysician outpatient encounters, and prescription drug fills) were analyzed as count variables. In the sensitivity analyses, we provide results for HcRU outcomes among a subgroup of individuals who had at least 90 days of continuous medical and prescription drug benefits following the hospital discharge.

Statistical Analysis

Descriptive Analysis

Measures of interest were reported using summary statistics depending on the nature of the variable. Continuous variables were described using t tests, and categorical variables were described using chi-square tests.

Propensity Score Matching

Cases and controls were matched using a 1:1 greedy matching algorithm based on propensity scores.¹⁵ We developed propensity scores based on confounders that we hypothesized would be associated with a DAMA and postdischarge HcRU. The propensity score model included the following variables: age, sex, region of residence, Elixhauser comorbidity index score, year of admission, timing of admission, LOS, and presence of any surgical procedure during the inpatient admission. The best match between cases and controls was determined based on the absolute difference in their propensity scores, which allowed for a maximal caliper width of 0.2 of the standard deviation of the logit of the propensity score.¹⁶ A standardized difference value of less than 0.1 was used to assess balance in baseline patient and hospital characteristics between cases and controls consistent with prior literature.^17,18 Proportions and balance, as measured by standardized differences between baseline covariates across cases and controls in the matched sample, are displayed in tabular format (Appendix Table 1).

Healthcare Resource Utilization

We estimated the adjusted odds ratio (AOR) using a logistic regression model. The AOR quantified the association between a DAMA and the prevalence of all-cause inpatient readmissions and ED visits during the 30-day postdischarge period. We estimated incident rate ratios (IRR) for count outcomes. Given the large number of individuals with no physician office visits, nonphysician outpatient encounters, or prescription drug fills, we estimated model parameters for IRRs using a finite mixture negative binomial hurdle model.¹⁹ We considered the data to represent a mixture of a constant distribution (which always generates zero counts) and a zero-truncated distribution (which always generates nonzero counts). The finite mixture count models include two outcomes: the mixing probabilities and the count distribution. The mixing probabilities quantify the probability that an observation for the HcRU category will be drawn from either the constant distribution (with mass at zero) or the count distribution. Conditional on having positive values, a zero-truncated generalized linear model (GLM) governs the count variable. Compared with other GLM specifications (eg, Poisson, negative binomial, zero-inflated), the negative binomial hurdle model presented the best-fitting model across several information criteria statistics (Appendix Figures 1-3 and Appendix Tables 2-4).

The GLM results provided IRR for the counts of HcRU. Ratios were interpreted as evidence of increased HcRU (IRR ≥ 1.0) or decreased HcRU (IRR < 1.0) among individuals with a DAMA compared with those discharged routinely. For all HcRU analyses, we reported results for the matched sample. All analyses were conducted using SAS version 9.4 (SAS Institute), and statistical significance was determined at α= .05. The study received the University of Maryland, Baltimore, Institutional Review Board approval (HP-00081497).

The unmatched sample included 457,530 individuals, of whom 0.5% had a DAMA. A consort diagram illustrating cohort inclusion and exclusion criteria is presented in Appendix Figure 4. Demographic, clinical, and inpatient admission characteristics of the unmatched sample and for subgroups defined by discharge status are displayed in Table 1. In the unmatched sample, the median age at admission was higher for individuals with a DAMA than it was for those discharged routinely (43 vs 42 years, respectively), and the proportion of males was higher among those with a DAMA (58.4% vs 33.1%). There were statistically significant differences based on the geographic region of residence and the comorbidity burden across both groups. The median LOS was shorter (1 day vs 2 days), the proportion of weekend admissions was higher (22.2% vs 16.3%), and the proportion of inpatient surgical procedures was lower (12.9% vs 59.2%) among those with a DAMA compared with that among those with routine discharges. The propensity score-matched sample included 2,245 cases and 2,245 controls (Appendix Table 1). Standardized differences for all baseline factors were less than 0.1, indicating that cases and controls were matched on the included baseline factors.

Summary Statistics: Proportions and Counts

Across the DAMA and routine discharge groups, the proportion of individuals with a 30-day inpatient readmission was similar (19.5% vs 18.7%; P = .47), whereas the proportion with an ED visit was higher (18.6% vs 9.1%; P < .01). There were no differences in the median number of inpatient readmissions (median, 0) and ED visits (median, 0) across both groups. Individuals with a DAMA and those discharged routinely displayed similar median counts of 30-day physician office (median, 1) and nonphysician outpatient encounters (median, 1) (Table 2). Individuals with a DAMA displayed a lower median number of prescription drug fills (median, 2 vs 3) than that among those with a routine discharge (Table 2).

Main Analysis: Thirty-Day Healthcare Resource Utilization

The associations between a DAMA and 30-day inpatient readmissions and ED visits based on the matched sample are presented in Table 3. Individuals with a DAMA had increased odds for an ED visit (AOR, 2.28; 95% CI, 1.90-2.72) but no significant difference in the odds of a 30-day inpatient readmission (AOR, 1.06; 95% CI, 0.91-1.23) compared with those discharged routinely.

The association between a DAMA and count HcRU outcomes is presented in Table 4. Compared with those discharged routinely, individuals with a DAMA displayed no significant difference in rates for physician office visits (IRR, 1.01; 95% CI, 0.91-1.11), nonphysician outpatient encounters (IRR, 0.89; 95% CI, 0.78-1.00), and prescription drug fills (IRR, 1.03; 95% CI, 0.97-1.09) during the 30-day postdischarge period.

Sensitivity Analysis: Ninety-Day Healthcare Resource Utilization

Relative to those discharged routinely, individuals with a DAMA had statistically significant increased odds of 90-day inpatient readmissions (AOR, 1.18; 95% CI, 1.02-1.36), odds of ED visits (AOR, 2.16; 95% CI, 1.85-2.51), and rates of prescription drug fills (IRR, 1.32; 95% CI, 1.29-1.35). No statistically significant differences were observed in the rates of physician office visits and nonphysician outpatient encounters across both groups.

In this commercially insured sample of working age individuals, we identified an association between a DAMA and the likelihood and intensity of postdischarge HcRU. The direction of the association varied across categories of HcRU and the duration of follow-up. A DAMA was associated with increased odds of 30-day ED visits but not 30-day readmissions compared with routine discharges. No significant differences were observed in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups. To our knowledge, this is the first study on DAMAs that examines postdischarge HcRU outside the inpatient setting.

The 0.5% prevalence of DAMAs in our study was lower than the approximate 1% to 2% value that is typically reported in the literature. Prior studies have typically reported results based on mixed-payer populations.^3-10 These mixed-payer populations include publicly insured (Medicare or Medicaid) or uninsured stays, which account for a disproportionate share of all DAMAs. In contrast, commercially insured stays account for the lowest proportion of all DAMAs.¹⁰ Similar to prior literature,⁵ the DAMA group in our study was younger, had a higher proportion of males, had a higher comorbidity burden, and had a shorter LOS than the routinely discharged group.

We observed a greater likelihood of ED utilization after a DAMA. Similar findings have been reported, which may indicate that patients with a DAMA receive inadequate treatment at the time of discharge and may require further acute treatment. For example, a prior study reported that, after a DAMA, individuals with asthma were four times more likely to have an ED visit within 14 days compared with those discharged routinely.⁴

Contrary to prior findings,^3-9 we found no significant difference in the odds of a 30-day inpatient readmission across the DAMA and routine discharge groups, which may be attributable to differences in the populations studied. Those previous studies used mixed payer populations and did not differentiate results by payer type. The mixed payer populations in these studies were older (mean ages, 55 years and above) and had an increased comorbidity burden compared with our commercially insured population. Furthermore, some of these studies were either limited to single sites,⁸ single state hospital systems,^3,4,9 or focused on specific medical populations.^3,4,6-9 Our national sample of commercially insured adults is considerably younger, with a mean age of 43 years. Thirty days may be too brief to observe enough inpatient readmissions for the purpose of comparative analyses. This is suggested by our results, which indicated that there is an association between DAMA and 90-day inpatient readmission. Additionally, nonsignificant findings for 30-day inpatient readmissions may also be due to the small sample size of the DAMA group in our study, which may have limited robust statistical inference. Future studies in a larger population of commercially insured individuals with a DAMA are required to confirm these findings.

Nonsignificant differences in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups may explain the null association with 30-day inpatient readmissions. Prior literature on specific medical populations or individuals with general hospital admissions report that early outpatient follow-up can help prevent 30-day readmissions.^20-25 In our sample, we observed similar rates of outpatient follow-up across the DAMA and routinely discharged groups. Prior studies based on single hospital sites have reported that, at the time of discharge, a lower proportion of individuals with a DAMA received medication prescriptions and outpatient follow-up plans compared with those discharged routinely.^11,12 In contrast, we evaluated prescription drug fills and outpatient visits during the postdischarge period, which may explain the difference in findings.

The present study has several strengths. To the best of our knowledge, our study represents the first and largest retrospective analysis of DAMAs in a national sample of commercially insured adults. In addition to a large generalizable sample, we examine HcRU after a DAMA across major points of service over a longitudinal postdischarge period. Our results provide a comprehensive understanding of utilization outcomes in this population including those outside the inpatient setting, which has been the focus of prior literature. These findings can help guide the implementation of appropriate patient- and system-level interventions to optimize DAMA prevention and mitigate the associated utilization burden on the healthcare system in the postdischarge period.^26,27

Our findings should be interpreted with certain limitations in mind. First, this study used data based on a commercially insured sample of patients and may not be generalizable to publicly insured or uninsured samples. Second, like prior DAMA studies that used the Nationwide Readmissions Database instead,^5-7 our study was unable to account for individual-level factors such as race, marital status, family social support, income, health literacy, and activation in self-care. Further, given the limitations of our data, we were unable to control for hospital characteristics such as bed size, urban-rural designation, teaching status, and control (eg, private or government ownership). Despite the use of propensity score methods to balance both comparison groups on observable sources of confounding, we cannot rule out the possibility of residual confounding. Lastly, due to a lack of data on postdischarge mortality outcomes, we could not control for competing risk of death in our analysis. However, in a population with an average age of 43 years, we did not expect high or differential 30- or 90-day postdischarge mortality rates across both groups.

Our findings suggest several important directions for future research. First, it will be useful to examine these associations among publicly insured and uninsured samples in which a DAMA is more prevalent and in which the associations with HcRU may be more pronounced than they are in the commercially insured population. Secondly, future research should identify subgroups of DAMA patients with an increased propensity for postdischarge HcRU. This can help in the design of individualized outpatient follow-up plans that address patient-specific medical and social needs. Finally, our findings highlight the need for education, practice guidelines, and suitable interventions to help providers in the prevention and management of a DAMA.

Using data from a commercially insured population, we identified associations between a DAMA and postdischarge HcRU. The associations differed by category of HcRU. We identified a positive association with the likelihood of ED utilization but no association with the likelihood of 30-day inpatient readmission or general outpatient utilization. Our results indicate that the examination of inpatient readmissions after a DAMA should not be considered in isolation. The identification of the full range of outpatient and inpatient HcRU after a DAMA in a broad population of patients can improve our understanding of outcomes following a DAMA and support appropriate system-level interventions designed to reduce their prevalence.

The statements, findings, conclusions, views, and opinions contained and expressed in this manuscript are based in part on data obtained under license from IQVIA. Source: IQVIA PharMetrics^® Plus January 2006 – December 2015, IQVIA. All Rights Reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IQVIA or any of its affiliated or subsidiary entities.

Dr Onukwugha reports grants from Bayer Healthcare Pharmaceuticals, grants from Pfizer, Inc, and personal fees from Novo Nordisk outside the submitted work. The other authors have nothing to disclose. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the US Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

The authors acknowledge the support of the University of Maryland, Baltimore Institute for Clinical & Translational Research (ICTR) through the ICTR Voucher Program.

Discharges against medical advice (DAMAs), in which a patient leaves the hospital prior to a physician-recommended endpoint, represent approximately 1% to 2% of inpatient discharges in the United States.¹ When compared with routine discharges, a DAMA is associated with adverse clinical consequences, including an increased risk of all-cause mortality.^2,3 Additionally, due to incomplete care, a DAMA may result in increased healthcare resource utilization (HcRU), including the use of inpatient, emergency department (ED), and outpatient services in the postdischarge period. Quantifying these relationships can provide important information regarding an individual’s healthcare-seeking behavior following a DAMA.

Prior literature has focused on the association between a DAMA and the risk of inpatient readmission. Relative to routine discharges, a DAMA is associated with a 1.5 to 2 times increased risk of a 30-day readmission.^3-9 However, these estimates are based on mixed-payer populations primarily composed (65%-80%) of individuals with public (Medicaid, Medicare) or no insurance. Further, they do not differentiate this association by payer type. It is unclear if prior results apply to commercially insured adults. These individuals represent a small but nonnegligible proportion (19%) of all DAMAs in the United States.¹⁰ Quantifying relationships among commercially insured adults can help advance our understanding of readmission patterns in the DAMA population.

There is limited evidence regarding the relationship between a DAMA and outpatient HcRU in the postdischarge period. Use of ED services after a DAMA has been explored only in specific disease populations such as asthma.⁴ Additionally, prior studies have reported a reduced frequency in the receipt of medication prescriptions and outpatient follow-up plans among individuals with a DAMA at the time of discharge.^11,12 Whether these practices translate to altered patterns of postdischarge prescription drug fills or use of outpatient services is not known.

To address these substantive gaps in the literature, the present study evaluates the association between a DAMA and all-cause HcRU in the postdischarge period among commercially insured adults. We examined HcRU across all points of service including inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters, and prescription drug fills. These results can serve as a benchmark for comparison to future studies on DAMAs among publicly insured or uninsured individuals. Furthermore, such knowledge can help providers, payers, and policy planners make evidence-based decisions regarding postdischarge healthcare delivery.

Data Source

This retrospective study used a 10% random sample of enrollees in the IQVIA PharMetrics^® Plus database (purchased by University of Maryland, Baltimore, under license from IQVIA). The database is composed of fully adjudicated claims and enrollment information from over 70 contributing US health plans and self-insured employer groups for over 140 million unique enrollees from 2006 onward. The enrollee population is generally representative of the commercially insured population that is younger than 65 years of age (with a subset of commercial Medicare and Medicaid) with respect to age and gender.

The database allows longitudinal follow-up for individuals using three files: medical claims, pharmacy claims, and insurance eligibility. The average length of enrollment is 39 months. The claims data represent payments to providers for services rendered to individuals covered by health plans. The medical claims file contains information on diagnostic and therapeutic services rendered in the inpatient and outpatient settings. The pharmacy claims file captures data on prescription drugs dispensed in retail and mail-order settings. The eligibility file contains demographic and insurance eligibility information for individuals.

Study Population

We identified all individuals aged 18 to 64 years with an inpatient admission record between January 1, 2007, and December 31, 2015. All individuals with continuous medical and prescription drug coverage from 6 months prior to the hospital admission date (baseline period) through 30 days following the discharge date (follow-up period) were included. Inpatient admissions with a missing discharge disposition or those that resulted in in-hospital death, discharge to a short-term hospital, skilled nursing facility, intermediate care facility, or any other type of facility were not considered for analysis. Only the first eligible inpatient admission was considered for analysis.

Main Predictor Variable

Individuals with a DAMA were analyzed as the case group. A DAMA was identified using the “Patient Status Code” variable, which represents the discharge disposition of each individual. Individuals who were discharged to home/self-care or discharged to a home health organization formed the control group (hereafter referred to as routine discharge).

Demographic, Clinical, and Hospitalization Characteristics

An individual’s age, sex, and region of residence were determined at the date of hospital admission. The Elixhauser algorithm was used to categorize comorbid conditions (as scores of 0, 1-2, ≥3 depending on number of comorbidities) based on International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis codes during the baseline period.^13,14 The following characteristics of each individual’s eligible inpatient admission were captured: year, timing (weekday or weekend), length of stay (LOS, measured in days), and receipt of a surgical procedure.

Outcomes

All-cause HcRU was identified during the 30-day postdischarge period. Specifically, we identified inpatient readmissions, ED visits, physician office visits, nonphysician outpatient encounters (for example, pathology, radiology, outpatient surgical services), and prescription drug fills. Binary variables (yes or no) were created for inpatient readmissions and ED visits while the remaining HcRU categories (ie, physician office visits, nonphysician outpatient encounters, and prescription drug fills) were analyzed as count variables. In the sensitivity analyses, we provide results for HcRU outcomes among a subgroup of individuals who had at least 90 days of continuous medical and prescription drug benefits following the hospital discharge.

Statistical Analysis

Descriptive Analysis

Measures of interest were reported using summary statistics depending on the nature of the variable. Continuous variables were described using t tests, and categorical variables were described using chi-square tests.

Propensity Score Matching

Cases and controls were matched using a 1:1 greedy matching algorithm based on propensity scores.¹⁵ We developed propensity scores based on confounders that we hypothesized would be associated with a DAMA and postdischarge HcRU. The propensity score model included the following variables: age, sex, region of residence, Elixhauser comorbidity index score, year of admission, timing of admission, LOS, and presence of any surgical procedure during the inpatient admission. The best match between cases and controls was determined based on the absolute difference in their propensity scores, which allowed for a maximal caliper width of 0.2 of the standard deviation of the logit of the propensity score.¹⁶ A standardized difference value of less than 0.1 was used to assess balance in baseline patient and hospital characteristics between cases and controls consistent with prior literature.^17,18 Proportions and balance, as measured by standardized differences between baseline covariates across cases and controls in the matched sample, are displayed in tabular format (Appendix Table 1).

Healthcare Resource Utilization

We estimated the adjusted odds ratio (AOR) using a logistic regression model. The AOR quantified the association between a DAMA and the prevalence of all-cause inpatient readmissions and ED visits during the 30-day postdischarge period. We estimated incident rate ratios (IRR) for count outcomes. Given the large number of individuals with no physician office visits, nonphysician outpatient encounters, or prescription drug fills, we estimated model parameters for IRRs using a finite mixture negative binomial hurdle model.¹⁹ We considered the data to represent a mixture of a constant distribution (which always generates zero counts) and a zero-truncated distribution (which always generates nonzero counts). The finite mixture count models include two outcomes: the mixing probabilities and the count distribution. The mixing probabilities quantify the probability that an observation for the HcRU category will be drawn from either the constant distribution (with mass at zero) or the count distribution. Conditional on having positive values, a zero-truncated generalized linear model (GLM) governs the count variable. Compared with other GLM specifications (eg, Poisson, negative binomial, zero-inflated), the negative binomial hurdle model presented the best-fitting model across several information criteria statistics (Appendix Figures 1-3 and Appendix Tables 2-4).

The GLM results provided IRR for the counts of HcRU. Ratios were interpreted as evidence of increased HcRU (IRR ≥ 1.0) or decreased HcRU (IRR < 1.0) among individuals with a DAMA compared with those discharged routinely. For all HcRU analyses, we reported results for the matched sample. All analyses were conducted using SAS version 9.4 (SAS Institute), and statistical significance was determined at α= .05. The study received the University of Maryland, Baltimore, Institutional Review Board approval (HP-00081497).

The unmatched sample included 457,530 individuals, of whom 0.5% had a DAMA. A consort diagram illustrating cohort inclusion and exclusion criteria is presented in Appendix Figure 4. Demographic, clinical, and inpatient admission characteristics of the unmatched sample and for subgroups defined by discharge status are displayed in Table 1. In the unmatched sample, the median age at admission was higher for individuals with a DAMA than it was for those discharged routinely (43 vs 42 years, respectively), and the proportion of males was higher among those with a DAMA (58.4% vs 33.1%). There were statistically significant differences based on the geographic region of residence and the comorbidity burden across both groups. The median LOS was shorter (1 day vs 2 days), the proportion of weekend admissions was higher (22.2% vs 16.3%), and the proportion of inpatient surgical procedures was lower (12.9% vs 59.2%) among those with a DAMA compared with that among those with routine discharges. The propensity score-matched sample included 2,245 cases and 2,245 controls (Appendix Table 1). Standardized differences for all baseline factors were less than 0.1, indicating that cases and controls were matched on the included baseline factors.

Summary Statistics: Proportions and Counts

Across the DAMA and routine discharge groups, the proportion of individuals with a 30-day inpatient readmission was similar (19.5% vs 18.7%; P = .47), whereas the proportion with an ED visit was higher (18.6% vs 9.1%; P < .01). There were no differences in the median number of inpatient readmissions (median, 0) and ED visits (median, 0) across both groups. Individuals with a DAMA and those discharged routinely displayed similar median counts of 30-day physician office (median, 1) and nonphysician outpatient encounters (median, 1) (Table 2). Individuals with a DAMA displayed a lower median number of prescription drug fills (median, 2 vs 3) than that among those with a routine discharge (Table 2).

Main Analysis: Thirty-Day Healthcare Resource Utilization

The associations between a DAMA and 30-day inpatient readmissions and ED visits based on the matched sample are presented in Table 3. Individuals with a DAMA had increased odds for an ED visit (AOR, 2.28; 95% CI, 1.90-2.72) but no significant difference in the odds of a 30-day inpatient readmission (AOR, 1.06; 95% CI, 0.91-1.23) compared with those discharged routinely.

The association between a DAMA and count HcRU outcomes is presented in Table 4. Compared with those discharged routinely, individuals with a DAMA displayed no significant difference in rates for physician office visits (IRR, 1.01; 95% CI, 0.91-1.11), nonphysician outpatient encounters (IRR, 0.89; 95% CI, 0.78-1.00), and prescription drug fills (IRR, 1.03; 95% CI, 0.97-1.09) during the 30-day postdischarge period.

