Hospital Medicine

Using point-of-care ultrasound, internists can reduce the number of tests performed in a hospital without affecting mortality, researchers say.

“It’s a safe and very useful tool,” Marco Barchiesi, MD, an internal medicine resident at Luigi Sacco Hospital in Milan, said in an interview. “We had a great reduction in chest x-rays because of the use of ultrasound.”

The finding addresses concerns that ultrasound used in primary care could consume more health care resources or put patients at risk.

Dr. Barchiesi and colleagues published their findings July 20 in the European Journal of Internal Medicine.

Point-of-care ultrasound has become increasingly common as miniaturization of devices has made them more portable. The approach has caught on particularly in emergency departments where quick decisions are of the essence.

Its use in internal medicine has been more controversial, with concerns raised that improperly trained practitioners may miss diagnoses or refer patients for unnecessary tests as a result of uncertainty about their findings.

To measure the effect of point-of-care ultrasound in an internal medicine hospital ward, Dr. Barchiesi and colleagues alternated months when point-of-care ultrasound was allowed with months when it was not allowed, for a total of 4 months each, on an internal medicine unit. They allowed the ultrasound to be used for invasive procedures and excluded patients whose critical condition made point-of-care ultrasound crucial.

The researchers analyzed data on 263 patients in the “on” months when point-of-care ultrasound was used, and 255 in the “off” months when it wasn’t used. The two groups were well balanced in age, sex, comorbidity, and clinical impairment.

During the on months, the internists ordered 113 diagnostic tests (0.43 per patient). During the off months they ordered 329 tests (1.29 per patient).

The odds of being referred for a chest x-ray were 87% less in the “on” months, compared with the off months, a statistically significant finding (P < .001). The risk for a chest CT scan and abdominal ultrasound were also reduced during the on months, but the risk for an abdominal CT was increased.

Nineteen patients died during the o” months and 10 during the off months, a difference that was not statistically significant (P = .15). The median length of stay in the hospital was almost the same for the two groups: 9 days for the on months and 9 days for the off months. The difference was also not statistically significant (P = .094).

Point-of-care ultrasound is particularly accurate in identifying cardiac abnormalities and pleural fluid and pneumonia, and it can be used effectively for monitoring heart conditions, the researchers wrote. This could explain the reduction in chest x-rays and CT scans.

On the other hand, ultrasound cannot address such questions as staging in an abdominal malignancy, and unexpected findings are more common with abdominal than chest ultrasound. This could explain why the point-of-care ultrasound did not reduce the use of abdominal CT, the researchers speculated.

They acknowledged that the patients in their sample had an average age of 81 years, raising questions about how well their data could be applied to a younger population. And they noted that they used point-of-care ultrasound frequently, so they were particularly adept with it. “We use it almost every day in our clinical practice,” said Dr. Barchiesi.

Those factors may have played a key role in the success of point-of-care ultrasound in this study, said Michael Wagner, MD, an assistant professor of medicine at the University of South Carolina, Greenville, who has helped colleagues incorporate ultrasound into their practices.

Elderly patients often present with multiple comorbidities and atypical signs and symptoms, he said. “Sometimes they can be very confusing as to the underlying clinical picture. Ultrasound is being used frequently to better assess these complicated patients.”

Dr. Wagner said extensive training is required to use point-of-care ultrasound accurately.

Dr. Barchiesi also acknowledged that the devices used in this study were large portable machines, not the simpler and less expensive hand-held versions that are also available for similar purposes.

Point-of-care ultrasound is a promising innovation, said Thomas Melgar, MD, a professor of medicine at Western Michigan University, Kalamazoo. “The advantage is that the exam is being done by someone who knows the patient and specifically what they’re looking for. It’s done at the bedside so you don’t have to move the patient.”

The study could help address opposition to internal medicine residents being trained in the technique, he said, adding that “I think it’s very exciting.”

The study was partially supported by Philips, which provided the ultrasound devices. Dr. Barchiesi, Dr. Melgar, and Dr. Wagner disclosed no relevant financial relationships.

A version of this article originally appeared on Medscape.com.

Using point-of-care ultrasound, internists can reduce the number of tests performed in a hospital without affecting mortality, researchers say.

“It’s a safe and very useful tool,” Marco Barchiesi, MD, an internal medicine resident at Luigi Sacco Hospital in Milan, said in an interview. “We had a great reduction in chest x-rays because of the use of ultrasound.”

The finding addresses concerns that ultrasound used in primary care could consume more health care resources or put patients at risk.

Dr. Barchiesi and colleagues published their findings July 20 in the European Journal of Internal Medicine.

Point-of-care ultrasound has become increasingly common as miniaturization of devices has made them more portable. The approach has caught on particularly in emergency departments where quick decisions are of the essence.

Its use in internal medicine has been more controversial, with concerns raised that improperly trained practitioners may miss diagnoses or refer patients for unnecessary tests as a result of uncertainty about their findings.

To measure the effect of point-of-care ultrasound in an internal medicine hospital ward, Dr. Barchiesi and colleagues alternated months when point-of-care ultrasound was allowed with months when it was not allowed, for a total of 4 months each, on an internal medicine unit. They allowed the ultrasound to be used for invasive procedures and excluded patients whose critical condition made point-of-care ultrasound crucial.

The researchers analyzed data on 263 patients in the “on” months when point-of-care ultrasound was used, and 255 in the “off” months when it wasn’t used. The two groups were well balanced in age, sex, comorbidity, and clinical impairment.

During the on months, the internists ordered 113 diagnostic tests (0.43 per patient). During the off months they ordered 329 tests (1.29 per patient).

The odds of being referred for a chest x-ray were 87% less in the “on” months, compared with the off months, a statistically significant finding (P < .001). The risk for a chest CT scan and abdominal ultrasound were also reduced during the on months, but the risk for an abdominal CT was increased.

Nineteen patients died during the o” months and 10 during the off months, a difference that was not statistically significant (P = .15). The median length of stay in the hospital was almost the same for the two groups: 9 days for the on months and 9 days for the off months. The difference was also not statistically significant (P = .094).

Point-of-care ultrasound is particularly accurate in identifying cardiac abnormalities and pleural fluid and pneumonia, and it can be used effectively for monitoring heart conditions, the researchers wrote. This could explain the reduction in chest x-rays and CT scans.

On the other hand, ultrasound cannot address such questions as staging in an abdominal malignancy, and unexpected findings are more common with abdominal than chest ultrasound. This could explain why the point-of-care ultrasound did not reduce the use of abdominal CT, the researchers speculated.

They acknowledged that the patients in their sample had an average age of 81 years, raising questions about how well their data could be applied to a younger population. And they noted that they used point-of-care ultrasound frequently, so they were particularly adept with it. “We use it almost every day in our clinical practice,” said Dr. Barchiesi.

Those factors may have played a key role in the success of point-of-care ultrasound in this study, said Michael Wagner, MD, an assistant professor of medicine at the University of South Carolina, Greenville, who has helped colleagues incorporate ultrasound into their practices.

Elderly patients often present with multiple comorbidities and atypical signs and symptoms, he said. “Sometimes they can be very confusing as to the underlying clinical picture. Ultrasound is being used frequently to better assess these complicated patients.”

Dr. Wagner said extensive training is required to use point-of-care ultrasound accurately.

Dr. Barchiesi also acknowledged that the devices used in this study were large portable machines, not the simpler and less expensive hand-held versions that are also available for similar purposes.

Point-of-care ultrasound is a promising innovation, said Thomas Melgar, MD, a professor of medicine at Western Michigan University, Kalamazoo. “The advantage is that the exam is being done by someone who knows the patient and specifically what they’re looking for. It’s done at the bedside so you don’t have to move the patient.”

The study could help address opposition to internal medicine residents being trained in the technique, he said, adding that “I think it’s very exciting.”

The study was partially supported by Philips, which provided the ultrasound devices. Dr. Barchiesi, Dr. Melgar, and Dr. Wagner disclosed no relevant financial relationships.

A version of this article originally appeared on Medscape.com.

Using point-of-care ultrasound, internists can reduce the number of tests performed in a hospital without affecting mortality, researchers say.

“It’s a safe and very useful tool,” Marco Barchiesi, MD, an internal medicine resident at Luigi Sacco Hospital in Milan, said in an interview. “We had a great reduction in chest x-rays because of the use of ultrasound.”

The finding addresses concerns that ultrasound used in primary care could consume more health care resources or put patients at risk.

Dr. Barchiesi and colleagues published their findings July 20 in the European Journal of Internal Medicine.

Point-of-care ultrasound has become increasingly common as miniaturization of devices has made them more portable. The approach has caught on particularly in emergency departments where quick decisions are of the essence.

Its use in internal medicine has been more controversial, with concerns raised that improperly trained practitioners may miss diagnoses or refer patients for unnecessary tests as a result of uncertainty about their findings.

To measure the effect of point-of-care ultrasound in an internal medicine hospital ward, Dr. Barchiesi and colleagues alternated months when point-of-care ultrasound was allowed with months when it was not allowed, for a total of 4 months each, on an internal medicine unit. They allowed the ultrasound to be used for invasive procedures and excluded patients whose critical condition made point-of-care ultrasound crucial.

The researchers analyzed data on 263 patients in the “on” months when point-of-care ultrasound was used, and 255 in the “off” months when it wasn’t used. The two groups were well balanced in age, sex, comorbidity, and clinical impairment.

During the on months, the internists ordered 113 diagnostic tests (0.43 per patient). During the off months they ordered 329 tests (1.29 per patient).

The odds of being referred for a chest x-ray were 87% less in the “on” months, compared with the off months, a statistically significant finding (P < .001). The risk for a chest CT scan and abdominal ultrasound were also reduced during the on months, but the risk for an abdominal CT was increased.

Nineteen patients died during the o” months and 10 during the off months, a difference that was not statistically significant (P = .15). The median length of stay in the hospital was almost the same for the two groups: 9 days for the on months and 9 days for the off months. The difference was also not statistically significant (P = .094).

Point-of-care ultrasound is particularly accurate in identifying cardiac abnormalities and pleural fluid and pneumonia, and it can be used effectively for monitoring heart conditions, the researchers wrote. This could explain the reduction in chest x-rays and CT scans.

On the other hand, ultrasound cannot address such questions as staging in an abdominal malignancy, and unexpected findings are more common with abdominal than chest ultrasound. This could explain why the point-of-care ultrasound did not reduce the use of abdominal CT, the researchers speculated.

They acknowledged that the patients in their sample had an average age of 81 years, raising questions about how well their data could be applied to a younger population. And they noted that they used point-of-care ultrasound frequently, so they were particularly adept with it. “We use it almost every day in our clinical practice,” said Dr. Barchiesi.

Those factors may have played a key role in the success of point-of-care ultrasound in this study, said Michael Wagner, MD, an assistant professor of medicine at the University of South Carolina, Greenville, who has helped colleagues incorporate ultrasound into their practices.

Elderly patients often present with multiple comorbidities and atypical signs and symptoms, he said. “Sometimes they can be very confusing as to the underlying clinical picture. Ultrasound is being used frequently to better assess these complicated patients.”

Dr. Wagner said extensive training is required to use point-of-care ultrasound accurately.

Dr. Barchiesi also acknowledged that the devices used in this study were large portable machines, not the simpler and less expensive hand-held versions that are also available for similar purposes.

Point-of-care ultrasound is a promising innovation, said Thomas Melgar, MD, a professor of medicine at Western Michigan University, Kalamazoo. “The advantage is that the exam is being done by someone who knows the patient and specifically what they’re looking for. It’s done at the bedside so you don’t have to move the patient.”

The study could help address opposition to internal medicine residents being trained in the technique, he said, adding that “I think it’s very exciting.”

The study was partially supported by Philips, which provided the ultrasound devices. Dr. Barchiesi, Dr. Melgar, and Dr. Wagner disclosed no relevant financial relationships.