Sensitivity Analysis: Ninety-Day Healthcare Resource Utilization

Relative to those discharged routinely, individuals with a DAMA had statistically significant increased odds of 90-day inpatient readmissions (AOR, 1.18; 95% CI, 1.02-1.36), odds of ED visits (AOR, 2.16; 95% CI, 1.85-2.51), and rates of prescription drug fills (IRR, 1.32; 95% CI, 1.29-1.35). No statistically significant differences were observed in the rates of physician office visits and nonphysician outpatient encounters across both groups.

In this commercially insured sample of working age individuals, we identified an association between a DAMA and the likelihood and intensity of postdischarge HcRU. The direction of the association varied across categories of HcRU and the duration of follow-up. A DAMA was associated with increased odds of 30-day ED visits but not 30-day readmissions compared with routine discharges. No significant differences were observed in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups. To our knowledge, this is the first study on DAMAs that examines postdischarge HcRU outside the inpatient setting.

The 0.5% prevalence of DAMAs in our study was lower than the approximate 1% to 2% value that is typically reported in the literature. Prior studies have typically reported results based on mixed-payer populations.^3-10 These mixed-payer populations include publicly insured (Medicare or Medicaid) or uninsured stays, which account for a disproportionate share of all DAMAs. In contrast, commercially insured stays account for the lowest proportion of all DAMAs.¹⁰ Similar to prior literature,⁵ the DAMA group in our study was younger, had a higher proportion of males, had a higher comorbidity burden, and had a shorter LOS than the routinely discharged group.

We observed a greater likelihood of ED utilization after a DAMA. Similar findings have been reported, which may indicate that patients with a DAMA receive inadequate treatment at the time of discharge and may require further acute treatment. For example, a prior study reported that, after a DAMA, individuals with asthma were four times more likely to have an ED visit within 14 days compared with those discharged routinely.⁴

Contrary to prior findings,^3-9 we found no significant difference in the odds of a 30-day inpatient readmission across the DAMA and routine discharge groups, which may be attributable to differences in the populations studied. Those previous studies used mixed payer populations and did not differentiate results by payer type. The mixed payer populations in these studies were older (mean ages, 55 years and above) and had an increased comorbidity burden compared with our commercially insured population. Furthermore, some of these studies were either limited to single sites,⁸ single state hospital systems,^3,4,9 or focused on specific medical populations.^3,4,6-9 Our national sample of commercially insured adults is considerably younger, with a mean age of 43 years. Thirty days may be too brief to observe enough inpatient readmissions for the purpose of comparative analyses. This is suggested by our results, which indicated that there is an association between DAMA and 90-day inpatient readmission. Additionally, nonsignificant findings for 30-day inpatient readmissions may also be due to the small sample size of the DAMA group in our study, which may have limited robust statistical inference. Future studies in a larger population of commercially insured individuals with a DAMA are required to confirm these findings.

Nonsignificant differences in the rates of 30-day physician office visits, nonphysician outpatient encounters, and prescription drug fills across both groups may explain the null association with 30-day inpatient readmissions. Prior literature on specific medical populations or individuals with general hospital admissions report that early outpatient follow-up can help prevent 30-day readmissions.^20-25 In our sample, we observed similar rates of outpatient follow-up across the DAMA and routinely discharged groups. Prior studies based on single hospital sites have reported that, at the time of discharge, a lower proportion of individuals with a DAMA received medication prescriptions and outpatient follow-up plans compared with those discharged routinely.^11,12 In contrast, we evaluated prescription drug fills and outpatient visits during the postdischarge period, which may explain the difference in findings.

The present study has several strengths. To the best of our knowledge, our study represents the first and largest retrospective analysis of DAMAs in a national sample of commercially insured adults. In addition to a large generalizable sample, we examine HcRU after a DAMA across major points of service over a longitudinal postdischarge period. Our results provide a comprehensive understanding of utilization outcomes in this population including those outside the inpatient setting, which has been the focus of prior literature. These findings can help guide the implementation of appropriate patient- and system-level interventions to optimize DAMA prevention and mitigate the associated utilization burden on the healthcare system in the postdischarge period.^26,27

Our findings should be interpreted with certain limitations in mind. First, this study used data based on a commercially insured sample of patients and may not be generalizable to publicly insured or uninsured samples. Second, like prior DAMA studies that used the Nationwide Readmissions Database instead,^5-7 our study was unable to account for individual-level factors such as race, marital status, family social support, income, health literacy, and activation in self-care. Further, given the limitations of our data, we were unable to control for hospital characteristics such as bed size, urban-rural designation, teaching status, and control (eg, private or government ownership). Despite the use of propensity score methods to balance both comparison groups on observable sources of confounding, we cannot rule out the possibility of residual confounding. Lastly, due to a lack of data on postdischarge mortality outcomes, we could not control for competing risk of death in our analysis. However, in a population with an average age of 43 years, we did not expect high or differential 30- or 90-day postdischarge mortality rates across both groups.

Our findings suggest several important directions for future research. First, it will be useful to examine these associations among publicly insured and uninsured samples in which a DAMA is more prevalent and in which the associations with HcRU may be more pronounced than they are in the commercially insured population. Secondly, future research should identify subgroups of DAMA patients with an increased propensity for postdischarge HcRU. This can help in the design of individualized outpatient follow-up plans that address patient-specific medical and social needs. Finally, our findings highlight the need for education, practice guidelines, and suitable interventions to help providers in the prevention and management of a DAMA.

Using data from a commercially insured population, we identified associations between a DAMA and postdischarge HcRU. The associations differed by category of HcRU. We identified a positive association with the likelihood of ED utilization but no association with the likelihood of 30-day inpatient readmission or general outpatient utilization. Our results indicate that the examination of inpatient readmissions after a DAMA should not be considered in isolation. The identification of the full range of outpatient and inpatient HcRU after a DAMA in a broad population of patients can improve our understanding of outcomes following a DAMA and support appropriate system-level interventions designed to reduce their prevalence.

The statements, findings, conclusions, views, and opinions contained and expressed in this manuscript are based in part on data obtained under license from IQVIA. Source: IQVIA PharMetrics^® Plus January 2006 – December 2015, IQVIA. All Rights Reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IQVIA or any of its affiliated or subsidiary entities.

Dr Onukwugha reports grants from Bayer Healthcare Pharmaceuticals, grants from Pfizer, Inc, and personal fees from Novo Nordisk outside the submitted work. The other authors have nothing to disclose. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the US Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

The authors acknowledge the support of the University of Maryland, Baltimore Institute for Clinical & Translational Research (ICTR) through the ICTR Voucher Program.

Hypoxemic respiratory failure is a hallmark of severe coronavirus disease 2019 (COVID-19). Initial guidelines favored early mechanical ventilation (MV) over traditional noninvasive strategies, such as high-flow nasal cannula (HFNC) and noninvasive positive pressure ventilation (NIV), based on perceived ineffectiveness and dangers extrapolated from severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) patients.^1,2 As COVID-19 progressed, early MV became associated with prolonged ventilator courses and high mortality.^3-6 Simultaneously, data emerged that HFNC/NIV and self-proning, could successfully stabilize some COVID-19 patients.^7-10 Based on evolving evidence, we implemented a noninvasive COVID-19 respiratory protocol (NCRP) that promoted the early use of HFNC, NIV, and self-proning for hypoxemia in patients with COVID-19, with the intention of avoiding MV in some patients. The protocol was implemented throughout our hospital system, from the Emergency Departments (EDs) to the medical floors and critical care units.

Although preliminary evidence supported the use of HFNC, NIV, and self-proning, the impact of a system-wide noninvasive COVID-19 respiratory protocol on safety has not been well described. The objective of this study was to evaluate patient safety outcomes after implementation of the NCRP, including intubation rate and mortality.

Study Design and Setting

We performed a retrospective chart review, adhering to SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines, to assess safety outcomes after implementation of the NCRP.¹¹ Baystate Health is a not-for-profit, integrated healthcare system in western Massachusetts composed of four hospitals and one free-standing ED with 980 beds serving over 800,000 people. The Baystate Health IRB determined that this project did not meet criteria for Human Subjects Research.

Selection of Participants

A consecutive sample of adults (≥18 years old) admitted to the hospital with a positive nucleic acid test for SARS-CoV-2 (reverse transcriptase–polymerase chain reaction [RT-PCR]) test via nasopharyngeal swab (Cepheid or Roche Cobas 6800) between March 15, 2020, and April 15, 2020, were included. Participants were identified by either an order for the COVID-19 test with a positive result or a discharge diagnosis of COVID-19. Daily rapid response team (RRT), intensive care unit (ICU), and COVID-19 unit logs were reviewed to ensure all COVID-19 patients were included. Patients with positive tests admitted for reasons unrelated to COVID-19 infections, such as patients in labor, were excluded.

Interventions

At the start of the COVID-19 pandemic, the Baystate Health system adopted a conservative approach to the respiratory management of patients with COVID-19. This approach started with nasal cannula up to 6 L/min or nonrebreather up to 15 L/min. If the patient remained in respiratory distress, intubation was recommended.

Based on emerging evidence, the NCRP was created. The details of the NCRP implementation have been previously described.¹² Briefly, over a 4-day period (April 3, 2020, to April 7, 2020), a multidisciplinary team developed, refined, and rapidly implemented a COVID-19 respiratory protocol that encouraged the early use of HFNC, NIV, and self-proning in clinically appropriate patients with hypoxemia and respiratory distress due to COVID-19 prior to intubation across all departments of the Baystate Health system (Appendix 1).

Measurements

A chart review was performed using a structured data collection form (Appendix 2). The data collection form was piloted by three physician-researchers. Data abstraction was performed by 16 clinicians. Abstractors were practicing emergency providers and hospitalists and were blinded to the study outcomes. Abstractors received a 1-hour training and abstracted data from at least five charts in parallel with investigators. An additional 10% of charts were double abstracted to calculate interrater reliability for five variables determined a priori.

To validate the capture of outcomes of interest, we triangulated data sources by cross-referencing the monthly RRT log, the ICU list, all orders for HFNC, and RRT activations. Data abstraction occurred from April 21, 2020, to April 30, 2020. Patients who were still hospitalized after April 30,2020, were followed until hospital discharge, ending July 1, 2020.

Outcomes and Analysis

The primary outcome was mortality, defined as the proportion of deaths by admissions during the post–NCRP implementation period (April 3, 2020, to April 15, 2020), compared with the preimplementation period (March 15, 2020, to April 2, 2020). Deaths were stratified by patient code status (do not resuscitate/do not intubate [DNR/DNI] established prior to admission vs Full Code or presumed Full Code). Mortality outcomes were evaluated using one-sided Fisher exact tests.

To assess whether the protocol led to an increase in the use of the interventions and a decrease in intubations, we compared the use of proning, HFNC, NIV, and intubation before the protocol was implemented and with use after. Intubation rates were analyzed using interrupted time series (piecemeal regression), without adjustments, using a cut point of April 2, 2020.

Secondary outcomes included unexpected cardiac arrests, ICU transfers and consultations, and RRT activations during the postimplementation period, compared with the preimplementation period. Secondary outcomes were evaluated using standard chi-square tests (χ²). Additional descriptive outcomes included use of the NCRP, overall and by components, and in-hospital rates of MV.

From March 15, 2020, through April 15, 2020, there were 469 patients with COVID-19 admitted to the four hospitals of the Baystate Health system. Patients had an average age of 70 years (SD, 16.4), 241 (52%) were female, and 336 (72%) spoke English as their primary language. Most patients, 405 (86.4%), required supplemental oxygen upon being admitted to the hospital (Table 1).

Postimplementation Mortality

Overall, 123 (26.2%) patients died during the study period. In the preimplementation cohort, 24% (61 of 254) of patients died, compared with 28.8% (62 of 215) in the postimplementation cohort (one-sided Fisher exact, P = .14). Excluding patients with an established DNR/DNI prior to admission, 21.8% (48 of 220) patients died in the preimplementation period vs 21.9% (35 of 160) patients after implementation of the NCRP (Table 2).

Secondary Safety Outcomes

There was no increase in RRT activations (preimplementation, 16.5% [42 of 254], vs postimplementation, 11.6% [25 of 215]; χ²P = 0.17) or ICU consultations (preimplementation, 18.1% [47 of 254], vs postimplementation, 16.3% [35 of 215]; χ²P = 0.52). ICU transfers decreased in the postimplementation period (preimplementation, 26.8% [68 of 254], vs postimplementation, 13.5% [29 of 215], χ²P < .001). There was one unexpected cardiac arrest documented in the postimplementation period, compared to none before implementation.

NCRP Protocol Implementation

After implementation, the proportion of patients using HFNC increased from 5.5% (14 of 254) to 24.7% (53 of 215), and self-proning increased from 7.5% (19 of 254) to 22.8% (49 of 215). The proportion of patients who were intubated (MV) decreased from 25.2% (64 of 254) to 10.7% (23 of 215) (χ²P < .01). Interrupted time series analysis demonstrated an immediate reduction in the proportion of patients intubated after the intervention (incident rate ratio, 0.44; 95% CI, 0.23-0.83; P = .012) (Figure). The median time from admission to MV was longer in the postimplementation period patients (postimplementation, 1.4 days; interquartile range, 0.21-2.9; vs preimplementation, 0.66 days; IQR 0.23-1.69).

Interrater Reliability

Interrater reliability for variables chosen a priori was k = 1.0 for self-proning, k = 1.0 for intubation, k = 0.95 for discharge disposition, k = 0.94 for nasal cannula, and k = 0.74 for HFNC.

The rapid spread of SARS-CoV-2 led to early recommendations based on minimal data. As evidence emerged, hospitals were forced to adapt to protect patients and medical providers. As a healthcare system, we incorporated emerging evidence to rapidly implement a noninvasive respiratory treatment protocol. Aware of the methodological problems in evaluating the NCRP itself, we integrated best practices of quality improvement to examine multiple patient safety outcomes after NCRP implementation. We found the rate of intubation decreased with no significant increase in mortality, ICU transfers, RRT activations, or unexpected deaths after the implementation of the NCRP.

Although we were unable to measure all confounders and changes that co-occurred during the study period, initial vital signs, age, BMI, past medical history, and use of oxygen were similar between the pre- and postimplementation cohorts. Further, there were many constants worth noting. First, COVID-19 respiratory protocols were highly regulated to ensure patient safety and minimize COVID-19 transmission. Second, there were no new nonrespiratory treatments or medications during the study. Third, although the COVID-19 hospital census rose during the study, it never overwhelmed resources; there was no rationing of clinical care.

The nonsignificant increase in mortality in the postimplementation period was limited to patients with an established DNR/DNI prior to admission. Established DNR/DNI patients were largely from skilled nursing facilities that were disproportionally impacted in the postimplementation period through clustered outbreaks of COVID-19 in our region, which likely contributed to the increased mortality.¹³

Additionally, despite decreased MV rates in the postimplementation period, we did not find a concurrent decrease in mortality. We do not believe this is a failure of noninvasive treatments. Rather, the increased proportion of DNR/DNI patients, combined with increased nursing home outbreaks in the postimplementation period likely influenced mortality. The postimplementation decreases in ICU transfers and RRT activations supports this hypothesis.

Finally, it is worth nothing that, although the goal of decreasing intubations was to improve patient care and decrease mortality, a decrease in intubations alone, without a change in mortality, may be important because mechanical ventilation has been associated with increased morbidity, such as posttraumatic stress disorder.¹⁴

Taken together, the post–NCRP implementation period appears to have been safe for patients, compared to the preimplementation period’s protocol. Future research may help understand the impact of specific noninvasive interventions on COVID-19–related MV and mortality.

Limitations

Given the urgency of COVID-19 treatment, the NCRP was designed as a quality improvement initiative rather than a prospective trial. Issues of selection bias and confounding limit our ability to evaluate the effect of the NCRP itself. Additionally, unmeasured patient and provider factors may have influenced outcomes. For example, increased provider knowledge and experience treating COVID-19 may have improved outcomes over time, and unmeasured patient characteristics may have been different in the pre- and postimplementation groups. Finally, our study was limited to a single healthcare system, which may limit generalizability

That said, the objective of our study was to evaluate patient safety outcomes of the NCRP, an important first step while other hospital systems continue to confront increasing rates of COVID-19 and must decide on appropriate respiratory management. To that end, our enrollment captured 469 COVID-19 admissions across four diverse hospitals without obvious differences in initial measured covariates. Further, the strict protocolization of respiratory treatments, the evaluation of multiple safety outcomes, and the complete patient follow-up all support the conclusion that NCRP in the postimplementation period did not increase adverse patient outcomes. Further studies are needed to determine the efficacy of the NCRP protocol itself.

In our health system, patients with COVID-19 did not experience a significant increase in mortality, RRT activations, or ICU admissions despite decreased rates of MV after implementation of a respiratory protocol that encouraged early noninvasive management of COVID-19 respiratory distress.

The authors would like to acknowledge Elizabeth Coray, Joseph Lahey, Richard Gabor, Cheryl Greenstein, Sarah Badach, Marie Boutin, Adrienne Wurl, Anthony Kitchen, Michelle Holton, Matthew Shapiro, Eleanor Ragone, Nageshwar Jonnalagadda, Ryan Flynn, Raghuveer Rakasi, and Jasmine Paadam.

Hypoxemic respiratory failure is a hallmark of severe coronavirus disease 2019 (COVID-19). Initial guidelines favored early mechanical ventilation (MV) over traditional noninvasive strategies, such as high-flow nasal cannula (HFNC) and noninvasive positive pressure ventilation (NIV), based on perceived ineffectiveness and dangers extrapolated from severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) patients.^1,2 As COVID-19 progressed, early MV became associated with prolonged ventilator courses and high mortality.^3-6 Simultaneously, data emerged that HFNC/NIV and self-proning, could successfully stabilize some COVID-19 patients.^7-10 Based on evolving evidence, we implemented a noninvasive COVID-19 respiratory protocol (NCRP) that promoted the early use of HFNC, NIV, and self-proning for hypoxemia in patients with COVID-19, with the intention of avoiding MV in some patients. The protocol was implemented throughout our hospital system, from the Emergency Departments (EDs) to the medical floors and critical care units.

Although preliminary evidence supported the use of HFNC, NIV, and self-proning, the impact of a system-wide noninvasive COVID-19 respiratory protocol on safety has not been well described. The objective of this study was to evaluate patient safety outcomes after implementation of the NCRP, including intubation rate and mortality.

Study Design and Setting

We performed a retrospective chart review, adhering to SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines, to assess safety outcomes after implementation of the NCRP.¹¹ Baystate Health is a not-for-profit, integrated healthcare system in western Massachusetts composed of four hospitals and one free-standing ED with 980 beds serving over 800,000 people. The Baystate Health IRB determined that this project did not meet criteria for Human Subjects Research.

Selection of Participants

A consecutive sample of adults (≥18 years old) admitted to the hospital with a positive nucleic acid test for SARS-CoV-2 (reverse transcriptase–polymerase chain reaction [RT-PCR]) test via nasopharyngeal swab (Cepheid or Roche Cobas 6800) between March 15, 2020, and April 15, 2020, were included. Participants were identified by either an order for the COVID-19 test with a positive result or a discharge diagnosis of COVID-19. Daily rapid response team (RRT), intensive care unit (ICU), and COVID-19 unit logs were reviewed to ensure all COVID-19 patients were included. Patients with positive tests admitted for reasons unrelated to COVID-19 infections, such as patients in labor, were excluded.

Interventions

At the start of the COVID-19 pandemic, the Baystate Health system adopted a conservative approach to the respiratory management of patients with COVID-19. This approach started with nasal cannula up to 6 L/min or nonrebreather up to 15 L/min. If the patient remained in respiratory distress, intubation was recommended.

Based on emerging evidence, the NCRP was created. The details of the NCRP implementation have been previously described.¹² Briefly, over a 4-day period (April 3, 2020, to April 7, 2020), a multidisciplinary team developed, refined, and rapidly implemented a COVID-19 respiratory protocol that encouraged the early use of HFNC, NIV, and self-proning in clinically appropriate patients with hypoxemia and respiratory distress due to COVID-19 prior to intubation across all departments of the Baystate Health system (Appendix 1).

Measurements

A chart review was performed using a structured data collection form (Appendix 2). The data collection form was piloted by three physician-researchers. Data abstraction was performed by 16 clinicians. Abstractors were practicing emergency providers and hospitalists and were blinded to the study outcomes. Abstractors received a 1-hour training and abstracted data from at least five charts in parallel with investigators. An additional 10% of charts were double abstracted to calculate interrater reliability for five variables determined a priori.

To validate the capture of outcomes of interest, we triangulated data sources by cross-referencing the monthly RRT log, the ICU list, all orders for HFNC, and RRT activations. Data abstraction occurred from April 21, 2020, to April 30, 2020. Patients who were still hospitalized after April 30,2020, were followed until hospital discharge, ending July 1, 2020.

Outcomes and Analysis

The primary outcome was mortality, defined as the proportion of deaths by admissions during the post–NCRP implementation period (April 3, 2020, to April 15, 2020), compared with the preimplementation period (March 15, 2020, to April 2, 2020). Deaths were stratified by patient code status (do not resuscitate/do not intubate [DNR/DNI] established prior to admission vs Full Code or presumed Full Code). Mortality outcomes were evaluated using one-sided Fisher exact tests.

To assess whether the protocol led to an increase in the use of the interventions and a decrease in intubations, we compared the use of proning, HFNC, NIV, and intubation before the protocol was implemented and with use after. Intubation rates were analyzed using interrupted time series (piecemeal regression), without adjustments, using a cut point of April 2, 2020.

Secondary outcomes included unexpected cardiac arrests, ICU transfers and consultations, and RRT activations during the postimplementation period, compared with the preimplementation period. Secondary outcomes were evaluated using standard chi-square tests (χ²). Additional descriptive outcomes included use of the NCRP, overall and by components, and in-hospital rates of MV.

From March 15, 2020, through April 15, 2020, there were 469 patients with COVID-19 admitted to the four hospitals of the Baystate Health system. Patients had an average age of 70 years (SD, 16.4), 241 (52%) were female, and 336 (72%) spoke English as their primary language. Most patients, 405 (86.4%), required supplemental oxygen upon being admitted to the hospital (Table 1).

Postimplementation Mortality

Overall, 123 (26.2%) patients died during the study period. In the preimplementation cohort, 24% (61 of 254) of patients died, compared with 28.8% (62 of 215) in the postimplementation cohort (one-sided Fisher exact, P = .14). Excluding patients with an established DNR/DNI prior to admission, 21.8% (48 of 220) patients died in the preimplementation period vs 21.9% (35 of 160) patients after implementation of the NCRP (Table 2).

Secondary Safety Outcomes

There was no increase in RRT activations (preimplementation, 16.5% [42 of 254], vs postimplementation, 11.6% [25 of 215]; χ²P = 0.17) or ICU consultations (preimplementation, 18.1% [47 of 254], vs postimplementation, 16.3% [35 of 215]; χ²P = 0.52). ICU transfers decreased in the postimplementation period (preimplementation, 26.8% [68 of 254], vs postimplementation, 13.5% [29 of 215], χ²P < .001). There was one unexpected cardiac arrest documented in the postimplementation period, compared to none before implementation.

NCRP Protocol Implementation

After implementation, the proportion of patients using HFNC increased from 5.5% (14 of 254) to 24.7% (53 of 215), and self-proning increased from 7.5% (19 of 254) to 22.8% (49 of 215). The proportion of patients who were intubated (MV) decreased from 25.2% (64 of 254) to 10.7% (23 of 215) (χ²P < .01). Interrupted time series analysis demonstrated an immediate reduction in the proportion of patients intubated after the intervention (incident rate ratio, 0.44; 95% CI, 0.23-0.83; P = .012) (Figure). The median time from admission to MV was longer in the postimplementation period patients (postimplementation, 1.4 days; interquartile range, 0.21-2.9; vs preimplementation, 0.66 days; IQR 0.23-1.69).

Interrater Reliability

Interrater reliability for variables chosen a priori was k = 1.0 for self-proning, k = 1.0 for intubation, k = 0.95 for discharge disposition, k = 0.94 for nasal cannula, and k = 0.74 for HFNC.

The rapid spread of SARS-CoV-2 led to early recommendations based on minimal data. As evidence emerged, hospitals were forced to adapt to protect patients and medical providers. As a healthcare system, we incorporated emerging evidence to rapidly implement a noninvasive respiratory treatment protocol. Aware of the methodological problems in evaluating the NCRP itself, we integrated best practices of quality improvement to examine multiple patient safety outcomes after NCRP implementation. We found the rate of intubation decreased with no significant increase in mortality, ICU transfers, RRT activations, or unexpected deaths after the implementation of the NCRP.

Although we were unable to measure all confounders and changes that co-occurred during the study period, initial vital signs, age, BMI, past medical history, and use of oxygen were similar between the pre- and postimplementation cohorts. Further, there were many constants worth noting. First, COVID-19 respiratory protocols were highly regulated to ensure patient safety and minimize COVID-19 transmission. Second, there were no new nonrespiratory treatments or medications during the study. Third, although the COVID-19 hospital census rose during the study, it never overwhelmed resources; there was no rationing of clinical care.

The nonsignificant increase in mortality in the postimplementation period was limited to patients with an established DNR/DNI prior to admission. Established DNR/DNI patients were largely from skilled nursing facilities that were disproportionally impacted in the postimplementation period through clustered outbreaks of COVID-19 in our region, which likely contributed to the increased mortality.¹³

Additionally, despite decreased MV rates in the postimplementation period, we did not find a concurrent decrease in mortality. We do not believe this is a failure of noninvasive treatments. Rather, the increased proportion of DNR/DNI patients, combined with increased nursing home outbreaks in the postimplementation period likely influenced mortality. The postimplementation decreases in ICU transfers and RRT activations supports this hypothesis.

Finally, it is worth nothing that, although the goal of decreasing intubations was to improve patient care and decrease mortality, a decrease in intubations alone, without a change in mortality, may be important because mechanical ventilation has been associated with increased morbidity, such as posttraumatic stress disorder.¹⁴

Taken together, the post–NCRP implementation period appears to have been safe for patients, compared to the preimplementation period’s protocol. Future research may help understand the impact of specific noninvasive interventions on COVID-19–related MV and mortality.

Limitations

Given the urgency of COVID-19 treatment, the NCRP was designed as a quality improvement initiative rather than a prospective trial. Issues of selection bias and confounding limit our ability to evaluate the effect of the NCRP itself. Additionally, unmeasured patient and provider factors may have influenced outcomes. For example, increased provider knowledge and experience treating COVID-19 may have improved outcomes over time, and unmeasured patient characteristics may have been different in the pre- and postimplementation groups. Finally, our study was limited to a single healthcare system, which may limit generalizability

That said, the objective of our study was to evaluate patient safety outcomes of the NCRP, an important first step while other hospital systems continue to confront increasing rates of COVID-19 and must decide on appropriate respiratory management. To that end, our enrollment captured 469 COVID-19 admissions across four diverse hospitals without obvious differences in initial measured covariates. Further, the strict protocolization of respiratory treatments, the evaluation of multiple safety outcomes, and the complete patient follow-up all support the conclusion that NCRP in the postimplementation period did not increase adverse patient outcomes. Further studies are needed to determine the efficacy of the NCRP protocol itself.

In our health system, patients with COVID-19 did not experience a significant increase in mortality, RRT activations, or ICU admissions despite decreased rates of MV after implementation of a respiratory protocol that encouraged early noninvasive management of COVID-19 respiratory distress.

The authors would like to acknowledge Elizabeth Coray, Joseph Lahey, Richard Gabor, Cheryl Greenstein, Sarah Badach, Marie Boutin, Adrienne Wurl, Anthony Kitchen, Michelle Holton, Matthew Shapiro, Eleanor Ragone, Nageshwar Jonnalagadda, Ryan Flynn, Raghuveer Rakasi, and Jasmine Paadam.

Hypoxemic respiratory failure is a hallmark of severe coronavirus disease 2019 (COVID-19). Initial guidelines favored early mechanical ventilation (MV) over traditional noninvasive strategies, such as high-flow nasal cannula (HFNC) and noninvasive positive pressure ventilation (NIV), based on perceived ineffectiveness and dangers extrapolated from severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) patients.^1,2 As COVID-19 progressed, early MV became associated with prolonged ventilator courses and high mortality.^3-6 Simultaneously, data emerged that HFNC/NIV and self-proning, could successfully stabilize some COVID-19 patients.^7-10 Based on evolving evidence, we implemented a noninvasive COVID-19 respiratory protocol (NCRP) that promoted the early use of HFNC, NIV, and self-proning for hypoxemia in patients with COVID-19, with the intention of avoiding MV in some patients. The protocol was implemented throughout our hospital system, from the Emergency Departments (EDs) to the medical floors and critical care units.

Although preliminary evidence supported the use of HFNC, NIV, and self-proning, the impact of a system-wide noninvasive COVID-19 respiratory protocol on safety has not been well described. The objective of this study was to evaluate patient safety outcomes after implementation of the NCRP, including intubation rate and mortality.

Study Design and Setting

We performed a retrospective chart review, adhering to SQUIRE (Standards for Quality Improvement Reporting Excellence) Guidelines, to assess safety outcomes after implementation of the NCRP.¹¹ Baystate Health is a not-for-profit, integrated healthcare system in western Massachusetts composed of four hospitals and one free-standing ED with 980 beds serving over 800,000 people. The Baystate Health IRB determined that this project did not meet criteria for Human Subjects Research.

Selection of Participants

A consecutive sample of adults (≥18 years old) admitted to the hospital with a positive nucleic acid test for SARS-CoV-2 (reverse transcriptase–polymerase chain reaction [RT-PCR]) test via nasopharyngeal swab (Cepheid or Roche Cobas 6800) between March 15, 2020, and April 15, 2020, were included. Participants were identified by either an order for the COVID-19 test with a positive result or a discharge diagnosis of COVID-19. Daily rapid response team (RRT), intensive care unit (ICU), and COVID-19 unit logs were reviewed to ensure all COVID-19 patients were included. Patients with positive tests admitted for reasons unrelated to COVID-19 infections, such as patients in labor, were excluded.

Interventions

At the start of the COVID-19 pandemic, the Baystate Health system adopted a conservative approach to the respiratory management of patients with COVID-19. This approach started with nasal cannula up to 6 L/min or nonrebreather up to 15 L/min. If the patient remained in respiratory distress, intubation was recommended.

Based on emerging evidence, the NCRP was created. The details of the NCRP implementation have been previously described.¹² Briefly, over a 4-day period (April 3, 2020, to April 7, 2020), a multidisciplinary team developed, refined, and rapidly implemented a COVID-19 respiratory protocol that encouraged the early use of HFNC, NIV, and self-proning in clinically appropriate patients with hypoxemia and respiratory distress due to COVID-19 prior to intubation across all departments of the Baystate Health system (Appendix 1).

Measurements

A chart review was performed using a structured data collection form (Appendix 2). The data collection form was piloted by three physician-researchers. Data abstraction was performed by 16 clinicians. Abstractors were practicing emergency providers and hospitalists and were blinded to the study outcomes. Abstractors received a 1-hour training and abstracted data from at least five charts in parallel with investigators. An additional 10% of charts were double abstracted to calculate interrater reliability for five variables determined a priori.

To validate the capture of outcomes of interest, we triangulated data sources by cross-referencing the monthly RRT log, the ICU list, all orders for HFNC, and RRT activations. Data abstraction occurred from April 21, 2020, to April 30, 2020. Patients who were still hospitalized after April 30,2020, were followed until hospital discharge, ending July 1, 2020.

Outcomes and Analysis

The primary outcome was mortality, defined as the proportion of deaths by admissions during the post–NCRP implementation period (April 3, 2020, to April 15, 2020), compared with the preimplementation period (March 15, 2020, to April 2, 2020). Deaths were stratified by patient code status (do not resuscitate/do not intubate [DNR/DNI] established prior to admission vs Full Code or presumed Full Code). Mortality outcomes were evaluated using one-sided Fisher exact tests.

To assess whether the protocol led to an increase in the use of the interventions and a decrease in intubations, we compared the use of proning, HFNC, NIV, and intubation before the protocol was implemented and with use after. Intubation rates were analyzed using interrupted time series (piecemeal regression), without adjustments, using a cut point of April 2, 2020.

Secondary outcomes included unexpected cardiac arrests, ICU transfers and consultations, and RRT activations during the postimplementation period, compared with the preimplementation period. Secondary outcomes were evaluated using standard chi-square tests (χ²). Additional descriptive outcomes included use of the NCRP, overall and by components, and in-hospital rates of MV.

From March 15, 2020, through April 15, 2020, there were 469 patients with COVID-19 admitted to the four hospitals of the Baystate Health system. Patients had an average age of 70 years (SD, 16.4), 241 (52%) were female, and 336 (72%) spoke English as their primary language. Most patients, 405 (86.4%), required supplemental oxygen upon being admitted to the hospital (Table 1).

Postimplementation Mortality

Overall, 123 (26.2%) patients died during the study period. In the preimplementation cohort, 24% (61 of 254) of patients died, compared with 28.8% (62 of 215) in the postimplementation cohort (one-sided Fisher exact, P = .14). Excluding patients with an established DNR/DNI prior to admission, 21.8% (48 of 220) patients died in the preimplementation period vs 21.9% (35 of 160) patients after implementation of the NCRP (Table 2).

Secondary Safety Outcomes

There was no increase in RRT activations (preimplementation, 16.5% [42 of 254], vs postimplementation, 11.6% [25 of 215]; χ²P = 0.17) or ICU consultations (preimplementation, 18.1% [47 of 254], vs postimplementation, 16.3% [35 of 215]; χ²P = 0.52). ICU transfers decreased in the postimplementation period (preimplementation, 26.8% [68 of 254], vs postimplementation, 13.5% [29 of 215], χ²P < .001). There was one unexpected cardiac arrest documented in the postimplementation period, compared to none before implementation.

NCRP Protocol Implementation

After implementation, the proportion of patients using HFNC increased from 5.5% (14 of 254) to 24.7% (53 of 215), and self-proning increased from 7.5% (19 of 254) to 22.8% (49 of 215). The proportion of patients who were intubated (MV) decreased from 25.2% (64 of 254) to 10.7% (23 of 215) (χ²P < .01). Interrupted time series analysis demonstrated an immediate reduction in the proportion of patients intubated after the intervention (incident rate ratio, 0.44; 95% CI, 0.23-0.83; P = .012) (Figure). The median time from admission to MV was longer in the postimplementation period patients (postimplementation, 1.4 days; interquartile range, 0.21-2.9; vs preimplementation, 0.66 days; IQR 0.23-1.69).

Interrater Reliability

Interrater reliability for variables chosen a priori was k = 1.0 for self-proning, k = 1.0 for intubation, k = 0.95 for discharge disposition, k = 0.94 for nasal cannula, and k = 0.74 for HFNC.

The rapid spread of SARS-CoV-2 led to early recommendations based on minimal data. As evidence emerged, hospitals were forced to adapt to protect patients and medical providers. As a healthcare system, we incorporated emerging evidence to rapidly implement a noninvasive respiratory treatment protocol. Aware of the methodological problems in evaluating the NCRP itself, we integrated best practices of quality improvement to examine multiple patient safety outcomes after NCRP implementation. We found the rate of intubation decreased with no significant increase in mortality, ICU transfers, RRT activations, or unexpected deaths after the implementation of the NCRP.

Although we were unable to measure all confounders and changes that co-occurred during the study period, initial vital signs, age, BMI, past medical history, and use of oxygen were similar between the pre- and postimplementation cohorts. Further, there were many constants worth noting. First, COVID-19 respiratory protocols were highly regulated to ensure patient safety and minimize COVID-19 transmission. Second, there were no new nonrespiratory treatments or medications during the study. Third, although the COVID-19 hospital census rose during the study, it never overwhelmed resources; there was no rationing of clinical care.

The nonsignificant increase in mortality in the postimplementation period was limited to patients with an established DNR/DNI prior to admission. Established DNR/DNI patients were largely from skilled nursing facilities that were disproportionally impacted in the postimplementation period through clustered outbreaks of COVID-19 in our region, which likely contributed to the increased mortality.¹³

Additionally, despite decreased MV rates in the postimplementation period, we did not find a concurrent decrease in mortality. We do not believe this is a failure of noninvasive treatments. Rather, the increased proportion of DNR/DNI patients, combined with increased nursing home outbreaks in the postimplementation period likely influenced mortality. The postimplementation decreases in ICU transfers and RRT activations supports this hypothesis.

Finally, it is worth nothing that, although the goal of decreasing intubations was to improve patient care and decrease mortality, a decrease in intubations alone, without a change in mortality, may be important because mechanical ventilation has been associated with increased morbidity, such as posttraumatic stress disorder.¹⁴

Taken together, the post–NCRP implementation period appears to have been safe for patients, compared to the preimplementation period’s protocol. Future research may help understand the impact of specific noninvasive interventions on COVID-19–related MV and mortality.

Limitations

Given the urgency of COVID-19 treatment, the NCRP was designed as a quality improvement initiative rather than a prospective trial. Issues of selection bias and confounding limit our ability to evaluate the effect of the NCRP itself. Additionally, unmeasured patient and provider factors may have influenced outcomes. For example, increased provider knowledge and experience treating COVID-19 may have improved outcomes over time, and unmeasured patient characteristics may have been different in the pre- and postimplementation groups. Finally, our study was limited to a single healthcare system, which may limit generalizability

That said, the objective of our study was to evaluate patient safety outcomes of the NCRP, an important first step while other hospital systems continue to confront increasing rates of COVID-19 and must decide on appropriate respiratory management. To that end, our enrollment captured 469 COVID-19 admissions across four diverse hospitals without obvious differences in initial measured covariates. Further, the strict protocolization of respiratory treatments, the evaluation of multiple safety outcomes, and the complete patient follow-up all support the conclusion that NCRP in the postimplementation period did not increase adverse patient outcomes. Further studies are needed to determine the efficacy of the NCRP protocol itself.

In our health system, patients with COVID-19 did not experience a significant increase in mortality, RRT activations, or ICU admissions despite decreased rates of MV after implementation of a respiratory protocol that encouraged early noninvasive management of COVID-19 respiratory distress.

The authors would like to acknowledge Elizabeth Coray, Joseph Lahey, Richard Gabor, Cheryl Greenstein, Sarah Badach, Marie Boutin, Adrienne Wurl, Anthony Kitchen, Michelle Holton, Matthew Shapiro, Eleanor Ragone, Nageshwar Jonnalagadda, Ryan Flynn, Raghuveer Rakasi, and Jasmine Paadam.

Vancomycin, a glycopeptide antibiotic, has been used for decades, yet knowledge gaps remain regarding the most appropriate dosing approach to optimize therapeutic effect while avoiding adverse effects in all patient populations. A committee composed of members of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists reviewed data available since publication of the original 2009 vancomycin dosing guidelines to provide new recommendations regarding vancomycin dosing and serum concentration–monitoring in the empiric treatment of presumed or confirmed methicillin-resistant Staphylococcus aureus (MRSA) infections.¹

The new guidelines provide 25 recommendations encompassing the following topics: vancomycin dosing and monitoring in adult, pediatric, and neonate care; vancomycin minimum inhibitory concentration (MIC) susceptibility testing; continuous infusion vs intermittent infusion; loading doses; dosing in obesity; and dosing in patients on hemodialysis and continuous renal replacement therapy. Because hospitalists in pediatric and adult care frequently prescribe vancomycin for empiric and targeted treatment of serious infections, they have a vested interest in ensuring optimal vancomycin outcomes (ie, best efficacy with least toxicity) with use of therapeutic drug monitoring and personalized dosing of vancomycin. Thus, it is important for hospitalists to be aware of the updated guideline and pivotal changes regarding therapeutic drug monitoring. In this guideline review, we will focus on the major differences from the 2009 guideline, specifically regarding therapeutic monitoring in adults and children.

The guideline includes pharmacology language and terminology with which many clinicians may not be familiar. To better understand the rationale for the guideline changes, a few concepts will be reviewed. Overall, antibiotics are dosed based on preclinical studies to determine the needed drug exposure for optimal efficacy. β-Lactams, for example, are optimally dosed with longer drug exposure time above the MIC of the infectious organism. Alternatively, area under the concentration time curve (AUC) describes the efficacy and toxicity of many other antibiotics. Since AUC is derived from products of concentration (mg/L) and time (hours), the units are often mg × h/L. For vancomycin, both drug exposure (ie, AUC) and organism susceptibility (ie, MIC) are incorporated to determine optimal drug exposure, with the ratio of AUC to MIC being the ideal marker. Therapeutic drug monitoring of vancomycin has classically been conducted with trough concentration monitoring, but with the updated guideline, there will be a transition to AUC monitoring that will affect patient care and experience.

The following is a summary of recommendations 1 to 6:

• In adults, the optimal drug exposure for vancomycin should be an AUC to MIC ratio of 400 to 600 for MRSA, with the assumption of MIC to be 1 mg/L (evidence quality: A-II).
  • The preferred method to monitor AUC is with a clinical statistical software that uses two blood samples (1 to 2 hours after completion of infusion and at the end of a dosing interval [ie, trough]) (evidence quality: A-II).
  • An alternative approach would be to use first-order pharmacokinetic equations at steady state with a peak and trough (evidence quality: A-II).

• These approaches replace the previously recommended trough-only monitoring. AUC-targeted exposure should generally be achieved within 48 hours; severity of infection does not justify higher AUC goals. Once the goal AUC is achieved, once-weekly monitoring is recommended for hemodynamically stable patients, but more frequent or daily monitoring is advised in patients at high risk of nephrotoxicity or who are hemodynamically unstable (evidence quality: B-II).

The currently accepted optimal drug exposure for vancomycin is an AUC to MIC ratio of 400 to 600 to maximize efficacy and minimize nephrotoxicity.² Due to clinical inconvenience of performing AUC-based monitoring for vancomycin in the past, previous guidelines recommended using trough concentrations as a surrogate marker for an AUC to MIC ratio, with the goal trough being 15 to 20 mg/L for serious MRSA infections.³ However, trough values may not correlate well with AUC. For example, a trough of 15 mg/L may represent an AUC ranging from 400 to 1000 mg × h/L over 24 hours. Without knowing an accurate AUC, there is risk for ineffective bactericidal activity with low AUCs or nephrotoxicity with high AUCs. Compared with trough-only monitoring, AUC-guided dosing is associated with decreased risk of acute kidney injury.^4,5 Therefore, the recommendation to transition to two-sample collection with a peak and trough was included.

Software programs are now readily available to compute the AUC and work best with peak and trough values rather than a single trough value because computing with two concentrations will rely more on specific patient data than it does on previously published vancomycin models. Trough-only monitoring (and without the support of clinical software) may still be possible when the exposures needed are further from the toxic range. To this end, trough-only monitoring may be reasonable when infections are not MRSA and are less invasive (eg, cellulitis) since the guideline found insufficient evidence for AUC monitoring in these scenarios. While specific targets are not provided, a plethora of historical literature demonstrated low kidney injury rates when troughs were maintained between 5 to 10 mg/L.