A version of this article originally appeared on Medscape.com.

“The world accommodates you for fitting in, but only rewards you for standing out.”

—Matshona Dhliwayo

Graceful self-promotion—a way of speaking diplomatically and strategically about yourself and your accomplishments—is a key behavior to achieve professional success in medicine. However, some of us are uncomfortable with promoting ourselves in the workplace because of concerns about receiving negative backlash for bragging. These concerns may have roots in our cultural and gender backgrounds, norms that strongly influence our social behaviors. Cultures that emphasize collectivism (eg, East Asia, Scandinavia, Latin America), which is associated with modesty and a focus on “we,” may not approve of self-promotion in contrast to cultures that emphasize individualism (eg, United States, Canada, and parts of Western Europe).¹ Additionally, societal gender roles across different cultures focus on women conforming to a “modesty norm,” by which they are socialized to “be nice” and “not too demanding.” Female physicians practicing self-promotion for career advancement may experience a backlash with social penalties and career repercussions.²

One’s avoiding self-promotion may lead others to prematurely dismiss a physician’s capability, competence, ambition, and qualifications for leadership and other opportunities. These oversights may be a contributing factor in the existing inequities in physician compensation, faculty promotions, leadership roles, speaking engagements, journal editorial boards, and more. Women make up over 50% of all US medical students, yet only 18% are hospital CEOs, 16% are deans and department chairs, and 7% are editors-in-chief of high-impact medical journals.³

So how do you get started overcoming cultural and gender barriers and embrace graceful self-promotion? Start small!

First, write a reference or nominating letter for a colleague. The exercise of synthesizing someone else’s accomplishments, skills, and experiences for a specific audience and purpose will give you a template to apply to yourself.

Second, identify an accomplishment with an outcome that educates others about you, your ideas, and your impact. Practice with a trusted peer to frame your accomplishment and its context as a story; for example: “Dr. X, I am pleased to share that I will present a key workshop on Y at the upcoming national Z meeting, based largely on the outcomes from a QI initiative that I developed and oversaw with support from my hospitalist team. We overcame initial staff resistance by recruiting project champions among the interdisciplinary team and successfully reduced readmissions for Y from A% to B% over a 12-month period.”

Third, consider when and how to strategically promote the accomplishment with your medical director, clinical leadership, department leadership, etc. Start out gracefully self-promoting in person or via email with a leader with whom you already have a relationship. If you want to share your accomplishment with a leader who does not yet know you (but may be important to your career), nudge a mentor or sponsor for an introductory conversation.

Finally, ask yourself the next time you are doing a performance review or attending a hospital committee meeting: Am I contributing to a culture in which everyone is encouraged to share their accomplishments? Which qualified candidates who don’t speak out about themselves can I nominate, sponsor, mentor, or encourage for an upcoming opportunity to increase cultural and gender representation? After all, paying it forward helps foster the success of others.

Graceful self-promotion is an important tool for personal and professional development in healthcare. Cultural and gender-based barriers to self-promotion can be surmounted through self-awareness, practice with trusted peers, and recognition of the importance of storytelling gracefully. A medical workplace culture that encourages sharing achievements and celebrates individual and team accomplishments can go a long way toward helping people change their perception of self-promotion and overcome their hesitations.

“The world accommodates you for fitting in, but only rewards you for standing out.”

—Matshona Dhliwayo

Graceful self-promotion—a way of speaking diplomatically and strategically about yourself and your accomplishments—is a key behavior to achieve professional success in medicine. However, some of us are uncomfortable with promoting ourselves in the workplace because of concerns about receiving negative backlash for bragging. These concerns may have roots in our cultural and gender backgrounds, norms that strongly influence our social behaviors. Cultures that emphasize collectivism (eg, East Asia, Scandinavia, Latin America), which is associated with modesty and a focus on “we,” may not approve of self-promotion in contrast to cultures that emphasize individualism (eg, United States, Canada, and parts of Western Europe).¹ Additionally, societal gender roles across different cultures focus on women conforming to a “modesty norm,” by which they are socialized to “be nice” and “not too demanding.” Female physicians practicing self-promotion for career advancement may experience a backlash with social penalties and career repercussions.²

One’s avoiding self-promotion may lead others to prematurely dismiss a physician’s capability, competence, ambition, and qualifications for leadership and other opportunities. These oversights may be a contributing factor in the existing inequities in physician compensation, faculty promotions, leadership roles, speaking engagements, journal editorial boards, and more. Women make up over 50% of all US medical students, yet only 18% are hospital CEOs, 16% are deans and department chairs, and 7% are editors-in-chief of high-impact medical journals.³

So how do you get started overcoming cultural and gender barriers and embrace graceful self-promotion? Start small!

First, write a reference or nominating letter for a colleague. The exercise of synthesizing someone else’s accomplishments, skills, and experiences for a specific audience and purpose will give you a template to apply to yourself.

Second, identify an accomplishment with an outcome that educates others about you, your ideas, and your impact. Practice with a trusted peer to frame your accomplishment and its context as a story; for example: “Dr. X, I am pleased to share that I will present a key workshop on Y at the upcoming national Z meeting, based largely on the outcomes from a QI initiative that I developed and oversaw with support from my hospitalist team. We overcame initial staff resistance by recruiting project champions among the interdisciplinary team and successfully reduced readmissions for Y from A% to B% over a 12-month period.”

Third, consider when and how to strategically promote the accomplishment with your medical director, clinical leadership, department leadership, etc. Start out gracefully self-promoting in person or via email with a leader with whom you already have a relationship. If you want to share your accomplishment with a leader who does not yet know you (but may be important to your career), nudge a mentor or sponsor for an introductory conversation.

Finally, ask yourself the next time you are doing a performance review or attending a hospital committee meeting: Am I contributing to a culture in which everyone is encouraged to share their accomplishments? Which qualified candidates who don’t speak out about themselves can I nominate, sponsor, mentor, or encourage for an upcoming opportunity to increase cultural and gender representation? After all, paying it forward helps foster the success of others.

Graceful self-promotion is an important tool for personal and professional development in healthcare. Cultural and gender-based barriers to self-promotion can be surmounted through self-awareness, practice with trusted peers, and recognition of the importance of storytelling gracefully. A medical workplace culture that encourages sharing achievements and celebrates individual and team accomplishments can go a long way toward helping people change their perception of self-promotion and overcome their hesitations.

“The world accommodates you for fitting in, but only rewards you for standing out.”

—Matshona Dhliwayo

Graceful self-promotion—a way of speaking diplomatically and strategically about yourself and your accomplishments—is a key behavior to achieve professional success in medicine. However, some of us are uncomfortable with promoting ourselves in the workplace because of concerns about receiving negative backlash for bragging. These concerns may have roots in our cultural and gender backgrounds, norms that strongly influence our social behaviors. Cultures that emphasize collectivism (eg, East Asia, Scandinavia, Latin America), which is associated with modesty and a focus on “we,” may not approve of self-promotion in contrast to cultures that emphasize individualism (eg, United States, Canada, and parts of Western Europe).¹ Additionally, societal gender roles across different cultures focus on women conforming to a “modesty norm,” by which they are socialized to “be nice” and “not too demanding.” Female physicians practicing self-promotion for career advancement may experience a backlash with social penalties and career repercussions.²

One’s avoiding self-promotion may lead others to prematurely dismiss a physician’s capability, competence, ambition, and qualifications for leadership and other opportunities. These oversights may be a contributing factor in the existing inequities in physician compensation, faculty promotions, leadership roles, speaking engagements, journal editorial boards, and more. Women make up over 50% of all US medical students, yet only 18% are hospital CEOs, 16% are deans and department chairs, and 7% are editors-in-chief of high-impact medical journals.³

So how do you get started overcoming cultural and gender barriers and embrace graceful self-promotion? Start small!

First, write a reference or nominating letter for a colleague. The exercise of synthesizing someone else’s accomplishments, skills, and experiences for a specific audience and purpose will give you a template to apply to yourself.

Second, identify an accomplishment with an outcome that educates others about you, your ideas, and your impact. Practice with a trusted peer to frame your accomplishment and its context as a story; for example: “Dr. X, I am pleased to share that I will present a key workshop on Y at the upcoming national Z meeting, based largely on the outcomes from a QI initiative that I developed and oversaw with support from my hospitalist team. We overcame initial staff resistance by recruiting project champions among the interdisciplinary team and successfully reduced readmissions for Y from A% to B% over a 12-month period.”

Third, consider when and how to strategically promote the accomplishment with your medical director, clinical leadership, department leadership, etc. Start out gracefully self-promoting in person or via email with a leader with whom you already have a relationship. If you want to share your accomplishment with a leader who does not yet know you (but may be important to your career), nudge a mentor or sponsor for an introductory conversation.

Finally, ask yourself the next time you are doing a performance review or attending a hospital committee meeting: Am I contributing to a culture in which everyone is encouraged to share their accomplishments? Which qualified candidates who don’t speak out about themselves can I nominate, sponsor, mentor, or encourage for an upcoming opportunity to increase cultural and gender representation? After all, paying it forward helps foster the success of others.

Graceful self-promotion is an important tool for personal and professional development in healthcare. Cultural and gender-based barriers to self-promotion can be surmounted through self-awareness, practice with trusted peers, and recognition of the importance of storytelling gracefully. A medical workplace culture that encourages sharing achievements and celebrates individual and team accomplishments can go a long way toward helping people change their perception of self-promotion and overcome their hesitations.

We read with interest the article by Maxwell and Mirza.¹ We appreciate using the large National Surgical Quality Improvement Project (NSQIP) database to assess comanagement outcomes, although we have concerns about the study design. Propensity score–matching (PSM) studies are limited; PSMs generate an average effect that neither establishes whether a treatment is optimal for a given patient nor control for unmeasured confounders.² Some baseline characteristics suggest that the comanaged and noncomanaged populations are quite different and, therefore, likely had unmeasured confounders that contributed to not detecting true effects. Also, as suggested by the authors, the NSQIP definitions of comanagement and standardized hip fracture program are broad. Recent studies in hip fracture comanagement attribute best outcomes to an organized program, shared decision making, expert comanagers, and each service having full responsibility including writing their own orders.^3-5 As no large database captures this distinction, it is not yet possible to perform a large, multicenter analysis. This type of comanagement cannot be studied in a randomized controlled trial. We recommend caution in overinterpreting the conclusions because there is substantial evidence in favor of optimized comanagement.

We read with interest the article by Maxwell and Mirza.¹ We appreciate using the large National Surgical Quality Improvement Project (NSQIP) database to assess comanagement outcomes, although we have concerns about the study design. Propensity score–matching (PSM) studies are limited; PSMs generate an average effect that neither establishes whether a treatment is optimal for a given patient nor control for unmeasured confounders.² Some baseline characteristics suggest that the comanaged and noncomanaged populations are quite different and, therefore, likely had unmeasured confounders that contributed to not detecting true effects. Also, as suggested by the authors, the NSQIP definitions of comanagement and standardized hip fracture program are broad. Recent studies in hip fracture comanagement attribute best outcomes to an organized program, shared decision making, expert comanagers, and each service having full responsibility including writing their own orders.^3-5 As no large database captures this distinction, it is not yet possible to perform a large, multicenter analysis. This type of comanagement cannot be studied in a randomized controlled trial. We recommend caution in overinterpreting the conclusions because there is substantial evidence in favor of optimized comanagement.

We read with interest the article by Maxwell and Mirza.¹ We appreciate using the large National Surgical Quality Improvement Project (NSQIP) database to assess comanagement outcomes, although we have concerns about the study design. Propensity score–matching (PSM) studies are limited; PSMs generate an average effect that neither establishes whether a treatment is optimal for a given patient nor control for unmeasured confounders.² Some baseline characteristics suggest that the comanaged and noncomanaged populations are quite different and, therefore, likely had unmeasured confounders that contributed to not detecting true effects. Also, as suggested by the authors, the NSQIP definitions of comanagement and standardized hip fracture program are broad. Recent studies in hip fracture comanagement attribute best outcomes to an organized program, shared decision making, expert comanagers, and each service having full responsibility including writing their own orders.^3-5 As no large database captures this distinction, it is not yet possible to perform a large, multicenter analysis. This type of comanagement cannot be studied in a randomized controlled trial. We recommend caution in overinterpreting the conclusions because there is substantial evidence in favor of optimized comanagement.