The following is a summary of recommendations 18 to 20:

• In pediatric care, based on a target AUC to MIC ratio of 400 to 600 with the assumption of MIC to be 1 mg/L, initial vancomycin dosage for MRSA is as follows (evidence quality: A-II) :
  • 60 to 80 mg/kg per day, divided into four doses, each given 6 hours apart, for children 3 months and older but younger than 12 years
  • 60 to 70 mg/kg per day, divided into four doses, each given 6 hours apart, for children 12 years and older
• As recommended in adults, use of a statistical software program to measure AUC is the optimal approach in pediatric care because it can account for age, weight, and renal function, which should be monitored closely. Monitoring should begin within 48 hours of therapy. Vancomycin AUC and trough concentrations should be less than 800 µg × h/mL over 24 hours and 15 µg/mL, respectively, to minimize acute kidney injury (evidence quality: A-II).

All the recommendations for pediatrics are new for the updated guideline. Pediatric data to support these recommendations are fewer in comparison with adult literature. Given MRSA infections are felt to be similar in adults and children, many pediatric recommendations are extrapolated from adult data and recommendations. The strongest level of evidence in children is the association of acute kidney injury with higher vancomycin exposure, especially with troughs exceeding 15 to 20 mg/L.⁶ In addition, one pediatric study found an AUC exposure of greater than 800 mg × h/L over 24 hours was strongly associated with risk for acute kidney injury.⁷ These findings suggest that high vancomycin exposure correlates with nephrotoxicity, so with AUC monitoring, the goal exposure should be less than 800 mg × hr/L over 24 hours.

Only one study has evaluated statistical software and prediction of AUC in pediatrics.⁸ A two-concentration approach (peak and trough) outperformed trough-only monitoring for accuracy and precision in determining AUC. While limited to one study, the results are similar to the studies completed in adults, thereby leading to the recommendation of the two-concentration technique in children.

Prospective outcome data are lacking, but multiple retrospective studies have examined S aureus bacteremia in children. Thus far, there have been no studies that have determined the optimal vancomycin exposure required for successful outcomes.^9,10 The proven risks of toxicity are the primary driver for the pediatric guideline change with the outcomes extrapolated from adult data.

Methods in Preparing Guideline

The main strength of the guideline is that the committee was represented by multiple organizations, which created a multidisciplinary panel of pharmacists and infectious disease physicians with clinical and research expertise in vancomycin dosing. Evidence was graded using an adaptation from the Canadian Task Force on the Periodic Health Examination.¹¹ The draft was peer-reviewed by the society organizations and allowed for comments, suggestions, and recommendations.

Sources of Potential Conflict of Interest or Bias

Disclosures of all authors were reported and identified in the guideline. While many members are involved with pharmaceutical companies through research or speakers’ roles, vancomycin, a generic drug, should have minimal conflicts of interest or bias from this involvement.

Generalizability

Implementation of vancomycin AUC dosing will be hospital dependent due to the implementation-related increase in human resources and the cost of clinical software; many hospital systems do not already have the software integrated into their clinical practice. Local guidelines will have to be developed to help clinicians determine which clinical situations require AUC-based dosing vs trough-only monitoring. Pharmacists at many hospitals are primarily responsible for vancomycin monitoring and provide dosing recommendations to physicians. Depending on a hospital system’s decision, the workload to determine the optimal vancomycin dose may increase, and it will be important to have close collaboration between hospitalists, pharmacists, and infectious diseases clinicians to appropriately educate clinicians who might be required to dose/monitor vancomycin. One potential way to decrease the burden of monitoring with two concentrations is to use specialized software that can perform complex assessments with only a single concentration. These software applications will still require serious collaboration of the aforementioned practitioners to implement. The variation in guideline adoption will likely be even more significant in pediatrics because the literature is extrapolated and the increased blood draws can be more problematic in pediatric patients.

Furthermore, clinicians should understand the dosing guideline is specifically addressing treatment of MRSA infections and extrapolation to other organisms such as coagulase-negative staphylococcal or methicillin-susceptible S aureus infections should be cautioned. Another caveat to note is that, when the MRSA isolate has an MIC of 2 mg/L or higher, these infections are associated with poor outcomes when vancomycin is used and alternative agents are recommended.

Research gaps still remain with appropriate vancomycin drug exposure. In pediatrics, determining the appropriate AUC target will be important given that current recommendations extrapolate from adult data. Future studies can focus on prospective outcome data in both pediatric and adult patients for infections outside of bacteremia or pneumonia, notably central nervous system and osteomyelitis infections. Thresholds for kidney injury will need to be more clearly defined for both adult and pediatric patients. There should also be research emphasis on the appropriate dosing for other non-MRSA invasive infections, notably coagulase-negative staphylococcal infections.

Dr Scheetz reported personal fees for consulting for Achaogen, SIGA technologies, and for serving on an advisory board for Paratek; grants from Merck and Co, Allecra, Nevakar, and SuperTrans Medical; personal fees from Hall, Booth, Smith, PC, and Chambless, Higdon, Richardson, Katz & Griggs, LLP, for consulting and expert testimony, outside the submitted work. In addition, Dr. Scheetz has patent US 2019 / 0099500 A1 pending. Dr Murphy reported having received fees from Becton Dickinson for participation to review IDSA guidelines on gastroenteritis. Dr Tang Girdwood has nothing to disclose.

Dr Murphy and Dr Tang Girdwood are supported by the National Institute of Child Health and Development Cincinnati Pediatric Clinical Pharmacology Postdoctoral Training Program (5T32HD069054-09). Dr Tang Girdwood is also supported by the Cincinnati Children’s Hospital Medical Center Arnold W Strauss Fellow Award and Cincinnati Children’s Hospital Medical Center Hospital Medicine Fellow Award. Dr Scheetz is supported in part by the National Institute of Allergy and Infectious Diseases award (R21AI149026). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Vancomycin, a glycopeptide antibiotic, has been used for decades, yet knowledge gaps remain regarding the most appropriate dosing approach to optimize therapeutic effect while avoiding adverse effects in all patient populations. A committee composed of members of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists reviewed data available since publication of the original 2009 vancomycin dosing guidelines to provide new recommendations regarding vancomycin dosing and serum concentration–monitoring in the empiric treatment of presumed or confirmed methicillin-resistant Staphylococcus aureus (MRSA) infections.¹

The new guidelines provide 25 recommendations encompassing the following topics: vancomycin dosing and monitoring in adult, pediatric, and neonate care; vancomycin minimum inhibitory concentration (MIC) susceptibility testing; continuous infusion vs intermittent infusion; loading doses; dosing in obesity; and dosing in patients on hemodialysis and continuous renal replacement therapy. Because hospitalists in pediatric and adult care frequently prescribe vancomycin for empiric and targeted treatment of serious infections, they have a vested interest in ensuring optimal vancomycin outcomes (ie, best efficacy with least toxicity) with use of therapeutic drug monitoring and personalized dosing of vancomycin. Thus, it is important for hospitalists to be aware of the updated guideline and pivotal changes regarding therapeutic drug monitoring. In this guideline review, we will focus on the major differences from the 2009 guideline, specifically regarding therapeutic monitoring in adults and children.

The guideline includes pharmacology language and terminology with which many clinicians may not be familiar. To better understand the rationale for the guideline changes, a few concepts will be reviewed. Overall, antibiotics are dosed based on preclinical studies to determine the needed drug exposure for optimal efficacy. β-Lactams, for example, are optimally dosed with longer drug exposure time above the MIC of the infectious organism. Alternatively, area under the concentration time curve (AUC) describes the efficacy and toxicity of many other antibiotics. Since AUC is derived from products of concentration (mg/L) and time (hours), the units are often mg × h/L. For vancomycin, both drug exposure (ie, AUC) and organism susceptibility (ie, MIC) are incorporated to determine optimal drug exposure, with the ratio of AUC to MIC being the ideal marker. Therapeutic drug monitoring of vancomycin has classically been conducted with trough concentration monitoring, but with the updated guideline, there will be a transition to AUC monitoring that will affect patient care and experience.

The following is a summary of recommendations 1 to 6:

• In adults, the optimal drug exposure for vancomycin should be an AUC to MIC ratio of 400 to 600 for MRSA, with the assumption of MIC to be 1 mg/L (evidence quality: A-II).
  • The preferred method to monitor AUC is with a clinical statistical software that uses two blood samples (1 to 2 hours after completion of infusion and at the end of a dosing interval [ie, trough]) (evidence quality: A-II).
  • An alternative approach would be to use first-order pharmacokinetic equations at steady state with a peak and trough (evidence quality: A-II).

• These approaches replace the previously recommended trough-only monitoring. AUC-targeted exposure should generally be achieved within 48 hours; severity of infection does not justify higher AUC goals. Once the goal AUC is achieved, once-weekly monitoring is recommended for hemodynamically stable patients, but more frequent or daily monitoring is advised in patients at high risk of nephrotoxicity or who are hemodynamically unstable (evidence quality: B-II).

The currently accepted optimal drug exposure for vancomycin is an AUC to MIC ratio of 400 to 600 to maximize efficacy and minimize nephrotoxicity.² Due to clinical inconvenience of performing AUC-based monitoring for vancomycin in the past, previous guidelines recommended using trough concentrations as a surrogate marker for an AUC to MIC ratio, with the goal trough being 15 to 20 mg/L for serious MRSA infections.³ However, trough values may not correlate well with AUC. For example, a trough of 15 mg/L may represent an AUC ranging from 400 to 1000 mg × h/L over 24 hours. Without knowing an accurate AUC, there is risk for ineffective bactericidal activity with low AUCs or nephrotoxicity with high AUCs. Compared with trough-only monitoring, AUC-guided dosing is associated with decreased risk of acute kidney injury.^4,5 Therefore, the recommendation to transition to two-sample collection with a peak and trough was included.

Software programs are now readily available to compute the AUC and work best with peak and trough values rather than a single trough value because computing with two concentrations will rely more on specific patient data than it does on previously published vancomycin models. Trough-only monitoring (and without the support of clinical software) may still be possible when the exposures needed are further from the toxic range. To this end, trough-only monitoring may be reasonable when infections are not MRSA and are less invasive (eg, cellulitis) since the guideline found insufficient evidence for AUC monitoring in these scenarios. While specific targets are not provided, a plethora of historical literature demonstrated low kidney injury rates when troughs were maintained between 5 to 10 mg/L.

The following is a summary of recommendations 18 to 20:

• In pediatric care, based on a target AUC to MIC ratio of 400 to 600 with the assumption of MIC to be 1 mg/L, initial vancomycin dosage for MRSA is as follows (evidence quality: A-II) :
  • 60 to 80 mg/kg per day, divided into four doses, each given 6 hours apart, for children 3 months and older but younger than 12 years
  • 60 to 70 mg/kg per day, divided into four doses, each given 6 hours apart, for children 12 years and older
• As recommended in adults, use of a statistical software program to measure AUC is the optimal approach in pediatric care because it can account for age, weight, and renal function, which should be monitored closely. Monitoring should begin within 48 hours of therapy. Vancomycin AUC and trough concentrations should be less than 800 µg × h/mL over 24 hours and 15 µg/mL, respectively, to minimize acute kidney injury (evidence quality: A-II).

All the recommendations for pediatrics are new for the updated guideline. Pediatric data to support these recommendations are fewer in comparison with adult literature. Given MRSA infections are felt to be similar in adults and children, many pediatric recommendations are extrapolated from adult data and recommendations. The strongest level of evidence in children is the association of acute kidney injury with higher vancomycin exposure, especially with troughs exceeding 15 to 20 mg/L.⁶ In addition, one pediatric study found an AUC exposure of greater than 800 mg × h/L over 24 hours was strongly associated with risk for acute kidney injury.⁷ These findings suggest that high vancomycin exposure correlates with nephrotoxicity, so with AUC monitoring, the goal exposure should be less than 800 mg × hr/L over 24 hours.

Only one study has evaluated statistical software and prediction of AUC in pediatrics.⁸ A two-concentration approach (peak and trough) outperformed trough-only monitoring for accuracy and precision in determining AUC. While limited to one study, the results are similar to the studies completed in adults, thereby leading to the recommendation of the two-concentration technique in children.

Prospective outcome data are lacking, but multiple retrospective studies have examined S aureus bacteremia in children. Thus far, there have been no studies that have determined the optimal vancomycin exposure required for successful outcomes.^9,10 The proven risks of toxicity are the primary driver for the pediatric guideline change with the outcomes extrapolated from adult data.

Methods in Preparing Guideline

The main strength of the guideline is that the committee was represented by multiple organizations, which created a multidisciplinary panel of pharmacists and infectious disease physicians with clinical and research expertise in vancomycin dosing. Evidence was graded using an adaptation from the Canadian Task Force on the Periodic Health Examination.¹¹ The draft was peer-reviewed by the society organizations and allowed for comments, suggestions, and recommendations.

Sources of Potential Conflict of Interest or Bias

Disclosures of all authors were reported and identified in the guideline. While many members are involved with pharmaceutical companies through research or speakers’ roles, vancomycin, a generic drug, should have minimal conflicts of interest or bias from this involvement.

Generalizability

Implementation of vancomycin AUC dosing will be hospital dependent due to the implementation-related increase in human resources and the cost of clinical software; many hospital systems do not already have the software integrated into their clinical practice. Local guidelines will have to be developed to help clinicians determine which clinical situations require AUC-based dosing vs trough-only monitoring. Pharmacists at many hospitals are primarily responsible for vancomycin monitoring and provide dosing recommendations to physicians. Depending on a hospital system’s decision, the workload to determine the optimal vancomycin dose may increase, and it will be important to have close collaboration between hospitalists, pharmacists, and infectious diseases clinicians to appropriately educate clinicians who might be required to dose/monitor vancomycin. One potential way to decrease the burden of monitoring with two concentrations is to use specialized software that can perform complex assessments with only a single concentration. These software applications will still require serious collaboration of the aforementioned practitioners to implement. The variation in guideline adoption will likely be even more significant in pediatrics because the literature is extrapolated and the increased blood draws can be more problematic in pediatric patients.

Furthermore, clinicians should understand the dosing guideline is specifically addressing treatment of MRSA infections and extrapolation to other organisms such as coagulase-negative staphylococcal or methicillin-susceptible S aureus infections should be cautioned. Another caveat to note is that, when the MRSA isolate has an MIC of 2 mg/L or higher, these infections are associated with poor outcomes when vancomycin is used and alternative agents are recommended.

Research gaps still remain with appropriate vancomycin drug exposure. In pediatrics, determining the appropriate AUC target will be important given that current recommendations extrapolate from adult data. Future studies can focus on prospective outcome data in both pediatric and adult patients for infections outside of bacteremia or pneumonia, notably central nervous system and osteomyelitis infections. Thresholds for kidney injury will need to be more clearly defined for both adult and pediatric patients. There should also be research emphasis on the appropriate dosing for other non-MRSA invasive infections, notably coagulase-negative staphylococcal infections.

Dr Scheetz reported personal fees for consulting for Achaogen, SIGA technologies, and for serving on an advisory board for Paratek; grants from Merck and Co, Allecra, Nevakar, and SuperTrans Medical; personal fees from Hall, Booth, Smith, PC, and Chambless, Higdon, Richardson, Katz & Griggs, LLP, for consulting and expert testimony, outside the submitted work. In addition, Dr. Scheetz has patent US 2019 / 0099500 A1 pending. Dr Murphy reported having received fees from Becton Dickinson for participation to review IDSA guidelines on gastroenteritis. Dr Tang Girdwood has nothing to disclose.

Dr Murphy and Dr Tang Girdwood are supported by the National Institute of Child Health and Development Cincinnati Pediatric Clinical Pharmacology Postdoctoral Training Program (5T32HD069054-09). Dr Tang Girdwood is also supported by the Cincinnati Children’s Hospital Medical Center Arnold W Strauss Fellow Award and Cincinnati Children’s Hospital Medical Center Hospital Medicine Fellow Award. Dr Scheetz is supported in part by the National Institute of Allergy and Infectious Diseases award (R21AI149026). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Vancomycin, a glycopeptide antibiotic, has been used for decades, yet knowledge gaps remain regarding the most appropriate dosing approach to optimize therapeutic effect while avoiding adverse effects in all patient populations. A committee composed of members of the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists reviewed data available since publication of the original 2009 vancomycin dosing guidelines to provide new recommendations regarding vancomycin dosing and serum concentration–monitoring in the empiric treatment of presumed or confirmed methicillin-resistant Staphylococcus aureus (MRSA) infections.¹

The new guidelines provide 25 recommendations encompassing the following topics: vancomycin dosing and monitoring in adult, pediatric, and neonate care; vancomycin minimum inhibitory concentration (MIC) susceptibility testing; continuous infusion vs intermittent infusion; loading doses; dosing in obesity; and dosing in patients on hemodialysis and continuous renal replacement therapy. Because hospitalists in pediatric and adult care frequently prescribe vancomycin for empiric and targeted treatment of serious infections, they have a vested interest in ensuring optimal vancomycin outcomes (ie, best efficacy with least toxicity) with use of therapeutic drug monitoring and personalized dosing of vancomycin. Thus, it is important for hospitalists to be aware of the updated guideline and pivotal changes regarding therapeutic drug monitoring. In this guideline review, we will focus on the major differences from the 2009 guideline, specifically regarding therapeutic monitoring in adults and children.

The guideline includes pharmacology language and terminology with which many clinicians may not be familiar. To better understand the rationale for the guideline changes, a few concepts will be reviewed. Overall, antibiotics are dosed based on preclinical studies to determine the needed drug exposure for optimal efficacy. β-Lactams, for example, are optimally dosed with longer drug exposure time above the MIC of the infectious organism. Alternatively, area under the concentration time curve (AUC) describes the efficacy and toxicity of many other antibiotics. Since AUC is derived from products of concentration (mg/L) and time (hours), the units are often mg × h/L. For vancomycin, both drug exposure (ie, AUC) and organism susceptibility (ie, MIC) are incorporated to determine optimal drug exposure, with the ratio of AUC to MIC being the ideal marker. Therapeutic drug monitoring of vancomycin has classically been conducted with trough concentration monitoring, but with the updated guideline, there will be a transition to AUC monitoring that will affect patient care and experience.

The following is a summary of recommendations 1 to 6:

• In adults, the optimal drug exposure for vancomycin should be an AUC to MIC ratio of 400 to 600 for MRSA, with the assumption of MIC to be 1 mg/L (evidence quality: A-II).
  • The preferred method to monitor AUC is with a clinical statistical software that uses two blood samples (1 to 2 hours after completion of infusion and at the end of a dosing interval [ie, trough]) (evidence quality: A-II).
  • An alternative approach would be to use first-order pharmacokinetic equations at steady state with a peak and trough (evidence quality: A-II).

• These approaches replace the previously recommended trough-only monitoring. AUC-targeted exposure should generally be achieved within 48 hours; severity of infection does not justify higher AUC goals. Once the goal AUC is achieved, once-weekly monitoring is recommended for hemodynamically stable patients, but more frequent or daily monitoring is advised in patients at high risk of nephrotoxicity or who are hemodynamically unstable (evidence quality: B-II).

The currently accepted optimal drug exposure for vancomycin is an AUC to MIC ratio of 400 to 600 to maximize efficacy and minimize nephrotoxicity.² Due to clinical inconvenience of performing AUC-based monitoring for vancomycin in the past, previous guidelines recommended using trough concentrations as a surrogate marker for an AUC to MIC ratio, with the goal trough being 15 to 20 mg/L for serious MRSA infections.³ However, trough values may not correlate well with AUC. For example, a trough of 15 mg/L may represent an AUC ranging from 400 to 1000 mg × h/L over 24 hours. Without knowing an accurate AUC, there is risk for ineffective bactericidal activity with low AUCs or nephrotoxicity with high AUCs. Compared with trough-only monitoring, AUC-guided dosing is associated with decreased risk of acute kidney injury.^4,5 Therefore, the recommendation to transition to two-sample collection with a peak and trough was included.

Software programs are now readily available to compute the AUC and work best with peak and trough values rather than a single trough value because computing with two concentrations will rely more on specific patient data than it does on previously published vancomycin models. Trough-only monitoring (and without the support of clinical software) may still be possible when the exposures needed are further from the toxic range. To this end, trough-only monitoring may be reasonable when infections are not MRSA and are less invasive (eg, cellulitis) since the guideline found insufficient evidence for AUC monitoring in these scenarios. While specific targets are not provided, a plethora of historical literature demonstrated low kidney injury rates when troughs were maintained between 5 to 10 mg/L.

The following is a summary of recommendations 18 to 20:

• In pediatric care, based on a target AUC to MIC ratio of 400 to 600 with the assumption of MIC to be 1 mg/L, initial vancomycin dosage for MRSA is as follows (evidence quality: A-II) :
  • 60 to 80 mg/kg per day, divided into four doses, each given 6 hours apart, for children 3 months and older but younger than 12 years
  • 60 to 70 mg/kg per day, divided into four doses, each given 6 hours apart, for children 12 years and older
• As recommended in adults, use of a statistical software program to measure AUC is the optimal approach in pediatric care because it can account for age, weight, and renal function, which should be monitored closely. Monitoring should begin within 48 hours of therapy. Vancomycin AUC and trough concentrations should be less than 800 µg × h/mL over 24 hours and 15 µg/mL, respectively, to minimize acute kidney injury (evidence quality: A-II).

All the recommendations for pediatrics are new for the updated guideline. Pediatric data to support these recommendations are fewer in comparison with adult literature. Given MRSA infections are felt to be similar in adults and children, many pediatric recommendations are extrapolated from adult data and recommendations. The strongest level of evidence in children is the association of acute kidney injury with higher vancomycin exposure, especially with troughs exceeding 15 to 20 mg/L.⁶ In addition, one pediatric study found an AUC exposure of greater than 800 mg × h/L over 24 hours was strongly associated with risk for acute kidney injury.⁷ These findings suggest that high vancomycin exposure correlates with nephrotoxicity, so with AUC monitoring, the goal exposure should be less than 800 mg × hr/L over 24 hours.

Only one study has evaluated statistical software and prediction of AUC in pediatrics.⁸ A two-concentration approach (peak and trough) outperformed trough-only monitoring for accuracy and precision in determining AUC. While limited to one study, the results are similar to the studies completed in adults, thereby leading to the recommendation of the two-concentration technique in children.

Prospective outcome data are lacking, but multiple retrospective studies have examined S aureus bacteremia in children. Thus far, there have been no studies that have determined the optimal vancomycin exposure required for successful outcomes.^9,10 The proven risks of toxicity are the primary driver for the pediatric guideline change with the outcomes extrapolated from adult data.

Methods in Preparing Guideline

The main strength of the guideline is that the committee was represented by multiple organizations, which created a multidisciplinary panel of pharmacists and infectious disease physicians with clinical and research expertise in vancomycin dosing. Evidence was graded using an adaptation from the Canadian Task Force on the Periodic Health Examination.¹¹ The draft was peer-reviewed by the society organizations and allowed for comments, suggestions, and recommendations.

Sources of Potential Conflict of Interest or Bias

Disclosures of all authors were reported and identified in the guideline. While many members are involved with pharmaceutical companies through research or speakers’ roles, vancomycin, a generic drug, should have minimal conflicts of interest or bias from this involvement.

Generalizability

Implementation of vancomycin AUC dosing will be hospital dependent due to the implementation-related increase in human resources and the cost of clinical software; many hospital systems do not already have the software integrated into their clinical practice. Local guidelines will have to be developed to help clinicians determine which clinical situations require AUC-based dosing vs trough-only monitoring. Pharmacists at many hospitals are primarily responsible for vancomycin monitoring and provide dosing recommendations to physicians. Depending on a hospital system’s decision, the workload to determine the optimal vancomycin dose may increase, and it will be important to have close collaboration between hospitalists, pharmacists, and infectious diseases clinicians to appropriately educate clinicians who might be required to dose/monitor vancomycin. One potential way to decrease the burden of monitoring with two concentrations is to use specialized software that can perform complex assessments with only a single concentration. These software applications will still require serious collaboration of the aforementioned practitioners to implement. The variation in guideline adoption will likely be even more significant in pediatrics because the literature is extrapolated and the increased blood draws can be more problematic in pediatric patients.

Furthermore, clinicians should understand the dosing guideline is specifically addressing treatment of MRSA infections and extrapolation to other organisms such as coagulase-negative staphylococcal or methicillin-susceptible S aureus infections should be cautioned. Another caveat to note is that, when the MRSA isolate has an MIC of 2 mg/L or higher, these infections are associated with poor outcomes when vancomycin is used and alternative agents are recommended.

Research gaps still remain with appropriate vancomycin drug exposure. In pediatrics, determining the appropriate AUC target will be important given that current recommendations extrapolate from adult data. Future studies can focus on prospective outcome data in both pediatric and adult patients for infections outside of bacteremia or pneumonia, notably central nervous system and osteomyelitis infections. Thresholds for kidney injury will need to be more clearly defined for both adult and pediatric patients. There should also be research emphasis on the appropriate dosing for other non-MRSA invasive infections, notably coagulase-negative staphylococcal infections.

Dr Scheetz reported personal fees for consulting for Achaogen, SIGA technologies, and for serving on an advisory board for Paratek; grants from Merck and Co, Allecra, Nevakar, and SuperTrans Medical; personal fees from Hall, Booth, Smith, PC, and Chambless, Higdon, Richardson, Katz & Griggs, LLP, for consulting and expert testimony, outside the submitted work. In addition, Dr. Scheetz has patent US 2019 / 0099500 A1 pending. Dr Murphy reported having received fees from Becton Dickinson for participation to review IDSA guidelines on gastroenteritis. Dr Tang Girdwood has nothing to disclose.

Dr Murphy and Dr Tang Girdwood are supported by the National Institute of Child Health and Development Cincinnati Pediatric Clinical Pharmacology Postdoctoral Training Program (5T32HD069054-09). Dr Tang Girdwood is also supported by the Cincinnati Children’s Hospital Medical Center Arnold W Strauss Fellow Award and Cincinnati Children’s Hospital Medical Center Hospital Medicine Fellow Award. Dr Scheetz is supported in part by the National Institute of Allergy and Infectious Diseases award (R21AI149026). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Between February 1 and July 1, 2020, SARS-CoV-2 killed over 120,000 people in the United States alone. Nearly 80% of deaths occurred in those 65 years and older; by contrast, this age group constituted only 65% of deaths from influenza during the same time period.¹ Though the reasons for these differences have not been completely elucidated, one thing is abundantly clear: Our nation’s oldest and most frail have been among the most likely to die of COVID-19. With an estimated mortality rate of 4.7% in the United States, we are fortunate that most infected patients survive^2,3; however, many survivors require an exceptionally long hospital stay in isolation. Hospitalizations for patients with COVID-19 are distinct and confer a high risk for hospital-associated disability (HAD). HAD, defined as a new loss of ability to complete one or more activities of daily living (ADLs) without assistance after hospital discharge, occurs in approximately one-third of all hospitalized patients.⁴ In this perspective, we explore why HAD might be worse in patients with COVID-19 and offer new models for delivery of physical and occupational therapy to help them with functional recovery during and after hospitalization.

Functional decline, a life-altering condition that patients experience as part of posthospital syndrome,⁵ is characterized by loss of mobility, cognitive decline, and HAD. The effects of functional decline can lead to a cascade of readmissions, institutionalization, and even death. During hospitalization, patients spend 87% to 100% of their time in bed. This immobilization is a major contributor to the development of HAD.^6,7 The $58.5 billion dollars in yearly Medicare spending that is attributed to post–acute care also highlights the financial toll arising from such disability.⁸ Early mobilization with physical and occupational therapy is important to prevent HAD. However, even under normal conditions, care teams face innumerable barriers to mobilizing patients: symptomatic patients can be resistant to mobilizing during illness, providers have fears of worsening symptoms or falls, and some providers are unaware of the importance of mobilization. In patients with COVID-19, the barriers are only magnified.

Given the increasing numbers of COVID-19 survivors discharged from the hospital, it is critical to consider why HAD could be an even larger problem in these patients. Consider their age, symptom burden, and illness severity: Among 5,700 patients who were admitted for COVID-19 in the New York City area, most were elderly (median age, 63 years), many were tachypneic (17%), and many required supplemental oxygen (28%).⁹ Fourteen percent of these patients required care in the intensive care unit (ICU), most of whom required mechanical ventilation (86%), which independently places them at higher risk of HAD. Given these severe respiratory issues in COVID-19, mobilization may cause significant discomfort. Being symptomatic is, by far, the most common reason hospitalized patients refuse to ambulate.¹⁰ As a result, this could make early mobilization for these COVID-19 patients exceptionally difficult.

Patients with COVID-19 also experience prolonged hospitalization. The median hospital length of stay (LOS) is 9.3 days for survivors of SARS-CoV-2 infection compared with the 7-day average LOS for patients with pneumonia requiring ICU admission and 5-day average LOS for patients with influenza.^11-13 Complications of COVID, such as cardiac injury, critical illness polyneuropathy or myopathy, or cognitive impairment, also contribute to the significant need for rehabilitation long after recovery from the acute illness.¹⁴

Physical and occupational therapy involve prolonged close contact with patients, a known risk factor for contracting SARS-CoV-2.¹⁵ For staff, mobilizing a patient with COVID-19 takes longer due to intricate PPE donning and doffing procedures and patients requiring rest breaks because of weakness and respiratory-related recovery time. For patients who are mobilized, their activity is constrained by isolation restrictions that prohibit patients from leaving the confines of their hospital rooms. On March 23, 2020, the World Confederation for Physical Therapy (WCPT) endorsed guidelines created by the Australian Physiotherapy Association (APA) on caring for patients with COVID-19 acknowledging this risk¹⁶. The guidelines suggested that personal protective equipment (PPE) required for reducing risk of droplet transmission is appropriate for some scenarios, but they noted that exercising may induce coughing or expectoration, which could make physical therapy an aerosol-generating procedure. Therefore, the guidelines recommended that therapists wear N95 masks and recommend that direct face-to-face physical therapy should be limited to patients with certain functional limitations, including frailty, multiple comorbidities, and advanced age.

Patients with COVID-19 face additional barriers to accessing therapy services following hospital discharge. Post–acute care placement may be difficult due to limited availability of isolation rooms for patients with COVID-19 and the requirement of negative results for recovering patients. For those who manage to secure a bed, PPE shortages in nursing facilities could lead to lower prioritization of therapy interventions among staff and more bedridden days for the patients. Given social distancing restrictions, home health and outpatient therapy may not be possible for similar reasons.

The confluence of often highly symptomatic and even fragile patients, time-consuming visits with high concern for contagion, limited space to freely mobilize, and barriers to post–acute care illustrates why it is likely that COVID-19 admissions will be associated with a higher degree of HAD than admissions for other illnesses.

The entire healthcare system has had to evolve and innovate rapidly to combat the morbidity and mortality of COVID-19. In the case of HAD, nursing staff, new billing guidelines, hospital redesign, and telemedicine are all facilitating novel ways to mobilize patients during and after hospitalization.

To limit the numbers of staff exposed to patients with COVID-19, the APA recommends engaging nursing staff in initial therapy evaluations and simple exercises that can be performed in a hospital room. Meaningful in-room exercise for some patients may include getting out of bed and walking to the bathroom to brush their teeth or complete other ADLs. Assessment of cognition should be carefully considered for discharge planning given its effects on the patient’s ability to independently participate in exercises and ADLs. For this reason, treatment and prevention of delirium or cognitive changes with interventions targeting environmental modifications, maintenance of healthy sleep-wake cycles, and orientation strategies are vital.

Therapy evaluations can also be administered remotely via phone call or video. To help facilitate telehealth visits, the Centers for Medicaid & Medicare Services has released new guidelines under the Coronavirus Aid, Relief, and Economic Security (CARES) Act. Physical and occupational therapists have been historically excluded from the list of providers able to bill for telehealth services, but the CARES Act allows physical and occupational therapists who accept Medicare part B to bill for telehealth services and e-visits. The new rule applies to patients in healthcare facilities or patients at home.¹⁷ Transitioning some physical and occupational therapy to telehealth could prove to be a critical resource for patients with COVID-19 trying to regain strength and independence during and after hospitalization.

Other solutions include converting areas of a hospital into rehabilitation units solely for patients recovering from COVID-19. Alternatively, rural hospitals, which usually run below capacity, or certain post–acute care facilities that are already prepared to manage infectious patients could serve as dedicated COVID-19 rehabilitation facilities, which can offer novel ways to continue therapy services after discharge while decreasing new exposures to COVID-19.¹⁸

Given the social isolation patients with COVID-19 experience during hospitalization, virtual group exercise classes may help for overall recovery. Most therapy companies already offer this service, and several include an app that allows therapists to monitor the patient’s exercises and progress. However, when transitioning to telemedicine, it is also important to consider the needs of those who may not be able to navigate technology effectively. For example, some elderly patients can be limited by a range of issues from poor computer skills and “technophobia” to visual and cognitive impairments. Having a friend or family member available to assist with technology should be considered. Additionally, being elderly, having lower income, or having a lower level of education makes it less likely that a patient will have access to internet or smartphones. Therefore, patients with these limitations may be poor candidates for telehealth and require post–acute care for their therapy services.^19,20

With all the devastation that COVID-19 has created, it might be easy to forget the importance of physical and occupational therapy. But without this focus, the disability resulting from COVID-19 hospitalizations could inflict considerable long-lasting effects on our patients at great cost to an already strained healthcare system. Immediate changes in how we adapt and innovate these services for patients with COVID-19 are critical. It may prove to have enormous impact on patients and the healthcare system long after the worst of the virus is forgotten.

The authors reported having nothing to disclose.

Dr Arora is funded by National Heart, Lung and Blood Institute (NHLBI Grant K24HL136859).

Between February 1 and July 1, 2020, SARS-CoV-2 killed over 120,000 people in the United States alone. Nearly 80% of deaths occurred in those 65 years and older; by contrast, this age group constituted only 65% of deaths from influenza during the same time period.¹ Though the reasons for these differences have not been completely elucidated, one thing is abundantly clear: Our nation’s oldest and most frail have been among the most likely to die of COVID-19. With an estimated mortality rate of 4.7% in the United States, we are fortunate that most infected patients survive^2,3; however, many survivors require an exceptionally long hospital stay in isolation. Hospitalizations for patients with COVID-19 are distinct and confer a high risk for hospital-associated disability (HAD). HAD, defined as a new loss of ability to complete one or more activities of daily living (ADLs) without assistance after hospital discharge, occurs in approximately one-third of all hospitalized patients.⁴ In this perspective, we explore why HAD might be worse in patients with COVID-19 and offer new models for delivery of physical and occupational therapy to help them with functional recovery during and after hospitalization.

Functional decline, a life-altering condition that patients experience as part of posthospital syndrome,⁵ is characterized by loss of mobility, cognitive decline, and HAD. The effects of functional decline can lead to a cascade of readmissions, institutionalization, and even death. During hospitalization, patients spend 87% to 100% of their time in bed. This immobilization is a major contributor to the development of HAD.^6,7 The $58.5 billion dollars in yearly Medicare spending that is attributed to post–acute care also highlights the financial toll arising from such disability.⁸ Early mobilization with physical and occupational therapy is important to prevent HAD. However, even under normal conditions, care teams face innumerable barriers to mobilizing patients: symptomatic patients can be resistant to mobilizing during illness, providers have fears of worsening symptoms or falls, and some providers are unaware of the importance of mobilization. In patients with COVID-19, the barriers are only magnified.

Given the increasing numbers of COVID-19 survivors discharged from the hospital, it is critical to consider why HAD could be an even larger problem in these patients. Consider their age, symptom burden, and illness severity: Among 5,700 patients who were admitted for COVID-19 in the New York City area, most were elderly (median age, 63 years), many were tachypneic (17%), and many required supplemental oxygen (28%).⁹ Fourteen percent of these patients required care in the intensive care unit (ICU), most of whom required mechanical ventilation (86%), which independently places them at higher risk of HAD. Given these severe respiratory issues in COVID-19, mobilization may cause significant discomfort. Being symptomatic is, by far, the most common reason hospitalized patients refuse to ambulate.¹⁰ As a result, this could make early mobilization for these COVID-19 patients exceptionally difficult.

Patients with COVID-19 also experience prolonged hospitalization. The median hospital length of stay (LOS) is 9.3 days for survivors of SARS-CoV-2 infection compared with the 7-day average LOS for patients with pneumonia requiring ICU admission and 5-day average LOS for patients with influenza.^11-13 Complications of COVID, such as cardiac injury, critical illness polyneuropathy or myopathy, or cognitive impairment, also contribute to the significant need for rehabilitation long after recovery from the acute illness.¹⁴

Physical and occupational therapy involve prolonged close contact with patients, a known risk factor for contracting SARS-CoV-2.¹⁵ For staff, mobilizing a patient with COVID-19 takes longer due to intricate PPE donning and doffing procedures and patients requiring rest breaks because of weakness and respiratory-related recovery time. For patients who are mobilized, their activity is constrained by isolation restrictions that prohibit patients from leaving the confines of their hospital rooms. On March 23, 2020, the World Confederation for Physical Therapy (WCPT) endorsed guidelines created by the Australian Physiotherapy Association (APA) on caring for patients with COVID-19 acknowledging this risk¹⁶. The guidelines suggested that personal protective equipment (PPE) required for reducing risk of droplet transmission is appropriate for some scenarios, but they noted that exercising may induce coughing or expectoration, which could make physical therapy an aerosol-generating procedure. Therefore, the guidelines recommended that therapists wear N95 masks and recommend that direct face-to-face physical therapy should be limited to patients with certain functional limitations, including frailty, multiple comorbidities, and advanced age.

Patients with COVID-19 face additional barriers to accessing therapy services following hospital discharge. Post–acute care placement may be difficult due to limited availability of isolation rooms for patients with COVID-19 and the requirement of negative results for recovering patients. For those who manage to secure a bed, PPE shortages in nursing facilities could lead to lower prioritization of therapy interventions among staff and more bedridden days for the patients. Given social distancing restrictions, home health and outpatient therapy may not be possible for similar reasons.

The confluence of often highly symptomatic and even fragile patients, time-consuming visits with high concern for contagion, limited space to freely mobilize, and barriers to post–acute care illustrates why it is likely that COVID-19 admissions will be associated with a higher degree of HAD than admissions for other illnesses.

The entire healthcare system has had to evolve and innovate rapidly to combat the morbidity and mortality of COVID-19. In the case of HAD, nursing staff, new billing guidelines, hospital redesign, and telemedicine are all facilitating novel ways to mobilize patients during and after hospitalization.

To limit the numbers of staff exposed to patients with COVID-19, the APA recommends engaging nursing staff in initial therapy evaluations and simple exercises that can be performed in a hospital room. Meaningful in-room exercise for some patients may include getting out of bed and walking to the bathroom to brush their teeth or complete other ADLs. Assessment of cognition should be carefully considered for discharge planning given its effects on the patient’s ability to independently participate in exercises and ADLs. For this reason, treatment and prevention of delirium or cognitive changes with interventions targeting environmental modifications, maintenance of healthy sleep-wake cycles, and orientation strategies are vital.

Therapy evaluations can also be administered remotely via phone call or video. To help facilitate telehealth visits, the Centers for Medicaid & Medicare Services has released new guidelines under the Coronavirus Aid, Relief, and Economic Security (CARES) Act. Physical and occupational therapists have been historically excluded from the list of providers able to bill for telehealth services, but the CARES Act allows physical and occupational therapists who accept Medicare part B to bill for telehealth services and e-visits. The new rule applies to patients in healthcare facilities or patients at home.¹⁷ Transitioning some physical and occupational therapy to telehealth could prove to be a critical resource for patients with COVID-19 trying to regain strength and independence during and after hospitalization.

Other solutions include converting areas of a hospital into rehabilitation units solely for patients recovering from COVID-19. Alternatively, rural hospitals, which usually run below capacity, or certain post–acute care facilities that are already prepared to manage infectious patients could serve as dedicated COVID-19 rehabilitation facilities, which can offer novel ways to continue therapy services after discharge while decreasing new exposures to COVID-19.¹⁸

Given the social isolation patients with COVID-19 experience during hospitalization, virtual group exercise classes may help for overall recovery. Most therapy companies already offer this service, and several include an app that allows therapists to monitor the patient’s exercises and progress. However, when transitioning to telemedicine, it is also important to consider the needs of those who may not be able to navigate technology effectively. For example, some elderly patients can be limited by a range of issues from poor computer skills and “technophobia” to visual and cognitive impairments. Having a friend or family member available to assist with technology should be considered. Additionally, being elderly, having lower income, or having a lower level of education makes it less likely that a patient will have access to internet or smartphones. Therefore, patients with these limitations may be poor candidates for telehealth and require post–acute care for their therapy services.^19,20

With all the devastation that COVID-19 has created, it might be easy to forget the importance of physical and occupational therapy. But without this focus, the disability resulting from COVID-19 hospitalizations could inflict considerable long-lasting effects on our patients at great cost to an already strained healthcare system. Immediate changes in how we adapt and innovate these services for patients with COVID-19 are critical. It may prove to have enormous impact on patients and the healthcare system long after the worst of the virus is forgotten.

The authors reported having nothing to disclose.

Dr Arora is funded by National Heart, Lung and Blood Institute (NHLBI Grant K24HL136859).

Between February 1 and July 1, 2020, SARS-CoV-2 killed over 120,000 people in the United States alone. Nearly 80% of deaths occurred in those 65 years and older; by contrast, this age group constituted only 65% of deaths from influenza during the same time period.¹ Though the reasons for these differences have not been completely elucidated, one thing is abundantly clear: Our nation’s oldest and most frail have been among the most likely to die of COVID-19. With an estimated mortality rate of 4.7% in the United States, we are fortunate that most infected patients survive^2,3; however, many survivors require an exceptionally long hospital stay in isolation. Hospitalizations for patients with COVID-19 are distinct and confer a high risk for hospital-associated disability (HAD). HAD, defined as a new loss of ability to complete one or more activities of daily living (ADLs) without assistance after hospital discharge, occurs in approximately one-third of all hospitalized patients.⁴ In this perspective, we explore why HAD might be worse in patients with COVID-19 and offer new models for delivery of physical and occupational therapy to help them with functional recovery during and after hospitalization.

Functional decline, a life-altering condition that patients experience as part of posthospital syndrome,⁵ is characterized by loss of mobility, cognitive decline, and HAD. The effects of functional decline can lead to a cascade of readmissions, institutionalization, and even death. During hospitalization, patients spend 87% to 100% of their time in bed. This immobilization is a major contributor to the development of HAD.^6,7 The $58.5 billion dollars in yearly Medicare spending that is attributed to post–acute care also highlights the financial toll arising from such disability.⁸ Early mobilization with physical and occupational therapy is important to prevent HAD. However, even under normal conditions, care teams face innumerable barriers to mobilizing patients: symptomatic patients can be resistant to mobilizing during illness, providers have fears of worsening symptoms or falls, and some providers are unaware of the importance of mobilization. In patients with COVID-19, the barriers are only magnified.