The growth in the hospitalist workforce has been one of the major trends shaping US (and international) inpatient medicine over the last 25 years.¹ Hospitalists’ clinical work is typically split among serving as the primary attending for admitted patients (termed “most responsible physician,” or MRP, in Canada), outpatient clinics, medical consults, and comanagement.^2,3 Comanagement typically involves the cooperative efforts of hospitalists and subspecialists ranging from general surgery to orthopedics to medical oncology. Comanagement differs from typical medical consultation because comanaging hospitalists are commonly given broad discretion to directly write orders, manage intercurrent medical illness (eg, hyperglycemia), and even discharge patients from the hospital when appropriate. There can be significant heterogeneity in how comanagement is implemented across institutions.⁴

With respect to hip fractures, literature suggests that subspecialists value comanagement and that comanagement is associated with reductions in hospital length of stay, timelier surgical repair, and potential cost savings for hospitals.^5-7 Some studies have found reductions in in-hospital and 1-year mortality (including one meta-analysis on ortho-geriatric comanagement)⁸ and complications,⁹ but others have found no such benefits.^10,11

In the current issue of the Journal of Hospital Medicine, Maxwell and Mirza used data from the National Surgical Quality Improvement Program (NSQIP) Participant Use Data File (PUF)—specifically, from the Hip Fracture PUF—to investigate the relationship between comanagement and mortality and major morbidity among more than 15,000 patients hospitalized with hip fracture.¹² The investigators did not find that comanagement was associated with a reduction in either morbidity or mortality.

Several factors give gravitas to their analysis. First, the NSQIP PUF is an extremely rigorous data source for evaluating surgical outcomes. Originally developed in the US Veterans Health Administration in the 1980’s to standardize data elements needed for quality improvement and hospital benchmarking, today NSQIP involves more than 600 hospitals in 9 different countries submitting hundreds of thousands of cases annually.¹³ Second, the authors recognized that the comanagement and noncomanagement groups differed substantially and used propensity score matching in an effort to account for these differences. Surprisingly, they found that the comanagement had significantly higher mortality and morbidity than the noncomanagement group, even after propensity score matching.

These results are important in testing the assumption of the inherent “good” of comanagement. Does this study provide definitive evidence that surgical comanagement does not improve outcomes? We would suggest that this study be interpreted in light of certain considerations.

First, comanagement is a broad term including a variety of operationalizations, such as geriatrician vs hospitalist comanagement, involvement before vs after surgery, and varying divisions of responsibility between the surgical and medical services. Research indicates that successful comanagement models tend to incorporate multidisciplinary teams, embrace the “dual primary caregiver” nature of comanagement, and shared goals among primary caregivers, specifically anticipating prevention of complications.⁵ The NSQIP data do not provide sufficient granularity to allow for investigation of these crucial nuances that may ultimately determine whether comanagement programs are effective. Additionally, comanagement often (but not always) coexists with a care pathway, and so deficiencies in or absence of a care pathway add additional heterogeneity to the comanagement group which is not captured in the NSQIP PUF.

Second, it is important to consider the potential for unmeasured confounding. The propensity score matching did seem to achieve balance in the distribution of most baseline variables between the comanagement and noncomanagement groups, though differences remain for certain covariates. A key assumption in propensity score matching (and in observational research more broadly) is the principle of “no unmeasured confounders” (ie, the assumption that all variables that might influence treatment assignment and outcomes are measured).¹⁴ For the NSQIP PUF this absence of unmeasured confounders is clearly not the case because hospital and surgeon variables are omitted from the PUF for reasons of confidentiality. Inclusion of hospital and surgeon variables could well be important because outcomes may vary by hospital or by surgeon, and simultaneously, different hospitals and different surgeons will have different protocols and preferences regarding comanagement. Furthermore, confounding is virtually guaranteed to the extent that hospitals and surgeons do not randomly assign hip fracture patients to comanagement or usual care. The finding of higher mortality in the comanagement group, even after adjustment and matching, suggests the presence of residual confounding. Even if residual confounding is the explanation for the worse outcomes observed in the comanagement group, the finding of a lack of benefit of comanagement is noteworthy and should not be dismissed out of hand.

Limitations aside, these results suggest a need for humility among strong proponents of comanagement, at least in the hip fracture population. While it may still be reasonable to claim that comanagement improves efficiency and may enhance certain aspects of patient or physician satisfaction, the lack of an impact on mortality highlights a need to examine the benefits of these programs more carefully. From a clinical perspective, hospitalists and orthopedic surgeons should consider which hip fracture patients might be most likely to benefit from comanagement.⁴ From a research perspective, the current study highlights the pressing need for a randomized trial of comanagement to definitively address the effectiveness of these programs.

The growth in the hospitalist workforce has been one of the major trends shaping US (and international) inpatient medicine over the last 25 years.¹ Hospitalists’ clinical work is typically split among serving as the primary attending for admitted patients (termed “most responsible physician,” or MRP, in Canada), outpatient clinics, medical consults, and comanagement.^2,3 Comanagement typically involves the cooperative efforts of hospitalists and subspecialists ranging from general surgery to orthopedics to medical oncology. Comanagement differs from typical medical consultation because comanaging hospitalists are commonly given broad discretion to directly write orders, manage intercurrent medical illness (eg, hyperglycemia), and even discharge patients from the hospital when appropriate. There can be significant heterogeneity in how comanagement is implemented across institutions.⁴

With respect to hip fractures, literature suggests that subspecialists value comanagement and that comanagement is associated with reductions in hospital length of stay, timelier surgical repair, and potential cost savings for hospitals.^5-7 Some studies have found reductions in in-hospital and 1-year mortality (including one meta-analysis on ortho-geriatric comanagement)⁸ and complications,⁹ but others have found no such benefits.^10,11

In the current issue of the Journal of Hospital Medicine, Maxwell and Mirza used data from the National Surgical Quality Improvement Program (NSQIP) Participant Use Data File (PUF)—specifically, from the Hip Fracture PUF—to investigate the relationship between comanagement and mortality and major morbidity among more than 15,000 patients hospitalized with hip fracture.¹² The investigators did not find that comanagement was associated with a reduction in either morbidity or mortality.

Several factors give gravitas to their analysis. First, the NSQIP PUF is an extremely rigorous data source for evaluating surgical outcomes. Originally developed in the US Veterans Health Administration in the 1980’s to standardize data elements needed for quality improvement and hospital benchmarking, today NSQIP involves more than 600 hospitals in 9 different countries submitting hundreds of thousands of cases annually.¹³ Second, the authors recognized that the comanagement and noncomanagement groups differed substantially and used propensity score matching in an effort to account for these differences. Surprisingly, they found that the comanagement had significantly higher mortality and morbidity than the noncomanagement group, even after propensity score matching.

These results are important in testing the assumption of the inherent “good” of comanagement. Does this study provide definitive evidence that surgical comanagement does not improve outcomes? We would suggest that this study be interpreted in light of certain considerations.

First, comanagement is a broad term including a variety of operationalizations, such as geriatrician vs hospitalist comanagement, involvement before vs after surgery, and varying divisions of responsibility between the surgical and medical services. Research indicates that successful comanagement models tend to incorporate multidisciplinary teams, embrace the “dual primary caregiver” nature of comanagement, and shared goals among primary caregivers, specifically anticipating prevention of complications.⁵ The NSQIP data do not provide sufficient granularity to allow for investigation of these crucial nuances that may ultimately determine whether comanagement programs are effective. Additionally, comanagement often (but not always) coexists with a care pathway, and so deficiencies in or absence of a care pathway add additional heterogeneity to the comanagement group which is not captured in the NSQIP PUF.

Second, it is important to consider the potential for unmeasured confounding. The propensity score matching did seem to achieve balance in the distribution of most baseline variables between the comanagement and noncomanagement groups, though differences remain for certain covariates. A key assumption in propensity score matching (and in observational research more broadly) is the principle of “no unmeasured confounders” (ie, the assumption that all variables that might influence treatment assignment and outcomes are measured).¹⁴ For the NSQIP PUF this absence of unmeasured confounders is clearly not the case because hospital and surgeon variables are omitted from the PUF for reasons of confidentiality. Inclusion of hospital and surgeon variables could well be important because outcomes may vary by hospital or by surgeon, and simultaneously, different hospitals and different surgeons will have different protocols and preferences regarding comanagement. Furthermore, confounding is virtually guaranteed to the extent that hospitals and surgeons do not randomly assign hip fracture patients to comanagement or usual care. The finding of higher mortality in the comanagement group, even after adjustment and matching, suggests the presence of residual confounding. Even if residual confounding is the explanation for the worse outcomes observed in the comanagement group, the finding of a lack of benefit of comanagement is noteworthy and should not be dismissed out of hand.

Limitations aside, these results suggest a need for humility among strong proponents of comanagement, at least in the hip fracture population. While it may still be reasonable to claim that comanagement improves efficiency and may enhance certain aspects of patient or physician satisfaction, the lack of an impact on mortality highlights a need to examine the benefits of these programs more carefully. From a clinical perspective, hospitalists and orthopedic surgeons should consider which hip fracture patients might be most likely to benefit from comanagement.⁴ From a research perspective, the current study highlights the pressing need for a randomized trial of comanagement to definitively address the effectiveness of these programs.

The growth in the hospitalist workforce has been one of the major trends shaping US (and international) inpatient medicine over the last 25 years.¹ Hospitalists’ clinical work is typically split among serving as the primary attending for admitted patients (termed “most responsible physician,” or MRP, in Canada), outpatient clinics, medical consults, and comanagement.^2,3 Comanagement typically involves the cooperative efforts of hospitalists and subspecialists ranging from general surgery to orthopedics to medical oncology. Comanagement differs from typical medical consultation because comanaging hospitalists are commonly given broad discretion to directly write orders, manage intercurrent medical illness (eg, hyperglycemia), and even discharge patients from the hospital when appropriate. There can be significant heterogeneity in how comanagement is implemented across institutions.⁴

With respect to hip fractures, literature suggests that subspecialists value comanagement and that comanagement is associated with reductions in hospital length of stay, timelier surgical repair, and potential cost savings for hospitals.^5-7 Some studies have found reductions in in-hospital and 1-year mortality (including one meta-analysis on ortho-geriatric comanagement)⁸ and complications,⁹ but others have found no such benefits.^10,11

In the current issue of the Journal of Hospital Medicine, Maxwell and Mirza used data from the National Surgical Quality Improvement Program (NSQIP) Participant Use Data File (PUF)—specifically, from the Hip Fracture PUF—to investigate the relationship between comanagement and mortality and major morbidity among more than 15,000 patients hospitalized with hip fracture.¹² The investigators did not find that comanagement was associated with a reduction in either morbidity or mortality.

Several factors give gravitas to their analysis. First, the NSQIP PUF is an extremely rigorous data source for evaluating surgical outcomes. Originally developed in the US Veterans Health Administration in the 1980’s to standardize data elements needed for quality improvement and hospital benchmarking, today NSQIP involves more than 600 hospitals in 9 different countries submitting hundreds of thousands of cases annually.¹³ Second, the authors recognized that the comanagement and noncomanagement groups differed substantially and used propensity score matching in an effort to account for these differences. Surprisingly, they found that the comanagement had significantly higher mortality and morbidity than the noncomanagement group, even after propensity score matching.

These results are important in testing the assumption of the inherent “good” of comanagement. Does this study provide definitive evidence that surgical comanagement does not improve outcomes? We would suggest that this study be interpreted in light of certain considerations.