Given the increasing numbers of COVID-19 survivors discharged from the hospital, it is critical to consider why HAD could be an even larger problem in these patients. Consider their age, symptom burden, and illness severity: Among 5,700 patients who were admitted for COVID-19 in the New York City area, most were elderly (median age, 63 years), many were tachypneic (17%), and many required supplemental oxygen (28%).⁹ Fourteen percent of these patients required care in the intensive care unit (ICU), most of whom required mechanical ventilation (86%), which independently places them at higher risk of HAD. Given these severe respiratory issues in COVID-19, mobilization may cause significant discomfort. Being symptomatic is, by far, the most common reason hospitalized patients refuse to ambulate.¹⁰ As a result, this could make early mobilization for these COVID-19 patients exceptionally difficult.

Patients with COVID-19 also experience prolonged hospitalization. The median hospital length of stay (LOS) is 9.3 days for survivors of SARS-CoV-2 infection compared with the 7-day average LOS for patients with pneumonia requiring ICU admission and 5-day average LOS for patients with influenza.^11-13 Complications of COVID, such as cardiac injury, critical illness polyneuropathy or myopathy, or cognitive impairment, also contribute to the significant need for rehabilitation long after recovery from the acute illness.¹⁴

Physical and occupational therapy involve prolonged close contact with patients, a known risk factor for contracting SARS-CoV-2.¹⁵ For staff, mobilizing a patient with COVID-19 takes longer due to intricate PPE donning and doffing procedures and patients requiring rest breaks because of weakness and respiratory-related recovery time. For patients who are mobilized, their activity is constrained by isolation restrictions that prohibit patients from leaving the confines of their hospital rooms. On March 23, 2020, the World Confederation for Physical Therapy (WCPT) endorsed guidelines created by the Australian Physiotherapy Association (APA) on caring for patients with COVID-19 acknowledging this risk¹⁶. The guidelines suggested that personal protective equipment (PPE) required for reducing risk of droplet transmission is appropriate for some scenarios, but they noted that exercising may induce coughing or expectoration, which could make physical therapy an aerosol-generating procedure. Therefore, the guidelines recommended that therapists wear N95 masks and recommend that direct face-to-face physical therapy should be limited to patients with certain functional limitations, including frailty, multiple comorbidities, and advanced age.

Patients with COVID-19 face additional barriers to accessing therapy services following hospital discharge. Post–acute care placement may be difficult due to limited availability of isolation rooms for patients with COVID-19 and the requirement of negative results for recovering patients. For those who manage to secure a bed, PPE shortages in nursing facilities could lead to lower prioritization of therapy interventions among staff and more bedridden days for the patients. Given social distancing restrictions, home health and outpatient therapy may not be possible for similar reasons.

The confluence of often highly symptomatic and even fragile patients, time-consuming visits with high concern for contagion, limited space to freely mobilize, and barriers to post–acute care illustrates why it is likely that COVID-19 admissions will be associated with a higher degree of HAD than admissions for other illnesses.

The entire healthcare system has had to evolve and innovate rapidly to combat the morbidity and mortality of COVID-19. In the case of HAD, nursing staff, new billing guidelines, hospital redesign, and telemedicine are all facilitating novel ways to mobilize patients during and after hospitalization.

To limit the numbers of staff exposed to patients with COVID-19, the APA recommends engaging nursing staff in initial therapy evaluations and simple exercises that can be performed in a hospital room. Meaningful in-room exercise for some patients may include getting out of bed and walking to the bathroom to brush their teeth or complete other ADLs. Assessment of cognition should be carefully considered for discharge planning given its effects on the patient’s ability to independently participate in exercises and ADLs. For this reason, treatment and prevention of delirium or cognitive changes with interventions targeting environmental modifications, maintenance of healthy sleep-wake cycles, and orientation strategies are vital.

Therapy evaluations can also be administered remotely via phone call or video. To help facilitate telehealth visits, the Centers for Medicaid & Medicare Services has released new guidelines under the Coronavirus Aid, Relief, and Economic Security (CARES) Act. Physical and occupational therapists have been historically excluded from the list of providers able to bill for telehealth services, but the CARES Act allows physical and occupational therapists who accept Medicare part B to bill for telehealth services and e-visits. The new rule applies to patients in healthcare facilities or patients at home.¹⁷ Transitioning some physical and occupational therapy to telehealth could prove to be a critical resource for patients with COVID-19 trying to regain strength and independence during and after hospitalization.

Other solutions include converting areas of a hospital into rehabilitation units solely for patients recovering from COVID-19. Alternatively, rural hospitals, which usually run below capacity, or certain post–acute care facilities that are already prepared to manage infectious patients could serve as dedicated COVID-19 rehabilitation facilities, which can offer novel ways to continue therapy services after discharge while decreasing new exposures to COVID-19.¹⁸

Given the social isolation patients with COVID-19 experience during hospitalization, virtual group exercise classes may help for overall recovery. Most therapy companies already offer this service, and several include an app that allows therapists to monitor the patient’s exercises and progress. However, when transitioning to telemedicine, it is also important to consider the needs of those who may not be able to navigate technology effectively. For example, some elderly patients can be limited by a range of issues from poor computer skills and “technophobia” to visual and cognitive impairments. Having a friend or family member available to assist with technology should be considered. Additionally, being elderly, having lower income, or having a lower level of education makes it less likely that a patient will have access to internet or smartphones. Therefore, patients with these limitations may be poor candidates for telehealth and require post–acute care for their therapy services.^19,20

With all the devastation that COVID-19 has created, it might be easy to forget the importance of physical and occupational therapy. But without this focus, the disability resulting from COVID-19 hospitalizations could inflict considerable long-lasting effects on our patients at great cost to an already strained healthcare system. Immediate changes in how we adapt and innovate these services for patients with COVID-19 are critical. It may prove to have enormous impact on patients and the healthcare system long after the worst of the virus is forgotten.

The authors reported having nothing to disclose.

Dr Arora is funded by National Heart, Lung and Blood Institute (NHLBI Grant K24HL136859).

On March 11, 2020, the novel coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization.¹ On March 13, 2020, a national emergency was declared in the United States concerning the COVID-19 outbreak.² Later that week, Mike Kueper, a 52-year-old previously healthy man and resident of the Indianapolis metropolitan area, became sick with what he would eventually learn was COVID-19. Prior to contracting the novel coronavirus, he had never had as much as an Emergency Department (ED) visit. He had never spent a night in a hospital. He and his sister, DeAnn Harvey, describe the events that followed.

DeAnn

As a 20-year veteran clinical child psychologist and mother of two teenagers, my first reaction to the governor’s call for state-wide lockdowns was that they sounded like an opportunity for time at home with my husband and children. I thought we would play games, watch movies, try new recipes, and get a much-needed reprieve from our hectic lives of sports schedules, homework, and social outings. Even a slowdown in my practice sounded good. Maybe I could finally finish those continuing education credits that were due for my upcoming license renewal. My greatest concerns about sheltering in place were about how I was going to structure my children’s online learning while at the same time getting into my office to manage my patients via telehealth. Unfortunately, this relaxed feeling was short-lived.

On March 20, 2020, a few days after the lockdown started, my brother Mike developed high fevers. During a virtual doctor visit, he was told that it could be COVID-19 and to self-quarantine. Our discussions turned to jokes about his lack of taste or smell. We had dropped off soup for him from a new recipe my daughter had tried. My son joked that Mike was lucky that he couldn’t taste it.

On the morning of March 28, my mother called to tell me that Mike needed to go to the ED. Because we needed to figure out which hospital would be the best for him and I didn’t want my children to worry too much, I jumped in my car and drove to our church parking lot. In between calls to area hospitals, I began praying for his health and for guidance and support from God. Mike, concerned about spreading the virus to the rest of the family, refused to let my parents or me drive him to the hospital.

Mike

I thought I had a regular cold, and then, once I had a temperature of 102 °F and night sweats, decided it was the flu. One night, I was so cold that I went to bed wearing winter gloves. After a virtual visit with a nurse, she said my symptoms did not sound like COVID, but recommended self-quarantine, just in case. On March 26, I noticed that my sense of taste and smell had disappeared completely, and it hurt to yawn or take deep breaths. By Saturday, March 28, I was getting sicker and was short of breath and very tired. My elderly parents wanted to drive me to the ED, but if it was COVID-19, I didn’t want them near me. After getting advice from my sister, I called a local hospital and asked if I could come into the ED. The person on the phone said if I got there within an hour, they would be able to take me. When I arrived, an aide came out to my car, put me in a protective gown and mask, and walked me in. Walking even this short distance was tiring, and from this moment, things get fuzzy. I only have glimpses of the next few days. At first, I was put into a negative pressure room. I spent the night in there. I remember talking to a doctor who asked if I had a living will. He recommended that I go on a ventilator. I asked him, “Do you expect me to die?”

I remember him saying, “That is always a possibility.”

DeAnn

Once Mike was admitted to the hospital, we didn’t hear from anyone for about 6 hours, and I started to panic. I called people I knew who worked in the hospital, and my friend who is an intensive care unit (ICU) nurse agreed to track him down. He was indeed admitted to the hospital and was receiving oxygen. When I finally got to talk to him later that night, Mike had difficulty completing sentences because he was so short of breath. I told him not to use his energy, and that if they would let me, I would be there by his side. I promised him that he was going to get through this. Around 1:30 AM, I texted him goodnight. He immediately responded, “They are transferring me to the ICU.” That was the last contact I would have with him for the next 17 days.

Mike

I don’t remember much from the ICU, but I understand that it was touch and go at times. I knew I was on a ventilator, and I found out later that I was “proned’ for up to 16 hours. Being on the ventilator was horrible, but what was even worse was that, once I was off the vent and alone in my hospital room, I had no idea how I got there. I thought I had been in a plane crash. I wanted to check my phone to see where I was flying in from but couldn’t because I thought my phone had been hacked by terrorists. I had no idea what was real and what was not. It was extremely scary.

DeAnn

When I think about the doctor coming in to tell Mike they had to put him on the ventilator, my heart absolutely breaks. It hurts to think of him all alone, having to make this decision without any of his family there to support him. Neither he nor I wanted to think about it, but we knew there was the possibility that he would never come off the ventilator. We hadn’t had a chance to hug him or even see him for days before his admission. If he didn’t make it, we would never get one of his amazing “Uncle Mike” hugs again.

Our friend, the ICU nurse, made it a point to find out which nurse was assigned to Mike and made it a priority to gather information from that nurse daily, allowing our family to receive updates on Mike’s status 2-3 times a day. In addition, the ICU physician was in daily contact with my parents: however, it was still excruciating not being able to be there. I spent a lot of time pacing the house, not eating or sleeping, checking my phone for texts, fielding texts and calls from friends and family. I was unable to do even simple household tasks, and left laundry, cooking, and my kids’ online schooling to my husband.

Feeling so helpless, I turned to prayer. My close friends organized a daily prayer vigil at 7:30 PM each night in the church parking lot and my family and I did not miss a night until Mike came home from the hospital. It brought me comfort to have that connection each night. I also began, along with one of Mike’s closest friends, organizing a meal train for the doctors and nurses in the ICU during both the day and night shifts. Interacting with those who were caring for him helped us feel closer to Mike. Because of the meal train, I became acquainted with two of his regular nurses, and they set up video chats on two occasions. Seeing him on the ventilator, so vulnerable, so ill, inspired the most difficult mix of emotions I have ever experienced. I was trying to just say anything that he would understand, hoping that he was hearing me. I told him, “We all miss you and love you. You are the strongest person we know and because of that, you will beat this!” I wish I felt as confident as I sounded.

Then, after 17 days, a miracle: he was taken off the ventilator and moved to the medical unit. Looking back, I think these are really the days that the presence of his family would have sped up his recovery. Mike was experiencing delirium and hallucinations as a result of illness, medications, and the time he spent in an induced coma. I wish I could have been there with him to be the one he asked if what he was experiencing was real or a hallucination. Then we could have laughed about it together; our family has always found that humor helps with healing.

Mike

I understand the purpose of the isolation, but it really did a number on my mind. I remember being in the ICU, having my catheter taken out, not knowing what was happening or how I ended up in the hospital. I was so confused and was seeing people who were not there. One morning, I woke up thinking I was in my house and I had stolen the hospital bed I was in. I was panicking and scheming about how I might get this hospital bed back to the hospital before anyone noticed. As I mentioned earlier, I thought I was on a plane that had been taken over by terrorists who were using us COVID patients as biological weapons. Then I thought agents of the Federal Bureau of Investigation were coming to interrogate me and that they were also looking for my sister. To protect DeAnn, the next time someone asked me the name of my sister, I told them, “Maria” (the name of my sister who passed away in 1991) rather than DeAnn, who is very much alive. Another time, I thought my grandmother and cousin had died in a plane crash. My cousin is a state representative in Illinois, so once I got my phone to work, I checked his Wikipedia page to see if there was a death date listed.

When I was less confused, I found it’s tough spending day after day lying in a hospital bed with no family member or friend to offer companionship, comfort, or clarity. Even though I was extremely weak and could barely walk, I was asking, daily, about when I was expected to be discharged. I had to get out.

One night, our friend the ICU nurse came into my room to sit with me and just talk. She spent about 30 minutes with me around 4:00 in the morning. It was wonderful. All I could think about was what a huge blessing it was. I don’t think she knows just how much that meant to me. More often, I would report some symptom of confusion or insomnia, and a nurse would offer me medications for sleep or pain. I did not want any more drugs in my system. Human contact would have been a far better treatment.

I was reluctant to ask for help when I needed something, like a trip to the bathroom or some ice water. When I did press the call button, I had to wait for the busy nurse or tech to put on all the protective gear, and then, when they left, watch them take all the steps to disinfect and rid themselves of the gear that they had just put on. Even so, I was excited when it came time to take my vital signs or administer medications because that meant human interaction, however brief (and even if it was 4:00 AM). I wanted to bathe or change gowns and/or socks, but I opted to wear the same gown and socks for over three-fourths of the time I was there because I did not want to burden the staff.

Video chat turned out to be one of the best tools for creating connection. I may have sobbed a few times when talking to my parents on FaceTime, but just seeing their faces made all the difference in the world.

Finally, on April 21, 25 days after I was led into the ED, I was discharged. As we reflect on this experience, my family and I have some recommendations for hospitals and health systems trying to make patient experience a priority during this pandemic:

Kueper Family Recommendations to Improve Patient Experience for Those With COVID-19

• Adopt a more systematic approach to communicating with patient families, which would greatly improve the connection between them and healthcare personnel. This is especially important for families when the patient is critically ill, and especially in times when the patient is in isolation. We were fortunate in that we received updates from nurses and physicians several times a day. This was partly due to the relationships or connections with staff members that existed previously or developed over the course of Mike’s stay. Staff members who became invested in Mike’s progress became part of his hospital “family.” Many people who have had a family member with COVID have not had this experience, nor did they have the opportunity to build relationships with the staff, which we felt were important to ensure good care and open and frequent communication with them and the patient. Therefore, we believe a more systematic approach toward communication (eg, “the team will call each day during multidisciplinary rounds at 11 AM,”) would greatly improve the connection between families and healthcare personnel.
• Allow visitation under certain conditions even while the patient is in isolation. Visitation would have been especially helpful once Mike was more awake but isolated and delirious. We know that these policies are difficult to create and navigate but believe that there should be allowance for some visitation when there is a clear clinical benefit (eg, delirium). Because Mike had little human contact the week after he was taken off the ventilator (eg, contact limited to nurses coming in to take vitals, once daily doctor visit), he had to navigate the hallucinations and delirium on his own. Even one family member by his side who could provide frequent feedback on reality would have helped to resolve the feelings of agitation and fear that can accompany delirium.
• Schedule more video chats. Even when Mike was on the ventilator, we found video chats to be an important way to understand his experience and connect with him. Although we know such chats are difficult for clinicians to schedule, it greatly improved the experience for us.
• Reassure patients that caring for them is not a burden and they should not hesitate to ask for help. Being contagious and believing you are a danger to others is a terrible feeling. No one on staff said or even implied that they were afraid to care for him, but Mike felt “dangerous” to the staff and as such hesitated to “burden” the clinicians with requests (eg, going to the bathroom, having a change of clothes). It is time-consuming and difficult to don PPE and the amount of effort it takes to enter the room is immediately obvious to the patient. Because of this, it is very important that the clinicians and staff reassure patients that it is part of the job and not a burden to come in and out of the room.

DeAnn

Having Mike alive and now home is an incredible gift. We are taking every chance we can to make up for the time that we could not see him and are so grateful for the hospital team that saved his life.

Mike

On April 21, I was discharged and sent home. Luckily, for about 2 weeks, I had a best friend, my brother, and DeAnn, separately, stay with me each night. This was a godsend as all made sure I was taking my medicine, eating, and doing my prescribed exercises. I am struggling with a long recovery. I used a walker for a while and had both a physical and occupational therapist visit me two to three times a week. I visit a neurologist for some of my symptoms that have not resolved, such as pain and atrophy in my right shoulder, hand tremors, and some numbness in my thighs. Thankfully, I was able to resume working from home, but even going up stairs causes me to become winded. I know that doctors don’t understand this disease very well, and neither do I. Sometimes I feel discouraged about how much it set me back physically. I wish things could have been different—that I could have avoided this disease altogether or had milder symptoms. But I am so grateful to be alive and so thankful for the doctors and nurses, as well as for my family, who could not be there physically during the hospitalization but did everything they could do to help me. Because of their love and support, I survived.

The authors have nothing to disclose.

On March 11, 2020, the novel coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization.¹ On March 13, 2020, a national emergency was declared in the United States concerning the COVID-19 outbreak.² Later that week, Mike Kueper, a 52-year-old previously healthy man and resident of the Indianapolis metropolitan area, became sick with what he would eventually learn was COVID-19. Prior to contracting the novel coronavirus, he had never had as much as an Emergency Department (ED) visit. He had never spent a night in a hospital. He and his sister, DeAnn Harvey, describe the events that followed.

DeAnn

As a 20-year veteran clinical child psychologist and mother of two teenagers, my first reaction to the governor’s call for state-wide lockdowns was that they sounded like an opportunity for time at home with my husband and children. I thought we would play games, watch movies, try new recipes, and get a much-needed reprieve from our hectic lives of sports schedules, homework, and social outings. Even a slowdown in my practice sounded good. Maybe I could finally finish those continuing education credits that were due for my upcoming license renewal. My greatest concerns about sheltering in place were about how I was going to structure my children’s online learning while at the same time getting into my office to manage my patients via telehealth. Unfortunately, this relaxed feeling was short-lived.

On March 20, 2020, a few days after the lockdown started, my brother Mike developed high fevers. During a virtual doctor visit, he was told that it could be COVID-19 and to self-quarantine. Our discussions turned to jokes about his lack of taste or smell. We had dropped off soup for him from a new recipe my daughter had tried. My son joked that Mike was lucky that he couldn’t taste it.

On the morning of March 28, my mother called to tell me that Mike needed to go to the ED. Because we needed to figure out which hospital would be the best for him and I didn’t want my children to worry too much, I jumped in my car and drove to our church parking lot. In between calls to area hospitals, I began praying for his health and for guidance and support from God. Mike, concerned about spreading the virus to the rest of the family, refused to let my parents or me drive him to the hospital.

Mike

I thought I had a regular cold, and then, once I had a temperature of 102 °F and night sweats, decided it was the flu. One night, I was so cold that I went to bed wearing winter gloves. After a virtual visit with a nurse, she said my symptoms did not sound like COVID, but recommended self-quarantine, just in case. On March 26, I noticed that my sense of taste and smell had disappeared completely, and it hurt to yawn or take deep breaths. By Saturday, March 28, I was getting sicker and was short of breath and very tired. My elderly parents wanted to drive me to the ED, but if it was COVID-19, I didn’t want them near me. After getting advice from my sister, I called a local hospital and asked if I could come into the ED. The person on the phone said if I got there within an hour, they would be able to take me. When I arrived, an aide came out to my car, put me in a protective gown and mask, and walked me in. Walking even this short distance was tiring, and from this moment, things get fuzzy. I only have glimpses of the next few days. At first, I was put into a negative pressure room. I spent the night in there. I remember talking to a doctor who asked if I had a living will. He recommended that I go on a ventilator. I asked him, “Do you expect me to die?”

I remember him saying, “That is always a possibility.”

DeAnn

Once Mike was admitted to the hospital, we didn’t hear from anyone for about 6 hours, and I started to panic. I called people I knew who worked in the hospital, and my friend who is an intensive care unit (ICU) nurse agreed to track him down. He was indeed admitted to the hospital and was receiving oxygen. When I finally got to talk to him later that night, Mike had difficulty completing sentences because he was so short of breath. I told him not to use his energy, and that if they would let me, I would be there by his side. I promised him that he was going to get through this. Around 1:30 AM, I texted him goodnight. He immediately responded, “They are transferring me to the ICU.” That was the last contact I would have with him for the next 17 days.

Mike

I don’t remember much from the ICU, but I understand that it was touch and go at times. I knew I was on a ventilator, and I found out later that I was “proned’ for up to 16 hours. Being on the ventilator was horrible, but what was even worse was that, once I was off the vent and alone in my hospital room, I had no idea how I got there. I thought I had been in a plane crash. I wanted to check my phone to see where I was flying in from but couldn’t because I thought my phone had been hacked by terrorists. I had no idea what was real and what was not. It was extremely scary.

DeAnn

When I think about the doctor coming in to tell Mike they had to put him on the ventilator, my heart absolutely breaks. It hurts to think of him all alone, having to make this decision without any of his family there to support him. Neither he nor I wanted to think about it, but we knew there was the possibility that he would never come off the ventilator. We hadn’t had a chance to hug him or even see him for days before his admission. If he didn’t make it, we would never get one of his amazing “Uncle Mike” hugs again.

Our friend, the ICU nurse, made it a point to find out which nurse was assigned to Mike and made it a priority to gather information from that nurse daily, allowing our family to receive updates on Mike’s status 2-3 times a day. In addition, the ICU physician was in daily contact with my parents: however, it was still excruciating not being able to be there. I spent a lot of time pacing the house, not eating or sleeping, checking my phone for texts, fielding texts and calls from friends and family. I was unable to do even simple household tasks, and left laundry, cooking, and my kids’ online schooling to my husband.

Feeling so helpless, I turned to prayer. My close friends organized a daily prayer vigil at 7:30 PM each night in the church parking lot and my family and I did not miss a night until Mike came home from the hospital. It brought me comfort to have that connection each night. I also began, along with one of Mike’s closest friends, organizing a meal train for the doctors and nurses in the ICU during both the day and night shifts. Interacting with those who were caring for him helped us feel closer to Mike. Because of the meal train, I became acquainted with two of his regular nurses, and they set up video chats on two occasions. Seeing him on the ventilator, so vulnerable, so ill, inspired the most difficult mix of emotions I have ever experienced. I was trying to just say anything that he would understand, hoping that he was hearing me. I told him, “We all miss you and love you. You are the strongest person we know and because of that, you will beat this!” I wish I felt as confident as I sounded.

Then, after 17 days, a miracle: he was taken off the ventilator and moved to the medical unit. Looking back, I think these are really the days that the presence of his family would have sped up his recovery. Mike was experiencing delirium and hallucinations as a result of illness, medications, and the time he spent in an induced coma. I wish I could have been there with him to be the one he asked if what he was experiencing was real or a hallucination. Then we could have laughed about it together; our family has always found that humor helps with healing.

Mike

I understand the purpose of the isolation, but it really did a number on my mind. I remember being in the ICU, having my catheter taken out, not knowing what was happening or how I ended up in the hospital. I was so confused and was seeing people who were not there. One morning, I woke up thinking I was in my house and I had stolen the hospital bed I was in. I was panicking and scheming about how I might get this hospital bed back to the hospital before anyone noticed. As I mentioned earlier, I thought I was on a plane that had been taken over by terrorists who were using us COVID patients as biological weapons. Then I thought agents of the Federal Bureau of Investigation were coming to interrogate me and that they were also looking for my sister. To protect DeAnn, the next time someone asked me the name of my sister, I told them, “Maria” (the name of my sister who passed away in 1991) rather than DeAnn, who is very much alive. Another time, I thought my grandmother and cousin had died in a plane crash. My cousin is a state representative in Illinois, so once I got my phone to work, I checked his Wikipedia page to see if there was a death date listed.

When I was less confused, I found it’s tough spending day after day lying in a hospital bed with no family member or friend to offer companionship, comfort, or clarity. Even though I was extremely weak and could barely walk, I was asking, daily, about when I was expected to be discharged. I had to get out.

One night, our friend the ICU nurse came into my room to sit with me and just talk. She spent about 30 minutes with me around 4:00 in the morning. It was wonderful. All I could think about was what a huge blessing it was. I don’t think she knows just how much that meant to me. More often, I would report some symptom of confusion or insomnia, and a nurse would offer me medications for sleep or pain. I did not want any more drugs in my system. Human contact would have been a far better treatment.

I was reluctant to ask for help when I needed something, like a trip to the bathroom or some ice water. When I did press the call button, I had to wait for the busy nurse or tech to put on all the protective gear, and then, when they left, watch them take all the steps to disinfect and rid themselves of the gear that they had just put on. Even so, I was excited when it came time to take my vital signs or administer medications because that meant human interaction, however brief (and even if it was 4:00 AM). I wanted to bathe or change gowns and/or socks, but I opted to wear the same gown and socks for over three-fourths of the time I was there because I did not want to burden the staff.

Video chat turned out to be one of the best tools for creating connection. I may have sobbed a few times when talking to my parents on FaceTime, but just seeing their faces made all the difference in the world.

Finally, on April 21, 25 days after I was led into the ED, I was discharged. As we reflect on this experience, my family and I have some recommendations for hospitals and health systems trying to make patient experience a priority during this pandemic:

Kueper Family Recommendations to Improve Patient Experience for Those With COVID-19

• Adopt a more systematic approach to communicating with patient families, which would greatly improve the connection between them and healthcare personnel. This is especially important for families when the patient is critically ill, and especially in times when the patient is in isolation. We were fortunate in that we received updates from nurses and physicians several times a day. This was partly due to the relationships or connections with staff members that existed previously or developed over the course of Mike’s stay. Staff members who became invested in Mike’s progress became part of his hospital “family.” Many people who have had a family member with COVID have not had this experience, nor did they have the opportunity to build relationships with the staff, which we felt were important to ensure good care and open and frequent communication with them and the patient. Therefore, we believe a more systematic approach toward communication (eg, “the team will call each day during multidisciplinary rounds at 11 AM,”) would greatly improve the connection between families and healthcare personnel.
• Allow visitation under certain conditions even while the patient is in isolation. Visitation would have been especially helpful once Mike was more awake but isolated and delirious. We know that these policies are difficult to create and navigate but believe that there should be allowance for some visitation when there is a clear clinical benefit (eg, delirium). Because Mike had little human contact the week after he was taken off the ventilator (eg, contact limited to nurses coming in to take vitals, once daily doctor visit), he had to navigate the hallucinations and delirium on his own. Even one family member by his side who could provide frequent feedback on reality would have helped to resolve the feelings of agitation and fear that can accompany delirium.
• Schedule more video chats. Even when Mike was on the ventilator, we found video chats to be an important way to understand his experience and connect with him. Although we know such chats are difficult for clinicians to schedule, it greatly improved the experience for us.
• Reassure patients that caring for them is not a burden and they should not hesitate to ask for help. Being contagious and believing you are a danger to others is a terrible feeling. No one on staff said or even implied that they were afraid to care for him, but Mike felt “dangerous” to the staff and as such hesitated to “burden” the clinicians with requests (eg, going to the bathroom, having a change of clothes). It is time-consuming and difficult to don PPE and the amount of effort it takes to enter the room is immediately obvious to the patient. Because of this, it is very important that the clinicians and staff reassure patients that it is part of the job and not a burden to come in and out of the room.

DeAnn

Having Mike alive and now home is an incredible gift. We are taking every chance we can to make up for the time that we could not see him and are so grateful for the hospital team that saved his life.

Mike

On April 21, I was discharged and sent home. Luckily, for about 2 weeks, I had a best friend, my brother, and DeAnn, separately, stay with me each night. This was a godsend as all made sure I was taking my medicine, eating, and doing my prescribed exercises. I am struggling with a long recovery. I used a walker for a while and had both a physical and occupational therapist visit me two to three times a week. I visit a neurologist for some of my symptoms that have not resolved, such as pain and atrophy in my right shoulder, hand tremors, and some numbness in my thighs. Thankfully, I was able to resume working from home, but even going up stairs causes me to become winded. I know that doctors don’t understand this disease very well, and neither do I. Sometimes I feel discouraged about how much it set me back physically. I wish things could have been different—that I could have avoided this disease altogether or had milder symptoms. But I am so grateful to be alive and so thankful for the doctors and nurses, as well as for my family, who could not be there physically during the hospitalization but did everything they could do to help me. Because of their love and support, I survived.

The authors have nothing to disclose.

On March 11, 2020, the novel coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization.¹ On March 13, 2020, a national emergency was declared in the United States concerning the COVID-19 outbreak.² Later that week, Mike Kueper, a 52-year-old previously healthy man and resident of the Indianapolis metropolitan area, became sick with what he would eventually learn was COVID-19. Prior to contracting the novel coronavirus, he had never had as much as an Emergency Department (ED) visit. He had never spent a night in a hospital. He and his sister, DeAnn Harvey, describe the events that followed.

DeAnn

As a 20-year veteran clinical child psychologist and mother of two teenagers, my first reaction to the governor’s call for state-wide lockdowns was that they sounded like an opportunity for time at home with my husband and children. I thought we would play games, watch movies, try new recipes, and get a much-needed reprieve from our hectic lives of sports schedules, homework, and social outings. Even a slowdown in my practice sounded good. Maybe I could finally finish those continuing education credits that were due for my upcoming license renewal. My greatest concerns about sheltering in place were about how I was going to structure my children’s online learning while at the same time getting into my office to manage my patients via telehealth. Unfortunately, this relaxed feeling was short-lived.

On March 20, 2020, a few days after the lockdown started, my brother Mike developed high fevers. During a virtual doctor visit, he was told that it could be COVID-19 and to self-quarantine. Our discussions turned to jokes about his lack of taste or smell. We had dropped off soup for him from a new recipe my daughter had tried. My son joked that Mike was lucky that he couldn’t taste it.

On the morning of March 28, my mother called to tell me that Mike needed to go to the ED. Because we needed to figure out which hospital would be the best for him and I didn’t want my children to worry too much, I jumped in my car and drove to our church parking lot. In between calls to area hospitals, I began praying for his health and for guidance and support from God. Mike, concerned about spreading the virus to the rest of the family, refused to let my parents or me drive him to the hospital.

Mike

I thought I had a regular cold, and then, once I had a temperature of 102 °F and night sweats, decided it was the flu. One night, I was so cold that I went to bed wearing winter gloves. After a virtual visit with a nurse, she said my symptoms did not sound like COVID, but recommended self-quarantine, just in case. On March 26, I noticed that my sense of taste and smell had disappeared completely, and it hurt to yawn or take deep breaths. By Saturday, March 28, I was getting sicker and was short of breath and very tired. My elderly parents wanted to drive me to the ED, but if it was COVID-19, I didn’t want them near me. After getting advice from my sister, I called a local hospital and asked if I could come into the ED. The person on the phone said if I got there within an hour, they would be able to take me. When I arrived, an aide came out to my car, put me in a protective gown and mask, and walked me in. Walking even this short distance was tiring, and from this moment, things get fuzzy. I only have glimpses of the next few days. At first, I was put into a negative pressure room. I spent the night in there. I remember talking to a doctor who asked if I had a living will. He recommended that I go on a ventilator. I asked him, “Do you expect me to die?”

I remember him saying, “That is always a possibility.”

DeAnn

Once Mike was admitted to the hospital, we didn’t hear from anyone for about 6 hours, and I started to panic. I called people I knew who worked in the hospital, and my friend who is an intensive care unit (ICU) nurse agreed to track him down. He was indeed admitted to the hospital and was receiving oxygen. When I finally got to talk to him later that night, Mike had difficulty completing sentences because he was so short of breath. I told him not to use his energy, and that if they would let me, I would be there by his side. I promised him that he was going to get through this. Around 1:30 AM, I texted him goodnight. He immediately responded, “They are transferring me to the ICU.” That was the last contact I would have with him for the next 17 days.

Mike

I don’t remember much from the ICU, but I understand that it was touch and go at times. I knew I was on a ventilator, and I found out later that I was “proned’ for up to 16 hours. Being on the ventilator was horrible, but what was even worse was that, once I was off the vent and alone in my hospital room, I had no idea how I got there. I thought I had been in a plane crash. I wanted to check my phone to see where I was flying in from but couldn’t because I thought my phone had been hacked by terrorists. I had no idea what was real and what was not. It was extremely scary.

DeAnn

When I think about the doctor coming in to tell Mike they had to put him on the ventilator, my heart absolutely breaks. It hurts to think of him all alone, having to make this decision without any of his family there to support him. Neither he nor I wanted to think about it, but we knew there was the possibility that he would never come off the ventilator. We hadn’t had a chance to hug him or even see him for days before his admission. If he didn’t make it, we would never get one of his amazing “Uncle Mike” hugs again.

Our friend, the ICU nurse, made it a point to find out which nurse was assigned to Mike and made it a priority to gather information from that nurse daily, allowing our family to receive updates on Mike’s status 2-3 times a day. In addition, the ICU physician was in daily contact with my parents: however, it was still excruciating not being able to be there. I spent a lot of time pacing the house, not eating or sleeping, checking my phone for texts, fielding texts and calls from friends and family. I was unable to do even simple household tasks, and left laundry, cooking, and my kids’ online schooling to my husband.

Feeling so helpless, I turned to prayer. My close friends organized a daily prayer vigil at 7:30 PM each night in the church parking lot and my family and I did not miss a night until Mike came home from the hospital. It brought me comfort to have that connection each night. I also began, along with one of Mike’s closest friends, organizing a meal train for the doctors and nurses in the ICU during both the day and night shifts. Interacting with those who were caring for him helped us feel closer to Mike. Because of the meal train, I became acquainted with two of his regular nurses, and they set up video chats on two occasions. Seeing him on the ventilator, so vulnerable, so ill, inspired the most difficult mix of emotions I have ever experienced. I was trying to just say anything that he would understand, hoping that he was hearing me. I told him, “We all miss you and love you. You are the strongest person we know and because of that, you will beat this!” I wish I felt as confident as I sounded.

Then, after 17 days, a miracle: he was taken off the ventilator and moved to the medical unit. Looking back, I think these are really the days that the presence of his family would have sped up his recovery. Mike was experiencing delirium and hallucinations as a result of illness, medications, and the time he spent in an induced coma. I wish I could have been there with him to be the one he asked if what he was experiencing was real or a hallucination. Then we could have laughed about it together; our family has always found that humor helps with healing.

Mike

I understand the purpose of the isolation, but it really did a number on my mind. I remember being in the ICU, having my catheter taken out, not knowing what was happening or how I ended up in the hospital. I was so confused and was seeing people who were not there. One morning, I woke up thinking I was in my house and I had stolen the hospital bed I was in. I was panicking and scheming about how I might get this hospital bed back to the hospital before anyone noticed. As I mentioned earlier, I thought I was on a plane that had been taken over by terrorists who were using us COVID patients as biological weapons. Then I thought agents of the Federal Bureau of Investigation were coming to interrogate me and that they were also looking for my sister. To protect DeAnn, the next time someone asked me the name of my sister, I told them, “Maria” (the name of my sister who passed away in 1991) rather than DeAnn, who is very much alive. Another time, I thought my grandmother and cousin had died in a plane crash. My cousin is a state representative in Illinois, so once I got my phone to work, I checked his Wikipedia page to see if there was a death date listed.

When I was less confused, I found it’s tough spending day after day lying in a hospital bed with no family member or friend to offer companionship, comfort, or clarity. Even though I was extremely weak and could barely walk, I was asking, daily, about when I was expected to be discharged. I had to get out.

One night, our friend the ICU nurse came into my room to sit with me and just talk. She spent about 30 minutes with me around 4:00 in the morning. It was wonderful. All I could think about was what a huge blessing it was. I don’t think she knows just how much that meant to me. More often, I would report some symptom of confusion or insomnia, and a nurse would offer me medications for sleep or pain. I did not want any more drugs in my system. Human contact would have been a far better treatment.

I was reluctant to ask for help when I needed something, like a trip to the bathroom or some ice water. When I did press the call button, I had to wait for the busy nurse or tech to put on all the protective gear, and then, when they left, watch them take all the steps to disinfect and rid themselves of the gear that they had just put on. Even so, I was excited when it came time to take my vital signs or administer medications because that meant human interaction, however brief (and even if it was 4:00 AM). I wanted to bathe or change gowns and/or socks, but I opted to wear the same gown and socks for over three-fourths of the time I was there because I did not want to burden the staff.

Video chat turned out to be one of the best tools for creating connection. I may have sobbed a few times when talking to my parents on FaceTime, but just seeing their faces made all the difference in the world.

Finally, on April 21, 25 days after I was led into the ED, I was discharged. As we reflect on this experience, my family and I have some recommendations for hospitals and health systems trying to make patient experience a priority during this pandemic:

Kueper Family Recommendations to Improve Patient Experience for Those With COVID-19

• Adopt a more systematic approach to communicating with patient families, which would greatly improve the connection between them and healthcare personnel. This is especially important for families when the patient is critically ill, and especially in times when the patient is in isolation. We were fortunate in that we received updates from nurses and physicians several times a day. This was partly due to the relationships or connections with staff members that existed previously or developed over the course of Mike’s stay. Staff members who became invested in Mike’s progress became part of his hospital “family.” Many people who have had a family member with COVID have not had this experience, nor did they have the opportunity to build relationships with the staff, which we felt were important to ensure good care and open and frequent communication with them and the patient. Therefore, we believe a more systematic approach toward communication (eg, “the team will call each day during multidisciplinary rounds at 11 AM,”) would greatly improve the connection between families and healthcare personnel.
• Allow visitation under certain conditions even while the patient is in isolation. Visitation would have been especially helpful once Mike was more awake but isolated and delirious. We know that these policies are difficult to create and navigate but believe that there should be allowance for some visitation when there is a clear clinical benefit (eg, delirium). Because Mike had little human contact the week after he was taken off the ventilator (eg, contact limited to nurses coming in to take vitals, once daily doctor visit), he had to navigate the hallucinations and delirium on his own. Even one family member by his side who could provide frequent feedback on reality would have helped to resolve the feelings of agitation and fear that can accompany delirium.
• Schedule more video chats. Even when Mike was on the ventilator, we found video chats to be an important way to understand his experience and connect with him. Although we know such chats are difficult for clinicians to schedule, it greatly improved the experience for us.
• Reassure patients that caring for them is not a burden and they should not hesitate to ask for help. Being contagious and believing you are a danger to others is a terrible feeling. No one on staff said or even implied that they were afraid to care for him, but Mike felt “dangerous” to the staff and as such hesitated to “burden” the clinicians with requests (eg, going to the bathroom, having a change of clothes). It is time-consuming and difficult to don PPE and the amount of effort it takes to enter the room is immediately obvious to the patient. Because of this, it is very important that the clinicians and staff reassure patients that it is part of the job and not a burden to come in and out of the room.

DeAnn

Having Mike alive and now home is an incredible gift. We are taking every chance we can to make up for the time that we could not see him and are so grateful for the hospital team that saved his life.

Mike

On April 21, I was discharged and sent home. Luckily, for about 2 weeks, I had a best friend, my brother, and DeAnn, separately, stay with me each night. This was a godsend as all made sure I was taking my medicine, eating, and doing my prescribed exercises. I am struggling with a long recovery. I used a walker for a while and had both a physical and occupational therapist visit me two to three times a week. I visit a neurologist for some of my symptoms that have not resolved, such as pain and atrophy in my right shoulder, hand tremors, and some numbness in my thighs. Thankfully, I was able to resume working from home, but even going up stairs causes me to become winded. I know that doctors don’t understand this disease very well, and neither do I. Sometimes I feel discouraged about how much it set me back physically. I wish things could have been different—that I could have avoided this disease altogether or had milder symptoms. But I am so grateful to be alive and so thankful for the doctors and nurses, as well as for my family, who could not be there physically during the hospitalization but did everything they could do to help me. Because of their love and support, I survived.

The authors have nothing to disclose.

Stepping onto a busy coronavirus disease (COVID-19) unit for the first time can elicit trepidation for any medical provider. For a group of deployed pediatric residents at a New York City hospital in the spring of 2020, it was also the first time caring for adults since medical school. Imagine a pediatrician receiving this handoff: “77-year-old female with a history of diabetes, peripheral vascular disease, and COPD admitted with COVID-19 pneumonia, now intubated and proned with O2 saturations in the 80s. To do: DNR discussion.” General anxiety around COVID-19 was compounded by the discomfort of being thrust into adult medicine. But the doctoring instinct we have been honing throughout training kicked in, and we acted.

As the COVID-19 crisis escalated in New York City, it became evident that staff from other specialties would be essential to manage the surge of patients. Hospital administrators selected a group of trainees for deployment based on their clinical experiences and willingness to volunteer. Almost overnight, a group of senior pediatric residents became adult providers, honoring the oath we each took to “remain a member of society with special obligations to all . . . fellow human beings.”¹

This health crisis brought different clinical disciplines together like never before. Entire wings of the hospital were converted into new COVID-19–dedicated wards and intensive care units (ICUs), and teams were built to optimize providers’ skills and capabilities. For example, one third-year pediatric resident was grouped with an outpatient endocrinologist—who had not practiced inpatient medicine in a decade—and a medicine intern. Hospitalists provided crucial support and guidance to these ward teams of deployed providers who were eager and willing to work but often not very knowledgeable about inpatient adult medicine.² In new ad hoc COVID-19 ICUs housed in other ICUs, where most pediatric residents were deployed, critical care attendings and neurointensivists led teams that also included anesthesiology, radiology, and neurosurgery residents, as well as nurses and advanced practice providers trained in various subspecialties of adult medicine.

Although we wanted to help in any way we could, the prospect of entering this new world was incredibly daunting. We had not treated adults in several years, and during that time, our clinical experience with pediatric medicine greatly surpassed our adult training from medical school. We dug out materials on adult diseases, watched impromptu lectures on COVID-19 given by our critical care attendings, taught ourselves ventilator management in adults, and reviewed advanced cardiac life support (ACLS) protocols. But putting this all into practice was entirely different. Nothing can truly prepare you for arriving at the bedside of a hemodynamically unstable patient suffering from a virus that no one really understands.

When we arrived and introduced ourselves, we occasionally encountered surprise and curiosity from other providers. We felt that there was a perception that pediatricians do not often take care of critically ill or complex patients. Some of us were reluctant to disclose our specialty, lest it cloud perceptions of our capabilities. However, sick patients awaited us, so we got to work.