First, comanagement is a broad term including a variety of operationalizations, such as geriatrician vs hospitalist comanagement, involvement before vs after surgery, and varying divisions of responsibility between the surgical and medical services. Research indicates that successful comanagement models tend to incorporate multidisciplinary teams, embrace the “dual primary caregiver” nature of comanagement, and shared goals among primary caregivers, specifically anticipating prevention of complications.⁵ The NSQIP data do not provide sufficient granularity to allow for investigation of these crucial nuances that may ultimately determine whether comanagement programs are effective. Additionally, comanagement often (but not always) coexists with a care pathway, and so deficiencies in or absence of a care pathway add additional heterogeneity to the comanagement group which is not captured in the NSQIP PUF.

Second, it is important to consider the potential for unmeasured confounding. The propensity score matching did seem to achieve balance in the distribution of most baseline variables between the comanagement and noncomanagement groups, though differences remain for certain covariates. A key assumption in propensity score matching (and in observational research more broadly) is the principle of “no unmeasured confounders” (ie, the assumption that all variables that might influence treatment assignment and outcomes are measured).¹⁴ For the NSQIP PUF this absence of unmeasured confounders is clearly not the case because hospital and surgeon variables are omitted from the PUF for reasons of confidentiality. Inclusion of hospital and surgeon variables could well be important because outcomes may vary by hospital or by surgeon, and simultaneously, different hospitals and different surgeons will have different protocols and preferences regarding comanagement. Furthermore, confounding is virtually guaranteed to the extent that hospitals and surgeons do not randomly assign hip fracture patients to comanagement or usual care. The finding of higher mortality in the comanagement group, even after adjustment and matching, suggests the presence of residual confounding. Even if residual confounding is the explanation for the worse outcomes observed in the comanagement group, the finding of a lack of benefit of comanagement is noteworthy and should not be dismissed out of hand.

Limitations aside, these results suggest a need for humility among strong proponents of comanagement, at least in the hip fracture population. While it may still be reasonable to claim that comanagement improves efficiency and may enhance certain aspects of patient or physician satisfaction, the lack of an impact on mortality highlights a need to examine the benefits of these programs more carefully. From a clinical perspective, hospitalists and orthopedic surgeons should consider which hip fracture patients might be most likely to benefit from comanagement.⁴ From a research perspective, the current study highlights the pressing need for a randomized trial of comanagement to definitively address the effectiveness of these programs.

Medicare beneficiary who requires skilled care in a nursing home? Better be admitted for at least 3 days in the hospital first if you want the nursing home paid for. Govt doesn’t always make sense. We’re listening to feedback.

—Centers for Medicare & Medicaid Services Administrator Seema Verma, @SeemaCMS, August 4, 2019, via Twitter.¹

On March 13, 2020, the president of the United States declared a national health emergency, granting the secretary of the United States Department of Health & Human Services authority to grant waivers intended to ease certain Medicare and Medicaid program requirements.² Broad waiver categories include those that may be requested by an individual institution, as well as “COVID-19 Emergency Declaration Blanket Waivers,” which automatically apply across all facilities and providers. As stated by the Centers for Medicare & Medicaid Services (CMS), waivers are intended to create “regulatory flexibilities to help healthcare providers contain the spread of 2019 Novel Coronavirus Disease (COVID-19).” These provisions are retroactive to March 1, 2020, expire at the end of the “emergency period or 60 days from the date the waiver . . . is first published” and can be extended by the secretary.²

The issued blanket waivers remove administrative requirements in a wide range of care settings including home health, hospice, hospitals, and skilled nursing facilities (SNF), among others. The waiving of many of these administrative requirements are welcomed by providers and administrators alike in this time of national crisis. For example, relaxation of verbal order signage requirements and expanded coverage of telehealth will, almost certainly, improve accessibility, efficiency, and requisite coordination and care across settings. Emergence of these new “COVID-19” waivers also present rare and valuable opportunities to examine care improvement in areas long believed to need permanent regulatory change. Perhaps the most important of these long over-due changes is the current CMS process for determining Part A eligibility for post-acute skilled nursing facility coverage for traditional Medicare beneficiaries following an inpatient hospitalization. Under COVID-19, CMS has now granted a waiver that “authorizes the Secretary to provide for Skilled Nursing Facilities (SNF) coverage in the absence of a qualifying [three consecutive inpatient midnight] hospital stay. . . .”² Although demand for SNF placement may shift during the pandemic, hospitals facing capacity issues will more easily be able to discharge Medicare beneficiaries ready for post-acute care.

When Medicare was established in 1965, approximately half of Americans over age 65 did not have health insurance, and older adults were the most likely demographic to be living in poverty.³ Originally called “Hospital Insurance” or “Medicare Part A,” these “Inpatient Hospital Services” are described in Social Security statute as “items and services furnished to an inpatient of a hospital” including room and board, nursing services, pharmaceuticals, and medical and surgical services delivered in the hospital.⁴ In 1967, Medicare beneficiaries staying three consecutive inpatient hospital midnights were also afforded post-acute SNF coverage for up to 100 days. As expected, hospital use increased as seniors had coverage for hospital care and were also, in many cases, able to access higher quality post-hospital care.⁵

Over the past 50 years, two important changes have shifted Medicare beneficiary SNF coverage. First, due to efficiencies and changes in care delivery, average length of hospital stay for Americans over age 65 has shrunk from 14 days in 1965 to approximately 5 days currently.^5,6 Now, fewer beneficiaries spend the necessary three or more nights in the hospital to qualify for post-acute SNF coverage. Second, and most importantly, CMS created “observation status” in the 1980s, which allowed for patients to be observed as “outpatients” in a hospital instead of as inpatients. Notably, these observation nights fall under outpatient status (Part B), and therefore do not count toward the statutory SNF coverage requirement of three inpatient midnights.

According to CMS, observation should be used so that a “decision can be made regarding whether patients will require further treatment as hospital inpatients or if they are able to be discharged from the hospital. . . . In the majority of cases, the decision can be made in less than 48 hours, usually in less than 24 hours.”⁷ At the time of its development, this concept fit the growing use of Emergency Department observation units, in which patients presented for an acute issue but could usually discharge home in the stated time frame.

In reality, outpatient (observation) status is not synonymous with observation units. Because observation is a billing determination, not a specific type of clinical care, observation care may be delivered anywhere in a hospital—including an observation unit, a hospital ward, or even an intensive care unit (ICU). While all hospitals may deliver observation care, only about one-third of hospitals have observation units, and even hospitals with observation units deliver observation care outside of these units. Traditional Medicare beneficiaries who stay three or more nights in the hospital but cannot meet the three inpatient midnight requirement to access their SNF coverage benefits because of outpatient (observation) nights are often left vulnerable and confused, saddling them with an average of $10,503 for each uncovered SNF stay.⁸ As emergent evidence demonstrates striking racial, geographic, and socioeconomic-based health disparities in COVID-19, renewal of the “three-midnight rule” could have disproportionate and long-lasting ramifications for these populations in particular.⁹

Hospital observation stays (or observation nights) can look identical to inpatient hospital stays, as defined by the Social Security statute⁴; yet never count toward the three-inpatient-midnight tally. In 2014, the Office of Inspector General (OIG) found there were 633,148 hospital stays that lasted three midnights or longer but did not contain three consecutive inpatient midnights, which resulted in nonqualifying stays for purposes of SNF coverage, if that coverage was needed.¹⁰ A more recent OIG report found that Medicare was paying erroneously for some SNF stays because even CMS could not distinguish between three midnights that were all inpatient or a combination of inpatient and observation.¹¹ Additionally, because care provided is often indistinguishable, status changes between outpatient and inpatient are common; in 2014, 40% of Medicare observation stays occurring within 30 days of an inpatient stay changed to inpatient over the course of a single hospitalization.¹² Now, in the time of COVID-19, this untenable decades-long problem has the potential to be definitively addressed by a permanent removal of the three midnight requirement altogether.

PROGRESS TOWARD REFORM

Several recent signals suggest that change is supported by a diverse group of stakeholders. In their 2019 Top 25 Unimplemented Recommendations, the OIG acknowledged the similarity in observation and inpatient care, recommending that “CMS . . . analyze the potential impacts of counting time spent as an outpatient toward the 3-night requirement for skilled nursing facility (SNF) services so that beneficiaries receiving similar hospital care have similar access to these services.”¹³ The “Improving Access to Medicare Coverage Act of 2019,” reintroduced in the 116th Congress, would count all midnights spent in the hospital, whether those nights are inpatient or observation, toward the three midnight requirement.¹⁴ This bill has bipartisan, bicameral support, which demonstrates unified legislative interest across the political spectrum. More recently in March 2020, a federal judge in the class action lawsuit Alexander v Azar determined that Medicare beneficiaries had the right to appeal to Medicare if a physician placed a patient in inpatient status and this decision was overturned administratively by a hospital, resulting in loss of a beneficiary’s SNF coverage.¹⁵ Although now under appeal, this judicial decision signals the importance of beneficiary rights to appeal directly to CMS.

Given the mounting support for reform, it is probable that cost concerns and allocation of resources to the Part A vs Part B “buckets” remain the only barrier to permanently reforming the three-midnight inpatient stay policy. Pilot programs testing Medicare SNF waivers more than 30 years ago suggested increased cost and SNF usage.¹⁶ However, more contemporary experience from Medicare Advantage programs suggest just the opposite. Grebla et al showed there was no increased SNF use nor SNF length of stay for beneficiaries in Medicare Advantage plans that waived the three inpatient midnight requirement.¹⁷

Arguably, the current COVID-19 emergency blanket SNF waiver is not a perfect test of short- or long-term Medicare costs. First, factors such as reduced hospital elective surgeries that may typically drive post-acute SNF admissions, as well as potentially reduced SNF utilization caused by fear of COVID-19 outbreaks, may temporarily lower SNF use and associated Medicare expenditures. The existing waiver of statute is also financially constrained, stipulating that “this action does not increase overall program payments. . . .”² Longer term, innovations in care delivery prompted by accelerated telehealth reforms may shift more post-acute care from SNFs to the home setting, changing patterns of SNF utilization altogether. Despite these limitations, this regulatory relief will still provide valuable utilization and cost information on SNF use under a system absent the three-midnight requirement.

Rarely, if ever, does a national healthcare system experience such a rapid and marked change as that seen with the COVID-19 pandemic. Despite the tragic emergency circumstances prompting CMS’s blanket waivers, it provides CMS and stakeholders with a rare opportunity to evaluate potential improvements revealed by each individual aspect of COVID-19 regulatory relief. CMS has in the past argued the three-midnight SNF requirement is a statutory issue and thus not within their control, yet they have used their regulatory authority to waive this policy to facilitate efficient care in a national health crisis. This is a change that many believe is long overdue, and one that should be maintained even after COVID-19 abates. “Govt doesn’t always make sense,” as Administrator Verma wrote,¹ should be a cry for government to make better sense of existing legislation and regulation. Reform of the three-midnight inpatient rule is the right place to start.

Medicare beneficiary who requires skilled care in a nursing home? Better be admitted for at least 3 days in the hospital first if you want the nursing home paid for. Govt doesn’t always make sense. We’re listening to feedback.

—Centers for Medicare & Medicaid Services Administrator Seema Verma, @SeemaCMS, August 4, 2019, via Twitter.¹

On March 13, 2020, the president of the United States declared a national health emergency, granting the secretary of the United States Department of Health & Human Services authority to grant waivers intended to ease certain Medicare and Medicaid program requirements.² Broad waiver categories include those that may be requested by an individual institution, as well as “COVID-19 Emergency Declaration Blanket Waivers,” which automatically apply across all facilities and providers. As stated by the Centers for Medicare & Medicaid Services (CMS), waivers are intended to create “regulatory flexibilities to help healthcare providers contain the spread of 2019 Novel Coronavirus Disease (COVID-19).” These provisions are retroactive to March 1, 2020, expire at the end of the “emergency period or 60 days from the date the waiver . . . is first published” and can be extended by the secretary.²

The issued blanket waivers remove administrative requirements in a wide range of care settings including home health, hospice, hospitals, and skilled nursing facilities (SNF), among others. The waiving of many of these administrative requirements are welcomed by providers and administrators alike in this time of national crisis. For example, relaxation of verbal order signage requirements and expanded coverage of telehealth will, almost certainly, improve accessibility, efficiency, and requisite coordination and care across settings. Emergence of these new “COVID-19” waivers also present rare and valuable opportunities to examine care improvement in areas long believed to need permanent regulatory change. Perhaps the most important of these long over-due changes is the current CMS process for determining Part A eligibility for post-acute skilled nursing facility coverage for traditional Medicare beneficiaries following an inpatient hospitalization. Under COVID-19, CMS has now granted a waiver that “authorizes the Secretary to provide for Skilled Nursing Facilities (SNF) coverage in the absence of a qualifying [three consecutive inpatient midnight] hospital stay. . . .”² Although demand for SNF placement may shift during the pandemic, hospitals facing capacity issues will more easily be able to discharge Medicare beneficiaries ready for post-acute care.

When Medicare was established in 1965, approximately half of Americans over age 65 did not have health insurance, and older adults were the most likely demographic to be living in poverty.³ Originally called “Hospital Insurance” or “Medicare Part A,” these “Inpatient Hospital Services” are described in Social Security statute as “items and services furnished to an inpatient of a hospital” including room and board, nursing services, pharmaceuticals, and medical and surgical services delivered in the hospital.⁴ In 1967, Medicare beneficiaries staying three consecutive inpatient hospital midnights were also afforded post-acute SNF coverage for up to 100 days. As expected, hospital use increased as seniors had coverage for hospital care and were also, in many cases, able to access higher quality post-hospital care.⁵

Over the past 50 years, two important changes have shifted Medicare beneficiary SNF coverage. First, due to efficiencies and changes in care delivery, average length of hospital stay for Americans over age 65 has shrunk from 14 days in 1965 to approximately 5 days currently.^5,6 Now, fewer beneficiaries spend the necessary three or more nights in the hospital to qualify for post-acute SNF coverage. Second, and most importantly, CMS created “observation status” in the 1980s, which allowed for patients to be observed as “outpatients” in a hospital instead of as inpatients. Notably, these observation nights fall under outpatient status (Part B), and therefore do not count toward the statutory SNF coverage requirement of three inpatient midnights.

According to CMS, observation should be used so that a “decision can be made regarding whether patients will require further treatment as hospital inpatients or if they are able to be discharged from the hospital. . . . In the majority of cases, the decision can be made in less than 48 hours, usually in less than 24 hours.”⁷ At the time of its development, this concept fit the growing use of Emergency Department observation units, in which patients presented for an acute issue but could usually discharge home in the stated time frame.

In reality, outpatient (observation) status is not synonymous with observation units. Because observation is a billing determination, not a specific type of clinical care, observation care may be delivered anywhere in a hospital—including an observation unit, a hospital ward, or even an intensive care unit (ICU). While all hospitals may deliver observation care, only about one-third of hospitals have observation units, and even hospitals with observation units deliver observation care outside of these units. Traditional Medicare beneficiaries who stay three or more nights in the hospital but cannot meet the three inpatient midnight requirement to access their SNF coverage benefits because of outpatient (observation) nights are often left vulnerable and confused, saddling them with an average of $10,503 for each uncovered SNF stay.⁸ As emergent evidence demonstrates striking racial, geographic, and socioeconomic-based health disparities in COVID-19, renewal of the “three-midnight rule” could have disproportionate and long-lasting ramifications for these populations in particular.⁹

Hospital observation stays (or observation nights) can look identical to inpatient hospital stays, as defined by the Social Security statute⁴; yet never count toward the three-inpatient-midnight tally. In 2014, the Office of Inspector General (OIG) found there were 633,148 hospital stays that lasted three midnights or longer but did not contain three consecutive inpatient midnights, which resulted in nonqualifying stays for purposes of SNF coverage, if that coverage was needed.¹⁰ A more recent OIG report found that Medicare was paying erroneously for some SNF stays because even CMS could not distinguish between three midnights that were all inpatient or a combination of inpatient and observation.¹¹ Additionally, because care provided is often indistinguishable, status changes between outpatient and inpatient are common; in 2014, 40% of Medicare observation stays occurring within 30 days of an inpatient stay changed to inpatient over the course of a single hospitalization.¹² Now, in the time of COVID-19, this untenable decades-long problem has the potential to be definitively addressed by a permanent removal of the three midnight requirement altogether.

PROGRESS TOWARD REFORM

Several recent signals suggest that change is supported by a diverse group of stakeholders. In their 2019 Top 25 Unimplemented Recommendations, the OIG acknowledged the similarity in observation and inpatient care, recommending that “CMS . . . analyze the potential impacts of counting time spent as an outpatient toward the 3-night requirement for skilled nursing facility (SNF) services so that beneficiaries receiving similar hospital care have similar access to these services.”¹³ The “Improving Access to Medicare Coverage Act of 2019,” reintroduced in the 116th Congress, would count all midnights spent in the hospital, whether those nights are inpatient or observation, toward the three midnight requirement.¹⁴ This bill has bipartisan, bicameral support, which demonstrates unified legislative interest across the political spectrum. More recently in March 2020, a federal judge in the class action lawsuit Alexander v Azar determined that Medicare beneficiaries had the right to appeal to Medicare if a physician placed a patient in inpatient status and this decision was overturned administratively by a hospital, resulting in loss of a beneficiary’s SNF coverage.¹⁵ Although now under appeal, this judicial decision signals the importance of beneficiary rights to appeal directly to CMS.

Given the mounting support for reform, it is probable that cost concerns and allocation of resources to the Part A vs Part B “buckets” remain the only barrier to permanently reforming the three-midnight inpatient stay policy. Pilot programs testing Medicare SNF waivers more than 30 years ago suggested increased cost and SNF usage.¹⁶ However, more contemporary experience from Medicare Advantage programs suggest just the opposite. Grebla et al showed there was no increased SNF use nor SNF length of stay for beneficiaries in Medicare Advantage plans that waived the three inpatient midnight requirement.¹⁷

Arguably, the current COVID-19 emergency blanket SNF waiver is not a perfect test of short- or long-term Medicare costs. First, factors such as reduced hospital elective surgeries that may typically drive post-acute SNF admissions, as well as potentially reduced SNF utilization caused by fear of COVID-19 outbreaks, may temporarily lower SNF use and associated Medicare expenditures. The existing waiver of statute is also financially constrained, stipulating that “this action does not increase overall program payments. . . .”² Longer term, innovations in care delivery prompted by accelerated telehealth reforms may shift more post-acute care from SNFs to the home setting, changing patterns of SNF utilization altogether. Despite these limitations, this regulatory relief will still provide valuable utilization and cost information on SNF use under a system absent the three-midnight requirement.

Rarely, if ever, does a national healthcare system experience such a rapid and marked change as that seen with the COVID-19 pandemic. Despite the tragic emergency circumstances prompting CMS’s blanket waivers, it provides CMS and stakeholders with a rare opportunity to evaluate potential improvements revealed by each individual aspect of COVID-19 regulatory relief. CMS has in the past argued the three-midnight SNF requirement is a statutory issue and thus not within their control, yet they have used their regulatory authority to waive this policy to facilitate efficient care in a national health crisis. This is a change that many believe is long overdue, and one that should be maintained even after COVID-19 abates. “Govt doesn’t always make sense,” as Administrator Verma wrote,¹ should be a cry for government to make better sense of existing legislation and regulation. Reform of the three-midnight inpatient rule is the right place to start.

Medicare beneficiary who requires skilled care in a nursing home? Better be admitted for at least 3 days in the hospital first if you want the nursing home paid for. Govt doesn’t always make sense. We’re listening to feedback.

—Centers for Medicare & Medicaid Services Administrator Seema Verma, @SeemaCMS, August 4, 2019, via Twitter.¹

On March 13, 2020, the president of the United States declared a national health emergency, granting the secretary of the United States Department of Health & Human Services authority to grant waivers intended to ease certain Medicare and Medicaid program requirements.² Broad waiver categories include those that may be requested by an individual institution, as well as “COVID-19 Emergency Declaration Blanket Waivers,” which automatically apply across all facilities and providers. As stated by the Centers for Medicare & Medicaid Services (CMS), waivers are intended to create “regulatory flexibilities to help healthcare providers contain the spread of 2019 Novel Coronavirus Disease (COVID-19).” These provisions are retroactive to March 1, 2020, expire at the end of the “emergency period or 60 days from the date the waiver . . . is first published” and can be extended by the secretary.²

The issued blanket waivers remove administrative requirements in a wide range of care settings including home health, hospice, hospitals, and skilled nursing facilities (SNF), among others. The waiving of many of these administrative requirements are welcomed by providers and administrators alike in this time of national crisis. For example, relaxation of verbal order signage requirements and expanded coverage of telehealth will, almost certainly, improve accessibility, efficiency, and requisite coordination and care across settings. Emergence of these new “COVID-19” waivers also present rare and valuable opportunities to examine care improvement in areas long believed to need permanent regulatory change. Perhaps the most important of these long over-due changes is the current CMS process for determining Part A eligibility for post-acute skilled nursing facility coverage for traditional Medicare beneficiaries following an inpatient hospitalization. Under COVID-19, CMS has now granted a waiver that “authorizes the Secretary to provide for Skilled Nursing Facilities (SNF) coverage in the absence of a qualifying [three consecutive inpatient midnight] hospital stay. . . .”² Although demand for SNF placement may shift during the pandemic, hospitals facing capacity issues will more easily be able to discharge Medicare beneficiaries ready for post-acute care.

When Medicare was established in 1965, approximately half of Americans over age 65 did not have health insurance, and older adults were the most likely demographic to be living in poverty.³ Originally called “Hospital Insurance” or “Medicare Part A,” these “Inpatient Hospital Services” are described in Social Security statute as “items and services furnished to an inpatient of a hospital” including room and board, nursing services, pharmaceuticals, and medical and surgical services delivered in the hospital.⁴ In 1967, Medicare beneficiaries staying three consecutive inpatient hospital midnights were also afforded post-acute SNF coverage for up to 100 days. As expected, hospital use increased as seniors had coverage for hospital care and were also, in many cases, able to access higher quality post-hospital care.⁵

Over the past 50 years, two important changes have shifted Medicare beneficiary SNF coverage. First, due to efficiencies and changes in care delivery, average length of hospital stay for Americans over age 65 has shrunk from 14 days in 1965 to approximately 5 days currently.^5,6 Now, fewer beneficiaries spend the necessary three or more nights in the hospital to qualify for post-acute SNF coverage. Second, and most importantly, CMS created “observation status” in the 1980s, which allowed for patients to be observed as “outpatients” in a hospital instead of as inpatients. Notably, these observation nights fall under outpatient status (Part B), and therefore do not count toward the statutory SNF coverage requirement of three inpatient midnights.

According to CMS, observation should be used so that a “decision can be made regarding whether patients will require further treatment as hospital inpatients or if they are able to be discharged from the hospital. . . . In the majority of cases, the decision can be made in less than 48 hours, usually in less than 24 hours.”⁷ At the time of its development, this concept fit the growing use of Emergency Department observation units, in which patients presented for an acute issue but could usually discharge home in the stated time frame.