There was a steep learning curve over the first few days, from adjusting insulin for type 2 diabetes to troubleshooting renal replacement therapy issues. Accustomed to pediatric weight-based dosing, we were very anxious about ordering medications. The adult providers on our teams oriented us and helped us with many of these concerns. But the mystery of COVID-19 was a great equalizing force, leaving providers of every background with questions: Should we anticoagulate? How about steroids? Could this clinical change be another effect of the virus or a new infection?

We were pleasantly surprised that many aspects of our pediatric training proved beneficial in caring for adults. The focus on family-centered rounds and shared decision-making in pediatrics had imprinted on us the paramount importance of good communication. We were very cognizant of involving loved ones in discussions, now conducted by telephone or video call because infection-prevention guidelines precluded visitors. Family members were thankful for frequent updates, and as a result, largely embraced us as the doctors treating their loved ones. On one occasion, an internist, whose mother was a patient, was delighted to learn that the provider was a pediatric resident, saying, “I know you’ll take such good care of her.”

With the hospital inundated with sick adults, colleagues were grateful for our help. More so, they seemed appreciative of our compassion and ability to maintain a sense of humanity during the pandemonium of the pandemic despite feeling vulnerable, scared, and often powerless against COVID-19. In pediatrics, we do our best to truly engage with patients, from playing games with a 6-year-old with perforated appendicitis to holding and soothing a newborn in the neonatal ICU. We carried those skills over to the adult side. The team appreciated when a pediatric resident, with the help of an occupational therapist, used a letter board to communicate and receive assent for a tracheostomy from a nonsedated, intubated patient, directly answering the patient’s questions and addressing concerns rather than relying solely on a family member’s consent. And, though we had not previously led end-of-life discussions, we found that we were capable of doing so with the compassion instilled in us from our pediatric training. It had prepared us to face the universal challenge of communication in times of grief.

Besides grappling with our insecurity in treating adults, we, like all medical providers, had to balance our desire to provide care while keeping ourselves safe from COVID-19. To reduce our risk of exposure and preserve the dwindling supply of personal protective equipment (PPE), the flow of rounding, bedside care, and interventions was adapted to better cluster examinations, blood draws, and bedside tasks. Although efficient, this meant we did not enter rooms as frequently, creating an unfamiliar distance between provider and patient.³ We feared missing moments of clinical decompensation, and for pediatricians who value close patient contact, this made for a deeply uncomfortable reality.

We considered every plausible treatment for critically ill patients, sometimes unsure if they were beneficial or instead complicating the course further. Was lack of improvement a treatment failure or just the natural progression of this new illness? Unfortunately, most of the time, treatments were to no avail. Watching the respiratory, cardiovascular, renal, and neurologic devastation of COVID-19 on so many patients was horrifying. Seeing patients die without their loved ones beside them and at an alarmingly fast rate was simply crushing, as other trainees have similarly described.⁴ It was unlike anything we had ever experienced in pediatrics. Though we had begun to see a few pediatric COVID-19 patients in the hospital, their disease course was less severe. And, in the rare cases when pediatric patients die, they are almost invariably surrounded by family. One pediatric resident, who had never performed a single death examination before, did three in 1 week. It was emotionally trying, yet we had little time to mourn, as deathbeds were only briefly empty before the next gravely ill patients filled them.

Deployment took a toll on our bodies as well. We padded our faces to alleviate skin breakdown from 12-hour shifts spent entirely in N95 masks. We sanitized and washed our hands constantly, developing cracked skin and dermatitis, and showered meticulously after every shift. We isolated ourselves from our families and loved ones to protect them from the virus.

Despite these challenges, positive moments emerged. We worked with many wonderful colleagues from different disciplines we likely never would have met, let alone work alongside. We valued each other’s skills, talents, and knowledge. On an overnight shift in one of the ICUs, among the “ragtag team of deployees,” as one pediatric resident phrased it, each presented a topic from his or her respective specialty that might interest others. The pediatrician presented Kawasaki disease, as adult colleagues were beginning to ask questions about its cousin, the emerging multisystem inflammatory syndrome in children (MIS-C). This collegiality promoted a culture of collaboration and respect for other specialties that will hopefully continue.

A strong drive toward teamwork and shared responsibility flourished during deployment. No one was above any task. Residents and even fellows performed typical frontline tasks, such as ordering laboratory work and coordinating imaging. We all helped the proning team turn patients. Everyone shared insights, perspectives, and information gleaned from friends in different wards and hospitals and the ever-evolving literature. As we grappled with unpredictable disease courses, the traditional hierarchical roles of medicine—attending, fellow, resident—often blurred. We felt like we were all in this together.

Patient triumphs were celebrated. We danced with an 80-year-old patient admitted for almost 2 weeks when she was informed of her discharge and gave a standing ovation for a 91-year-old woman as she headed home. Music played over the hospital loudspeaker for every patient discharge. We also tried to create moments of lightheartedness. In the ICUs, we ate donated meals together and posed for pictures to express our gratitude to restaurants. Camaraderie blossomed during deployment.

Answering the call to help during the COVID-19 surge in New York City indelibly shaped our experiences as trainees and physicians. We will carry with us the lessons that we learned, both in the short term for the possible resurgence of cases and in the long term for ongoing patient care for the rest of our careers. For those residents who may be called upon next, the experience will be challenging, but rewarding. Each trainee has a foundation of knowledge, abilities, and instincts that will be useful, so trust in your training. Do not be afraid to ask questions or for help. You may be leaving your comfort zone, but you will not be alone, and families and other clinicians will be grateful to have you there. You are resilient, and you will make a difference.

The authors have nothing to disclose.

Stepping onto a busy coronavirus disease (COVID-19) unit for the first time can elicit trepidation for any medical provider. For a group of deployed pediatric residents at a New York City hospital in the spring of 2020, it was also the first time caring for adults since medical school. Imagine a pediatrician receiving this handoff: “77-year-old female with a history of diabetes, peripheral vascular disease, and COPD admitted with COVID-19 pneumonia, now intubated and proned with O2 saturations in the 80s. To do: DNR discussion.” General anxiety around COVID-19 was compounded by the discomfort of being thrust into adult medicine. But the doctoring instinct we have been honing throughout training kicked in, and we acted.

As the COVID-19 crisis escalated in New York City, it became evident that staff from other specialties would be essential to manage the surge of patients. Hospital administrators selected a group of trainees for deployment based on their clinical experiences and willingness to volunteer. Almost overnight, a group of senior pediatric residents became adult providers, honoring the oath we each took to “remain a member of society with special obligations to all . . . fellow human beings.”¹

This health crisis brought different clinical disciplines together like never before. Entire wings of the hospital were converted into new COVID-19–dedicated wards and intensive care units (ICUs), and teams were built to optimize providers’ skills and capabilities. For example, one third-year pediatric resident was grouped with an outpatient endocrinologist—who had not practiced inpatient medicine in a decade—and a medicine intern. Hospitalists provided crucial support and guidance to these ward teams of deployed providers who were eager and willing to work but often not very knowledgeable about inpatient adult medicine.² In new ad hoc COVID-19 ICUs housed in other ICUs, where most pediatric residents were deployed, critical care attendings and neurointensivists led teams that also included anesthesiology, radiology, and neurosurgery residents, as well as nurses and advanced practice providers trained in various subspecialties of adult medicine.

Although we wanted to help in any way we could, the prospect of entering this new world was incredibly daunting. We had not treated adults in several years, and during that time, our clinical experience with pediatric medicine greatly surpassed our adult training from medical school. We dug out materials on adult diseases, watched impromptu lectures on COVID-19 given by our critical care attendings, taught ourselves ventilator management in adults, and reviewed advanced cardiac life support (ACLS) protocols. But putting this all into practice was entirely different. Nothing can truly prepare you for arriving at the bedside of a hemodynamically unstable patient suffering from a virus that no one really understands.

When we arrived and introduced ourselves, we occasionally encountered surprise and curiosity from other providers. We felt that there was a perception that pediatricians do not often take care of critically ill or complex patients. Some of us were reluctant to disclose our specialty, lest it cloud perceptions of our capabilities. However, sick patients awaited us, so we got to work.

There was a steep learning curve over the first few days, from adjusting insulin for type 2 diabetes to troubleshooting renal replacement therapy issues. Accustomed to pediatric weight-based dosing, we were very anxious about ordering medications. The adult providers on our teams oriented us and helped us with many of these concerns. But the mystery of COVID-19 was a great equalizing force, leaving providers of every background with questions: Should we anticoagulate? How about steroids? Could this clinical change be another effect of the virus or a new infection?

We were pleasantly surprised that many aspects of our pediatric training proved beneficial in caring for adults. The focus on family-centered rounds and shared decision-making in pediatrics had imprinted on us the paramount importance of good communication. We were very cognizant of involving loved ones in discussions, now conducted by telephone or video call because infection-prevention guidelines precluded visitors. Family members were thankful for frequent updates, and as a result, largely embraced us as the doctors treating their loved ones. On one occasion, an internist, whose mother was a patient, was delighted to learn that the provider was a pediatric resident, saying, “I know you’ll take such good care of her.”

With the hospital inundated with sick adults, colleagues were grateful for our help. More so, they seemed appreciative of our compassion and ability to maintain a sense of humanity during the pandemonium of the pandemic despite feeling vulnerable, scared, and often powerless against COVID-19. In pediatrics, we do our best to truly engage with patients, from playing games with a 6-year-old with perforated appendicitis to holding and soothing a newborn in the neonatal ICU. We carried those skills over to the adult side. The team appreciated when a pediatric resident, with the help of an occupational therapist, used a letter board to communicate and receive assent for a tracheostomy from a nonsedated, intubated patient, directly answering the patient’s questions and addressing concerns rather than relying solely on a family member’s consent. And, though we had not previously led end-of-life discussions, we found that we were capable of doing so with the compassion instilled in us from our pediatric training. It had prepared us to face the universal challenge of communication in times of grief.

Besides grappling with our insecurity in treating adults, we, like all medical providers, had to balance our desire to provide care while keeping ourselves safe from COVID-19. To reduce our risk of exposure and preserve the dwindling supply of personal protective equipment (PPE), the flow of rounding, bedside care, and interventions was adapted to better cluster examinations, blood draws, and bedside tasks. Although efficient, this meant we did not enter rooms as frequently, creating an unfamiliar distance between provider and patient.³ We feared missing moments of clinical decompensation, and for pediatricians who value close patient contact, this made for a deeply uncomfortable reality.

We considered every plausible treatment for critically ill patients, sometimes unsure if they were beneficial or instead complicating the course further. Was lack of improvement a treatment failure or just the natural progression of this new illness? Unfortunately, most of the time, treatments were to no avail. Watching the respiratory, cardiovascular, renal, and neurologic devastation of COVID-19 on so many patients was horrifying. Seeing patients die without their loved ones beside them and at an alarmingly fast rate was simply crushing, as other trainees have similarly described.⁴ It was unlike anything we had ever experienced in pediatrics. Though we had begun to see a few pediatric COVID-19 patients in the hospital, their disease course was less severe. And, in the rare cases when pediatric patients die, they are almost invariably surrounded by family. One pediatric resident, who had never performed a single death examination before, did three in 1 week. It was emotionally trying, yet we had little time to mourn, as deathbeds were only briefly empty before the next gravely ill patients filled them.

Deployment took a toll on our bodies as well. We padded our faces to alleviate skin breakdown from 12-hour shifts spent entirely in N95 masks. We sanitized and washed our hands constantly, developing cracked skin and dermatitis, and showered meticulously after every shift. We isolated ourselves from our families and loved ones to protect them from the virus.

Despite these challenges, positive moments emerged. We worked with many wonderful colleagues from different disciplines we likely never would have met, let alone work alongside. We valued each other’s skills, talents, and knowledge. On an overnight shift in one of the ICUs, among the “ragtag team of deployees,” as one pediatric resident phrased it, each presented a topic from his or her respective specialty that might interest others. The pediatrician presented Kawasaki disease, as adult colleagues were beginning to ask questions about its cousin, the emerging multisystem inflammatory syndrome in children (MIS-C). This collegiality promoted a culture of collaboration and respect for other specialties that will hopefully continue.

A strong drive toward teamwork and shared responsibility flourished during deployment. No one was above any task. Residents and even fellows performed typical frontline tasks, such as ordering laboratory work and coordinating imaging. We all helped the proning team turn patients. Everyone shared insights, perspectives, and information gleaned from friends in different wards and hospitals and the ever-evolving literature. As we grappled with unpredictable disease courses, the traditional hierarchical roles of medicine—attending, fellow, resident—often blurred. We felt like we were all in this together.

Patient triumphs were celebrated. We danced with an 80-year-old patient admitted for almost 2 weeks when she was informed of her discharge and gave a standing ovation for a 91-year-old woman as she headed home. Music played over the hospital loudspeaker for every patient discharge. We also tried to create moments of lightheartedness. In the ICUs, we ate donated meals together and posed for pictures to express our gratitude to restaurants. Camaraderie blossomed during deployment.

Answering the call to help during the COVID-19 surge in New York City indelibly shaped our experiences as trainees and physicians. We will carry with us the lessons that we learned, both in the short term for the possible resurgence of cases and in the long term for ongoing patient care for the rest of our careers. For those residents who may be called upon next, the experience will be challenging, but rewarding. Each trainee has a foundation of knowledge, abilities, and instincts that will be useful, so trust in your training. Do not be afraid to ask questions or for help. You may be leaving your comfort zone, but you will not be alone, and families and other clinicians will be grateful to have you there. You are resilient, and you will make a difference.

The authors have nothing to disclose.

Stepping onto a busy coronavirus disease (COVID-19) unit for the first time can elicit trepidation for any medical provider. For a group of deployed pediatric residents at a New York City hospital in the spring of 2020, it was also the first time caring for adults since medical school. Imagine a pediatrician receiving this handoff: “77-year-old female with a history of diabetes, peripheral vascular disease, and COPD admitted with COVID-19 pneumonia, now intubated and proned with O2 saturations in the 80s. To do: DNR discussion.” General anxiety around COVID-19 was compounded by the discomfort of being thrust into adult medicine. But the doctoring instinct we have been honing throughout training kicked in, and we acted.

As the COVID-19 crisis escalated in New York City, it became evident that staff from other specialties would be essential to manage the surge of patients. Hospital administrators selected a group of trainees for deployment based on their clinical experiences and willingness to volunteer. Almost overnight, a group of senior pediatric residents became adult providers, honoring the oath we each took to “remain a member of society with special obligations to all . . . fellow human beings.”¹

This health crisis brought different clinical disciplines together like never before. Entire wings of the hospital were converted into new COVID-19–dedicated wards and intensive care units (ICUs), and teams were built to optimize providers’ skills and capabilities. For example, one third-year pediatric resident was grouped with an outpatient endocrinologist—who had not practiced inpatient medicine in a decade—and a medicine intern. Hospitalists provided crucial support and guidance to these ward teams of deployed providers who were eager and willing to work but often not very knowledgeable about inpatient adult medicine.² In new ad hoc COVID-19 ICUs housed in other ICUs, where most pediatric residents were deployed, critical care attendings and neurointensivists led teams that also included anesthesiology, radiology, and neurosurgery residents, as well as nurses and advanced practice providers trained in various subspecialties of adult medicine.

Although we wanted to help in any way we could, the prospect of entering this new world was incredibly daunting. We had not treated adults in several years, and during that time, our clinical experience with pediatric medicine greatly surpassed our adult training from medical school. We dug out materials on adult diseases, watched impromptu lectures on COVID-19 given by our critical care attendings, taught ourselves ventilator management in adults, and reviewed advanced cardiac life support (ACLS) protocols. But putting this all into practice was entirely different. Nothing can truly prepare you for arriving at the bedside of a hemodynamically unstable patient suffering from a virus that no one really understands.

When we arrived and introduced ourselves, we occasionally encountered surprise and curiosity from other providers. We felt that there was a perception that pediatricians do not often take care of critically ill or complex patients. Some of us were reluctant to disclose our specialty, lest it cloud perceptions of our capabilities. However, sick patients awaited us, so we got to work.

There was a steep learning curve over the first few days, from adjusting insulin for type 2 diabetes to troubleshooting renal replacement therapy issues. Accustomed to pediatric weight-based dosing, we were very anxious about ordering medications. The adult providers on our teams oriented us and helped us with many of these concerns. But the mystery of COVID-19 was a great equalizing force, leaving providers of every background with questions: Should we anticoagulate? How about steroids? Could this clinical change be another effect of the virus or a new infection?

We were pleasantly surprised that many aspects of our pediatric training proved beneficial in caring for adults. The focus on family-centered rounds and shared decision-making in pediatrics had imprinted on us the paramount importance of good communication. We were very cognizant of involving loved ones in discussions, now conducted by telephone or video call because infection-prevention guidelines precluded visitors. Family members were thankful for frequent updates, and as a result, largely embraced us as the doctors treating their loved ones. On one occasion, an internist, whose mother was a patient, was delighted to learn that the provider was a pediatric resident, saying, “I know you’ll take such good care of her.”

With the hospital inundated with sick adults, colleagues were grateful for our help. More so, they seemed appreciative of our compassion and ability to maintain a sense of humanity during the pandemonium of the pandemic despite feeling vulnerable, scared, and often powerless against COVID-19. In pediatrics, we do our best to truly engage with patients, from playing games with a 6-year-old with perforated appendicitis to holding and soothing a newborn in the neonatal ICU. We carried those skills over to the adult side. The team appreciated when a pediatric resident, with the help of an occupational therapist, used a letter board to communicate and receive assent for a tracheostomy from a nonsedated, intubated patient, directly answering the patient’s questions and addressing concerns rather than relying solely on a family member’s consent. And, though we had not previously led end-of-life discussions, we found that we were capable of doing so with the compassion instilled in us from our pediatric training. It had prepared us to face the universal challenge of communication in times of grief.

Besides grappling with our insecurity in treating adults, we, like all medical providers, had to balance our desire to provide care while keeping ourselves safe from COVID-19. To reduce our risk of exposure and preserve the dwindling supply of personal protective equipment (PPE), the flow of rounding, bedside care, and interventions was adapted to better cluster examinations, blood draws, and bedside tasks. Although efficient, this meant we did not enter rooms as frequently, creating an unfamiliar distance between provider and patient.³ We feared missing moments of clinical decompensation, and for pediatricians who value close patient contact, this made for a deeply uncomfortable reality.

We considered every plausible treatment for critically ill patients, sometimes unsure if they were beneficial or instead complicating the course further. Was lack of improvement a treatment failure or just the natural progression of this new illness? Unfortunately, most of the time, treatments were to no avail. Watching the respiratory, cardiovascular, renal, and neurologic devastation of COVID-19 on so many patients was horrifying. Seeing patients die without their loved ones beside them and at an alarmingly fast rate was simply crushing, as other trainees have similarly described.⁴ It was unlike anything we had ever experienced in pediatrics. Though we had begun to see a few pediatric COVID-19 patients in the hospital, their disease course was less severe. And, in the rare cases when pediatric patients die, they are almost invariably surrounded by family. One pediatric resident, who had never performed a single death examination before, did three in 1 week. It was emotionally trying, yet we had little time to mourn, as deathbeds were only briefly empty before the next gravely ill patients filled them.

Deployment took a toll on our bodies as well. We padded our faces to alleviate skin breakdown from 12-hour shifts spent entirely in N95 masks. We sanitized and washed our hands constantly, developing cracked skin and dermatitis, and showered meticulously after every shift. We isolated ourselves from our families and loved ones to protect them from the virus.

Despite these challenges, positive moments emerged. We worked with many wonderful colleagues from different disciplines we likely never would have met, let alone work alongside. We valued each other’s skills, talents, and knowledge. On an overnight shift in one of the ICUs, among the “ragtag team of deployees,” as one pediatric resident phrased it, each presented a topic from his or her respective specialty that might interest others. The pediatrician presented Kawasaki disease, as adult colleagues were beginning to ask questions about its cousin, the emerging multisystem inflammatory syndrome in children (MIS-C). This collegiality promoted a culture of collaboration and respect for other specialties that will hopefully continue.

A strong drive toward teamwork and shared responsibility flourished during deployment. No one was above any task. Residents and even fellows performed typical frontline tasks, such as ordering laboratory work and coordinating imaging. We all helped the proning team turn patients. Everyone shared insights, perspectives, and information gleaned from friends in different wards and hospitals and the ever-evolving literature. As we grappled with unpredictable disease courses, the traditional hierarchical roles of medicine—attending, fellow, resident—often blurred. We felt like we were all in this together.

Patient triumphs were celebrated. We danced with an 80-year-old patient admitted for almost 2 weeks when she was informed of her discharge and gave a standing ovation for a 91-year-old woman as she headed home. Music played over the hospital loudspeaker for every patient discharge. We also tried to create moments of lightheartedness. In the ICUs, we ate donated meals together and posed for pictures to express our gratitude to restaurants. Camaraderie blossomed during deployment.

Answering the call to help during the COVID-19 surge in New York City indelibly shaped our experiences as trainees and physicians. We will carry with us the lessons that we learned, both in the short term for the possible resurgence of cases and in the long term for ongoing patient care for the rest of our careers. For those residents who may be called upon next, the experience will be challenging, but rewarding. Each trainee has a foundation of knowledge, abilities, and instincts that will be useful, so trust in your training. Do not be afraid to ask questions or for help. You may be leaving your comfort zone, but you will not be alone, and families and other clinicians will be grateful to have you there. You are resilient, and you will make a difference.

The authors have nothing to disclose.