In reality, outpatient (observation) status is not synonymous with observation units. Because observation is a billing determination, not a specific type of clinical care, observation care may be delivered anywhere in a hospital—including an observation unit, a hospital ward, or even an intensive care unit (ICU). While all hospitals may deliver observation care, only about one-third of hospitals have observation units, and even hospitals with observation units deliver observation care outside of these units. Traditional Medicare beneficiaries who stay three or more nights in the hospital but cannot meet the three inpatient midnight requirement to access their SNF coverage benefits because of outpatient (observation) nights are often left vulnerable and confused, saddling them with an average of $10,503 for each uncovered SNF stay.⁸ As emergent evidence demonstrates striking racial, geographic, and socioeconomic-based health disparities in COVID-19, renewal of the “three-midnight rule” could have disproportionate and long-lasting ramifications for these populations in particular.⁹

Hospital observation stays (or observation nights) can look identical to inpatient hospital stays, as defined by the Social Security statute⁴; yet never count toward the three-inpatient-midnight tally. In 2014, the Office of Inspector General (OIG) found there were 633,148 hospital stays that lasted three midnights or longer but did not contain three consecutive inpatient midnights, which resulted in nonqualifying stays for purposes of SNF coverage, if that coverage was needed.¹⁰ A more recent OIG report found that Medicare was paying erroneously for some SNF stays because even CMS could not distinguish between three midnights that were all inpatient or a combination of inpatient and observation.¹¹ Additionally, because care provided is often indistinguishable, status changes between outpatient and inpatient are common; in 2014, 40% of Medicare observation stays occurring within 30 days of an inpatient stay changed to inpatient over the course of a single hospitalization.¹² Now, in the time of COVID-19, this untenable decades-long problem has the potential to be definitively addressed by a permanent removal of the three midnight requirement altogether.

PROGRESS TOWARD REFORM

Several recent signals suggest that change is supported by a diverse group of stakeholders. In their 2019 Top 25 Unimplemented Recommendations, the OIG acknowledged the similarity in observation and inpatient care, recommending that “CMS . . . analyze the potential impacts of counting time spent as an outpatient toward the 3-night requirement for skilled nursing facility (SNF) services so that beneficiaries receiving similar hospital care have similar access to these services.”¹³ The “Improving Access to Medicare Coverage Act of 2019,” reintroduced in the 116th Congress, would count all midnights spent in the hospital, whether those nights are inpatient or observation, toward the three midnight requirement.¹⁴ This bill has bipartisan, bicameral support, which demonstrates unified legislative interest across the political spectrum. More recently in March 2020, a federal judge in the class action lawsuit Alexander v Azar determined that Medicare beneficiaries had the right to appeal to Medicare if a physician placed a patient in inpatient status and this decision was overturned administratively by a hospital, resulting in loss of a beneficiary’s SNF coverage.¹⁵ Although now under appeal, this judicial decision signals the importance of beneficiary rights to appeal directly to CMS.

Given the mounting support for reform, it is probable that cost concerns and allocation of resources to the Part A vs Part B “buckets” remain the only barrier to permanently reforming the three-midnight inpatient stay policy. Pilot programs testing Medicare SNF waivers more than 30 years ago suggested increased cost and SNF usage.¹⁶ However, more contemporary experience from Medicare Advantage programs suggest just the opposite. Grebla et al showed there was no increased SNF use nor SNF length of stay for beneficiaries in Medicare Advantage plans that waived the three inpatient midnight requirement.¹⁷

Arguably, the current COVID-19 emergency blanket SNF waiver is not a perfect test of short- or long-term Medicare costs. First, factors such as reduced hospital elective surgeries that may typically drive post-acute SNF admissions, as well as potentially reduced SNF utilization caused by fear of COVID-19 outbreaks, may temporarily lower SNF use and associated Medicare expenditures. The existing waiver of statute is also financially constrained, stipulating that “this action does not increase overall program payments. . . .”² Longer term, innovations in care delivery prompted by accelerated telehealth reforms may shift more post-acute care from SNFs to the home setting, changing patterns of SNF utilization altogether. Despite these limitations, this regulatory relief will still provide valuable utilization and cost information on SNF use under a system absent the three-midnight requirement.

Rarely, if ever, does a national healthcare system experience such a rapid and marked change as that seen with the COVID-19 pandemic. Despite the tragic emergency circumstances prompting CMS’s blanket waivers, it provides CMS and stakeholders with a rare opportunity to evaluate potential improvements revealed by each individual aspect of COVID-19 regulatory relief. CMS has in the past argued the three-midnight SNF requirement is a statutory issue and thus not within their control, yet they have used their regulatory authority to waive this policy to facilitate efficient care in a national health crisis. This is a change that many believe is long overdue, and one that should be maintained even after COVID-19 abates. “Govt doesn’t always make sense,” as Administrator Verma wrote,¹ should be a cry for government to make better sense of existing legislation and regulation. Reform of the three-midnight inpatient rule is the right place to start.

Coronavirus disease 2019 (COVID-19) is the most important public health emergency of the 21st century. The pandemic has devastated New York City, where over 17,000 confirmed deaths have occurred as of June 5, 2020.¹ The most common cause of death in COVID-19 patients is respiratory failure from acute respiratory distress syndrome (ARDS). A recent study reported high mortality rates among COVID-19 patients who received mechanical ventilation (MV).²

Glucocorticoids are useful as adjunctive treatment for some infections with inflammatory responses, but their efficacy in COVID-19 is unclear. Prior experience with influenza and other coronaviruses may be relevant. A recent meta-analysis of influenza pneumonia showed increased mortality and a higher rate of secondary infections in patients who were administered glucocorticoids.³ For Middle East respiratory syndrome, severe acute respiratory syndrome, and influenza, some studies have demonstrated an association between glucocorticoid use and delayed viral clearance.^4-7 However, a recent retrospective series of patients with COVID-19 and ARDS demonstrated a decrease in mortality with glucocorticoid use.⁸ Glucocorticoids are easily obtained and familiar to providers caring for COVID-19 patients. Hence their empiric use is widespread.^8,9

The primary goal of this study was to determine whether early glucocorticoid treatment is associated with reduced mortality or need for MV in COVID-19 patients.

Study Setting and Overview

Montefiore Medical Center comprises four hospitals totaling 1,536 beds in the Bronx borough of New York, New York. Based upon early experience, some clinicians began prescribing systemic glucocorticoids to patients with COVID-19 while others did not. We leveraged this variation in practice to examine the effectiveness of glucocorticoids in reducing mortality and the rate of MV in hospitalized COVID-19 patients.

Study Populations

There were 2,998 patients admitted with a positive COVID-19 test between March 11, 2020, and April 13, 2020. An a priori decision was made to include all hospitalized COVID-19 patients, including children. Because the outcomes of in-hospital mortality and in-hospital MV cannot be assessed in patients still hospitalized, we included only patients who either died or had been discharged from the hospital. Patients who died or were placed on MV within the first 48 hours of admission were excluded because outcome events occurred before having the opportunity for glucocorticoid treatment. To ensure treatment preceded outcome measurement, we included only patients treated with glucocorticoids within the first 48 hours of admission (treatment group) and compared them with patients never treated with glucocorticoids (control group).

Outcomes and Independent Variables

The primary outcome was a composite of in-hospital mortality or in-hospital MV. Secondary outcomes were the components of the primary. Timing of MV was determined using the first documentation of a ventilator respiratory rate or tidal volume. The independent variable of interest was treatment with glucocorticoids within the first 48 hours of admission. Formulations included are described in the Appendix.

To compare treatment and control groups and to perform adjusted analyses, we also examined the demographic and clinical characteristics, comorbidities, and laboratory values of each admission. For the comparison of study populations, missing values for each variable were ignored. In the primary (unstratified) multivariable analysis, continuous variables were categorized, with missing values assumed to be normal when used as an adjustment variable. All variables extracted, number of missing values, candidates for inclusion in the multivariable analysis, and those that fell out of the model are presented in the Appendix. Several subgroup analyses were predefined including age, diabetes, admission glucose, C-reactive protein (CRP), D-dimer, and troponin T levels.

Statistical Analysis

The treated and control groups were compared with respect to demographics, clinical characteristics, comorbidities, and laboratory values. Primary and secondary outcomes in the groups were compared in unadjusted and adjusted analyses using univariable and multivariable logistic regression models. All patient characteristics that were candidates for inclusion in the adjustment models are listed in the Appendix. Variables were included in the final model if they were associated with the primary outcome (Wald test P < .20) in univariable regression. A sensitivity analysis excluded all variables missing greater than 10% of data, including CRP. Interactions between treatment and six predefined subgroups were tested using logistic regression with interaction terms (eg, [steroids]*[age]). Stratified logistic regression was used to test the association between treatment and the primary outcome in each of the predefined subgroups. Patients who were missing CRP were excluded from the stratified analysis. Because a significant interaction between treatment and initial CRP level was discovered, we undertook a post hoc adjusted analysis within each of the 15 predefined subgroup variables. Because there were fewer outcome events in each subgroup, we constructed a parsimonious logistic regression model that included all variables independently associated with the exposure (P < .05). The same seven adjustment variables were used in each of the predefined subgroups. The study was approved by the Albert Einstein College of Medicine Institutional Review Board. Stata 15.1 software (StataCorp) was used for data analysis.

Of 2,998 patients examined, 1,806 met inclusion criteria and included 140 (7.7%) treated with glucocorticoids within 48 hours of admission and 1,666 who never received glucocorticoids. Reasons for exclusion of 1,192 patients are provided in the Appendix. Among patients who remained hospitalized and were excluded, 169 of 962 (17.6%) received glucocorticoids. Characteristics of the study population are presented in Table 1. Treatment and control groups were similar except that glucocorticoid-treated patients were more likely to have chronic obstructive pulmonary disease (COPD), asthma, rheumatoid arthritis or lupus, or to have received glucocorticoids in the year prior to admission.

There were 318 who met the primary outcome of death or mechanical ventilation, 270 of whom died and 135 of whom required mechanical ventilation. Overall, early use of glucocorticoids was not associated with in-hospital mortality or MV as a composite outcome or as separate outcomes in both unadjusted and adjusted models (Table 2A). However, there was significant heterogeneity of treatment effect in the subgroups defined by CRP levels (P for interaction = .008; Figure). Early glucocorticoid use and an initial CRP of 20 mg/dL or higher was associated with a significantly reduced risk of mortality or MV in unadjusted (odds ratio, 0.23; 95% CI, 0.08-0.70) and adjusted (aOR, 0.20; 95% CI, 0.06-0.67) analyses (Table 2B). Conversely, glucocorticoid treatment in patients with CRP levels less than 10 mg/dL was associated with a significantly increased risk of mortality or MV in unadjusted (OR_, 2.64; 95% CI, 1.39-5.03) and adjusted (aOR_, 3.14; 95% CI, 1.52-6.50) analyses.

The results of this study indicate that early treatment with glucocorticoids is not associated with mortality or need for MV in unselected patients with COVID-19. Subgroup analyses suggest that glucocorticoid-treated patients with markedly elevated CRP may benefit from glucocorticoid treatment, whereas those patients with lower CRP may be harmed. Our findings were consistent after adjustment for clinical characteristics. The public health implications of these findings are hard to overestimate. Given the global growth of the pandemic and that glucocorticoids are widely available and inexpensive, glucocorticoid therapy may save many thousands of lives. Equally important because we have been able to identify a group that may be harmed, some patients may be saved because glucocorticoids will not be given.

Our study reaffirms the finding of the as yet unpublished Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial that there is a subset of patients with COVID-19 who benefit from treatment with glucocorticoids.¹⁰ Our study extends the findings of the RECOVERY trial in two important ways. First, in addition to finding some patients who may benefit, we also have identified patient groups that may experience harm from treatment with glucocorticoids. This finding suggests choosing the right patients for glucocorticoid treatment is critical to maximize the likelihood of benefit and minimize the risk of harm. Second, we have identified patient groups who are likely to benefit (or be harmed) on the basis of a widely available lab test (CRP).

Our results are also consistent with previous studies of patients with SARS-CoV and MERS-CoV, in which no associations between glucocorticoid treatment and mortality were found.⁷ However, the results of studies examining the effect of glucocorticoids in patients with COVID-19 are less consistent.^8,11,12

Few of the previous studies examined the effects of glucocorticoids in subgroups of patients. In our study, the improved outcomes associated with glucocorticoid use in patients with elevated CRPs is intriguing and may be clinically important. Proinflammatory cytokines, especially interleukin-6, acutely increase CRP levels. Cytokine storm syndrome (CSS) is a hyperinflammatory condition that occurs in a subset of COVID-19 patients, often resulting in multiorgan dysfunction.¹³ CRP is markedly elevated in CSS,¹⁴ and improved outcomes with glucocorticoid therapy in this subgroup may indicate benefit in this inflammatory phenotype. Patients with lower CRP are less likely to have CSS and may experience more harm than benefit associated with glucocorticoid treatment.

Several limitations are inherent to this study. Since it was done at a single center, the results may not be generalizable. As a retrospective analysis, it is subject to confounding and bias. In addition, because patients were included only if they had reached the outcome of death/MV or hospital discharge, the sample size was truncated. We believe glucocorticoid use in hospitalized patients excluded from the study reflects increased use with time because of a growing belief in their effectiveness.

Preliminary analysis from the RECOVERY study showed a reduced rate of mortality in patients randomized to dexamethasone, compared with those who received standard of care.¹⁰ These results led to the National Institutes for Health COVID-19 Treatment Guidelines Panel recommendation for dexamethasone treatment in patients with COVID-19 who require supplemental oxygen or MV.¹⁵ Our findings suggest a role for CRP to identify patients who may benefit from glucocorticoid therapy, as well as those in whom it may be harmful. Additional studies to further elucidate the role of CRP in guiding glucocorticoid therapy and to predict clinical response are needed.

Coronavirus disease 2019 (COVID-19) is the most important public health emergency of the 21st century. The pandemic has devastated New York City, where over 17,000 confirmed deaths have occurred as of June 5, 2020.¹ The most common cause of death in COVID-19 patients is respiratory failure from acute respiratory distress syndrome (ARDS). A recent study reported high mortality rates among COVID-19 patients who received mechanical ventilation (MV).²

Glucocorticoids are useful as adjunctive treatment for some infections with inflammatory responses, but their efficacy in COVID-19 is unclear. Prior experience with influenza and other coronaviruses may be relevant. A recent meta-analysis of influenza pneumonia showed increased mortality and a higher rate of secondary infections in patients who were administered glucocorticoids.³ For Middle East respiratory syndrome, severe acute respiratory syndrome, and influenza, some studies have demonstrated an association between glucocorticoid use and delayed viral clearance.^4-7 However, a recent retrospective series of patients with COVID-19 and ARDS demonstrated a decrease in mortality with glucocorticoid use.⁸ Glucocorticoids are easily obtained and familiar to providers caring for COVID-19 patients. Hence their empiric use is widespread.^8,9

The primary goal of this study was to determine whether early glucocorticoid treatment is associated with reduced mortality or need for MV in COVID-19 patients.

Study Setting and Overview

Montefiore Medical Center comprises four hospitals totaling 1,536 beds in the Bronx borough of New York, New York. Based upon early experience, some clinicians began prescribing systemic glucocorticoids to patients with COVID-19 while others did not. We leveraged this variation in practice to examine the effectiveness of glucocorticoids in reducing mortality and the rate of MV in hospitalized COVID-19 patients.

Study Populations

There were 2,998 patients admitted with a positive COVID-19 test between March 11, 2020, and April 13, 2020. An a priori decision was made to include all hospitalized COVID-19 patients, including children. Because the outcomes of in-hospital mortality and in-hospital MV cannot be assessed in patients still hospitalized, we included only patients who either died or had been discharged from the hospital. Patients who died or were placed on MV within the first 48 hours of admission were excluded because outcome events occurred before having the opportunity for glucocorticoid treatment. To ensure treatment preceded outcome measurement, we included only patients treated with glucocorticoids within the first 48 hours of admission (treatment group) and compared them with patients never treated with glucocorticoids (control group).

Outcomes and Independent Variables

The primary outcome was a composite of in-hospital mortality or in-hospital MV. Secondary outcomes were the components of the primary. Timing of MV was determined using the first documentation of a ventilator respiratory rate or tidal volume. The independent variable of interest was treatment with glucocorticoids within the first 48 hours of admission. Formulations included are described in the Appendix.

To compare treatment and control groups and to perform adjusted analyses, we also examined the demographic and clinical characteristics, comorbidities, and laboratory values of each admission. For the comparison of study populations, missing values for each variable were ignored. In the primary (unstratified) multivariable analysis, continuous variables were categorized, with missing values assumed to be normal when used as an adjustment variable. All variables extracted, number of missing values, candidates for inclusion in the multivariable analysis, and those that fell out of the model are presented in the Appendix. Several subgroup analyses were predefined including age, diabetes, admission glucose, C-reactive protein (CRP), D-dimer, and troponin T levels.

Statistical Analysis

The treated and control groups were compared with respect to demographics, clinical characteristics, comorbidities, and laboratory values. Primary and secondary outcomes in the groups were compared in unadjusted and adjusted analyses using univariable and multivariable logistic regression models. All patient characteristics that were candidates for inclusion in the adjustment models are listed in the Appendix. Variables were included in the final model if they were associated with the primary outcome (Wald test P < .20) in univariable regression. A sensitivity analysis excluded all variables missing greater than 10% of data, including CRP. Interactions between treatment and six predefined subgroups were tested using logistic regression with interaction terms (eg, [steroids]*[age]). Stratified logistic regression was used to test the association between treatment and the primary outcome in each of the predefined subgroups. Patients who were missing CRP were excluded from the stratified analysis. Because a significant interaction between treatment and initial CRP level was discovered, we undertook a post hoc adjusted analysis within each of the 15 predefined subgroup variables. Because there were fewer outcome events in each subgroup, we constructed a parsimonious logistic regression model that included all variables independently associated with the exposure (P < .05). The same seven adjustment variables were used in each of the predefined subgroups. The study was approved by the Albert Einstein College of Medicine Institutional Review Board. Stata 15.1 software (StataCorp) was used for data analysis.

Of 2,998 patients examined, 1,806 met inclusion criteria and included 140 (7.7%) treated with glucocorticoids within 48 hours of admission and 1,666 who never received glucocorticoids. Reasons for exclusion of 1,192 patients are provided in the Appendix. Among patients who remained hospitalized and were excluded, 169 of 962 (17.6%) received glucocorticoids. Characteristics of the study population are presented in Table 1. Treatment and control groups were similar except that glucocorticoid-treated patients were more likely to have chronic obstructive pulmonary disease (COPD), asthma, rheumatoid arthritis or lupus, or to have received glucocorticoids in the year prior to admission.

There were 318 who met the primary outcome of death or mechanical ventilation, 270 of whom died and 135 of whom required mechanical ventilation. Overall, early use of glucocorticoids was not associated with in-hospital mortality or MV as a composite outcome or as separate outcomes in both unadjusted and adjusted models (Table 2A). However, there was significant heterogeneity of treatment effect in the subgroups defined by CRP levels (P for interaction = .008; Figure). Early glucocorticoid use and an initial CRP of 20 mg/dL or higher was associated with a significantly reduced risk of mortality or MV in unadjusted (odds ratio, 0.23; 95% CI, 0.08-0.70) and adjusted (aOR, 0.20; 95% CI, 0.06-0.67) analyses (Table 2B). Conversely, glucocorticoid treatment in patients with CRP levels less than 10 mg/dL was associated with a significantly increased risk of mortality or MV in unadjusted (OR_, 2.64; 95% CI, 1.39-5.03) and adjusted (aOR_, 3.14; 95% CI, 1.52-6.50) analyses.

The results of this study indicate that early treatment with glucocorticoids is not associated with mortality or need for MV in unselected patients with COVID-19. Subgroup analyses suggest that glucocorticoid-treated patients with markedly elevated CRP may benefit from glucocorticoid treatment, whereas those patients with lower CRP may be harmed. Our findings were consistent after adjustment for clinical characteristics. The public health implications of these findings are hard to overestimate. Given the global growth of the pandemic and that glucocorticoids are widely available and inexpensive, glucocorticoid therapy may save many thousands of lives. Equally important because we have been able to identify a group that may be harmed, some patients may be saved because glucocorticoids will not be given.

Our study reaffirms the finding of the as yet unpublished Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial that there is a subset of patients with COVID-19 who benefit from treatment with glucocorticoids.¹⁰ Our study extends the findings of the RECOVERY trial in two important ways. First, in addition to finding some patients who may benefit, we also have identified patient groups that may experience harm from treatment with glucocorticoids. This finding suggests choosing the right patients for glucocorticoid treatment is critical to maximize the likelihood of benefit and minimize the risk of harm. Second, we have identified patient groups who are likely to benefit (or be harmed) on the basis of a widely available lab test (CRP).

Our results are also consistent with previous studies of patients with SARS-CoV and MERS-CoV, in which no associations between glucocorticoid treatment and mortality were found.⁷ However, the results of studies examining the effect of glucocorticoids in patients with COVID-19 are less consistent.^8,11,12

Few of the previous studies examined the effects of glucocorticoids in subgroups of patients. In our study, the improved outcomes associated with glucocorticoid use in patients with elevated CRPs is intriguing and may be clinically important. Proinflammatory cytokines, especially interleukin-6, acutely increase CRP levels. Cytokine storm syndrome (CSS) is a hyperinflammatory condition that occurs in a subset of COVID-19 patients, often resulting in multiorgan dysfunction.¹³ CRP is markedly elevated in CSS,¹⁴ and improved outcomes with glucocorticoid therapy in this subgroup may indicate benefit in this inflammatory phenotype. Patients with lower CRP are less likely to have CSS and may experience more harm than benefit associated with glucocorticoid treatment.

Several limitations are inherent to this study. Since it was done at a single center, the results may not be generalizable. As a retrospective analysis, it is subject to confounding and bias. In addition, because patients were included only if they had reached the outcome of death/MV or hospital discharge, the sample size was truncated. We believe glucocorticoid use in hospitalized patients excluded from the study reflects increased use with time because of a growing belief in their effectiveness.

Preliminary analysis from the RECOVERY study showed a reduced rate of mortality in patients randomized to dexamethasone, compared with those who received standard of care.¹⁰ These results led to the National Institutes for Health COVID-19 Treatment Guidelines Panel recommendation for dexamethasone treatment in patients with COVID-19 who require supplemental oxygen or MV.¹⁵ Our findings suggest a role for CRP to identify patients who may benefit from glucocorticoid therapy, as well as those in whom it may be harmful. Additional studies to further elucidate the role of CRP in guiding glucocorticoid therapy and to predict clinical response are needed.

Coronavirus disease 2019 (COVID-19) is the most important public health emergency of the 21st century. The pandemic has devastated New York City, where over 17,000 confirmed deaths have occurred as of June 5, 2020.¹ The most common cause of death in COVID-19 patients is respiratory failure from acute respiratory distress syndrome (ARDS). A recent study reported high mortality rates among COVID-19 patients who received mechanical ventilation (MV).²

Glucocorticoids are useful as adjunctive treatment for some infections with inflammatory responses, but their efficacy in COVID-19 is unclear. Prior experience with influenza and other coronaviruses may be relevant. A recent meta-analysis of influenza pneumonia showed increased mortality and a higher rate of secondary infections in patients who were administered glucocorticoids.³ For Middle East respiratory syndrome, severe acute respiratory syndrome, and influenza, some studies have demonstrated an association between glucocorticoid use and delayed viral clearance.^4-7 However, a recent retrospective series of patients with COVID-19 and ARDS demonstrated a decrease in mortality with glucocorticoid use.⁸ Glucocorticoids are easily obtained and familiar to providers caring for COVID-19 patients. Hence their empiric use is widespread.^8,9

The primary goal of this study was to determine whether early glucocorticoid treatment is associated with reduced mortality or need for MV in COVID-19 patients.

Study Setting and Overview

Montefiore Medical Center comprises four hospitals totaling 1,536 beds in the Bronx borough of New York, New York. Based upon early experience, some clinicians began prescribing systemic glucocorticoids to patients with COVID-19 while others did not. We leveraged this variation in practice to examine the effectiveness of glucocorticoids in reducing mortality and the rate of MV in hospitalized COVID-19 patients.

Study Populations

There were 2,998 patients admitted with a positive COVID-19 test between March 11, 2020, and April 13, 2020. An a priori decision was made to include all hospitalized COVID-19 patients, including children. Because the outcomes of in-hospital mortality and in-hospital MV cannot be assessed in patients still hospitalized, we included only patients who either died or had been discharged from the hospital. Patients who died or were placed on MV within the first 48 hours of admission were excluded because outcome events occurred before having the opportunity for glucocorticoid treatment. To ensure treatment preceded outcome measurement, we included only patients treated with glucocorticoids within the first 48 hours of admission (treatment group) and compared them with patients never treated with glucocorticoids (control group).

Outcomes and Independent Variables

The primary outcome was a composite of in-hospital mortality or in-hospital MV. Secondary outcomes were the components of the primary. Timing of MV was determined using the first documentation of a ventilator respiratory rate or tidal volume. The independent variable of interest was treatment with glucocorticoids within the first 48 hours of admission. Formulations included are described in the Appendix.

To compare treatment and control groups and to perform adjusted analyses, we also examined the demographic and clinical characteristics, comorbidities, and laboratory values of each admission. For the comparison of study populations, missing values for each variable were ignored. In the primary (unstratified) multivariable analysis, continuous variables were categorized, with missing values assumed to be normal when used as an adjustment variable. All variables extracted, number of missing values, candidates for inclusion in the multivariable analysis, and those that fell out of the model are presented in the Appendix. Several subgroup analyses were predefined including age, diabetes, admission glucose, C-reactive protein (CRP), D-dimer, and troponin T levels.

Statistical Analysis

The treated and control groups were compared with respect to demographics, clinical characteristics, comorbidities, and laboratory values. Primary and secondary outcomes in the groups were compared in unadjusted and adjusted analyses using univariable and multivariable logistic regression models. All patient characteristics that were candidates for inclusion in the adjustment models are listed in the Appendix. Variables were included in the final model if they were associated with the primary outcome (Wald test P < .20) in univariable regression. A sensitivity analysis excluded all variables missing greater than 10% of data, including CRP. Interactions between treatment and six predefined subgroups were tested using logistic regression with interaction terms (eg, [steroids]*[age]). Stratified logistic regression was used to test the association between treatment and the primary outcome in each of the predefined subgroups. Patients who were missing CRP were excluded from the stratified analysis. Because a significant interaction between treatment and initial CRP level was discovered, we undertook a post hoc adjusted analysis within each of the 15 predefined subgroup variables. Because there were fewer outcome events in each subgroup, we constructed a parsimonious logistic regression model that included all variables independently associated with the exposure (P < .05). The same seven adjustment variables were used in each of the predefined subgroups. The study was approved by the Albert Einstein College of Medicine Institutional Review Board. Stata 15.1 software (StataCorp) was used for data analysis.

Of 2,998 patients examined, 1,806 met inclusion criteria and included 140 (7.7%) treated with glucocorticoids within 48 hours of admission and 1,666 who never received glucocorticoids. Reasons for exclusion of 1,192 patients are provided in the Appendix. Among patients who remained hospitalized and were excluded, 169 of 962 (17.6%) received glucocorticoids. Characteristics of the study population are presented in Table 1. Treatment and control groups were similar except that glucocorticoid-treated patients were more likely to have chronic obstructive pulmonary disease (COPD), asthma, rheumatoid arthritis or lupus, or to have received glucocorticoids in the year prior to admission.

There were 318 who met the primary outcome of death or mechanical ventilation, 270 of whom died and 135 of whom required mechanical ventilation. Overall, early use of glucocorticoids was not associated with in-hospital mortality or MV as a composite outcome or as separate outcomes in both unadjusted and adjusted models (Table 2A). However, there was significant heterogeneity of treatment effect in the subgroups defined by CRP levels (P for interaction = .008; Figure). Early glucocorticoid use and an initial CRP of 20 mg/dL or higher was associated with a significantly reduced risk of mortality or MV in unadjusted (odds ratio, 0.23; 95% CI, 0.08-0.70) and adjusted (aOR, 0.20; 95% CI, 0.06-0.67) analyses (Table 2B). Conversely, glucocorticoid treatment in patients with CRP levels less than 10 mg/dL was associated with a significantly increased risk of mortality or MV in unadjusted (OR_, 2.64; 95% CI, 1.39-5.03) and adjusted (aOR_, 3.14; 95% CI, 1.52-6.50) analyses.

The results of this study indicate that early treatment with glucocorticoids is not associated with mortality or need for MV in unselected patients with COVID-19. Subgroup analyses suggest that glucocorticoid-treated patients with markedly elevated CRP may benefit from glucocorticoid treatment, whereas those patients with lower CRP may be harmed. Our findings were consistent after adjustment for clinical characteristics. The public health implications of these findings are hard to overestimate. Given the global growth of the pandemic and that glucocorticoids are widely available and inexpensive, glucocorticoid therapy may save many thousands of lives. Equally important because we have been able to identify a group that may be harmed, some patients may be saved because glucocorticoids will not be given.

Our study reaffirms the finding of the as yet unpublished Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial that there is a subset of patients with COVID-19 who benefit from treatment with glucocorticoids.¹⁰ Our study extends the findings of the RECOVERY trial in two important ways. First, in addition to finding some patients who may benefit, we also have identified patient groups that may experience harm from treatment with glucocorticoids. This finding suggests choosing the right patients for glucocorticoid treatment is critical to maximize the likelihood of benefit and minimize the risk of harm. Second, we have identified patient groups who are likely to benefit (or be harmed) on the basis of a widely available lab test (CRP).

Our results are also consistent with previous studies of patients with SARS-CoV and MERS-CoV, in which no associations between glucocorticoid treatment and mortality were found.⁷ However, the results of studies examining the effect of glucocorticoids in patients with COVID-19 are less consistent.^8,11,12

Few of the previous studies examined the effects of glucocorticoids in subgroups of patients. In our study, the improved outcomes associated with glucocorticoid use in patients with elevated CRPs is intriguing and may be clinically important. Proinflammatory cytokines, especially interleukin-6, acutely increase CRP levels. Cytokine storm syndrome (CSS) is a hyperinflammatory condition that occurs in a subset of COVID-19 patients, often resulting in multiorgan dysfunction.¹³ CRP is markedly elevated in CSS,¹⁴ and improved outcomes with glucocorticoid therapy in this subgroup may indicate benefit in this inflammatory phenotype. Patients with lower CRP are less likely to have CSS and may experience more harm than benefit associated with glucocorticoid treatment.

Several limitations are inherent to this study. Since it was done at a single center, the results may not be generalizable. As a retrospective analysis, it is subject to confounding and bias. In addition, because patients were included only if they had reached the outcome of death/MV or hospital discharge, the sample size was truncated. We believe glucocorticoid use in hospitalized patients excluded from the study reflects increased use with time because of a growing belief in their effectiveness.

Preliminary analysis from the RECOVERY study showed a reduced rate of mortality in patients randomized to dexamethasone, compared with those who received standard of care.¹⁰ These results led to the National Institutes for Health COVID-19 Treatment Guidelines Panel recommendation for dexamethasone treatment in patients with COVID-19 who require supplemental oxygen or MV.¹⁵ Our findings suggest a role for CRP to identify patients who may benefit from glucocorticoid therapy, as well as those in whom it may be harmful. Additional studies to further elucidate the role of CRP in guiding glucocorticoid therapy and to predict clinical response are needed.

Immigrant physicians and international medical graduates (IMGs) have for decades been very important to the healthcare delivery in the United States. For many currently serving on the front lines, the path has been full of challenges and uncertainties, now acutely worsened by the pandemic at hand. Manpreet Malik, MD, is one of those hospitalists. He grew up in a small city in India. He completed medical school in South India where he met students from all over the world and learned to speak a new language to serve local patients. The multicultural experience inspired him to pursue residency in the United States. Manpreet obtained a J-1 visa for residency and subsequently applied for a J-1 waiver for his first hospitalist job in 2013. Then his employer, a nonprofit organization, applied for H-1B and permanent resident status. He continues on an H-1B status but awaits his green card 7 years later. His wife, a dentist, is also an H-1B visa holder and they have two children. While they have assimilated into American society and flourished professionally, a sense of security eludes them. The COVID-19 pandemic has amplified this for their family. Like many other families, they are both in high-risk occupations and worry about the future, including what would happen if either or both of them contracted the virus. Their carefully planned life feels like a wobbly house of cards.

Immigrant healthcare workers are on the front lines in the fight against COVID-19 in the United States, accounting for 16.4% of healthcare workers amid this pandemic.¹ Of physicians in the United States, 29% are not born in the United States,and of the practicing hospitalists, 32% are IMGs.^1,2 IMGs are physicians who have graduated from medical schools outside of the United States and Canada who lack accreditation by the Liaison Committee on Medical Education.³ IMGs are a heterogeneous group with widely varying cultural, educational, and linguistic backgrounds with around 12,000 IMGs applying yearly for US residency positions.⁴ IMG hospitalists are uniquely positioned at the front lines facing arguably more risks with less recognition.⁵ The top five countries sending physicians to the United States are India, China, the Philippines, South Korea, and Pakistan.⁶ Yet many of these doctors—more than a third of those practicing in this country who graduated from international medical schools—have visa restrictions that limit their ability to work in communities with the greatest need.⁷ Another group of approximately 65,000 IMGs currently living in the United States are not licensed; they have not passed the board exam because they haven’t matched into a residency program to be eligible to take it.⁸ Many are working other jobs such as medical research, even though they could be deployed to serve as scribes or work in triage via telemedicine if their visas permitted.

During the COVID-19 pandemic, immigrant doctors are putting their lives on the line daily to care for patients. Immigrant doctors on visas are not eligible for Medicaid or Social Security benefits. Further, their partners and children are often dependent on them for legal resident status in the United States because of employer-based visa sponsorship. As the primary visa holder, if a non–US-born physician in the United States gets severely ill while fighting the virus, or gets disabled, they may have no benefits to fall back on. These physicians have houses, families, and children who are American citizens, and they are contributing members of society. Physicians on visas pay taxes the same way US citizens do. If their health or employment is jeopardized, their families would be unable to stay in the US legally, becoming undocumented and risking deportation. These physicians, who are fighting COVID-19 today, are helpless to provide a stable structure for their own loved ones.

With the COVID-19 pandemic unfolding, there is a risk of more physician shortages. The US healthcare workforce relies on immigrant physicians to help provide high-quality and accessible patient care. There are challenges for IMGs for getting into residency programs, and this limits the potential workforce during COVID-19. This year, according to the National Resident Matching Program, 4,222 non–US-born IMGs are due to start their US residency training on July 1.⁹ These doctors have the opportunity to serve across the country during this pandemic. According to data from the matching program, IMGs make up a large proportion of the workforce, obtaining 23% of the total number of US residency positions filled, and are in many leading academic institutions. These doctors, many of whom are waiting for their visas to be processed, need to be admitted in order to provide the care that Americans need during this pandemic. A similar number of IMGs will be completing their specialty training and are due to become attending physicians in their chosen field, including areas with critical shortages in this pandemic, such as critical care medicine. These skilled physicians depend on the processing of visa extensions or green cards in order to remain in the United States. Subspecialties like internal medicine and family medicine have a large proportion of actively practicing IMGs,⁷ and therefore provide primary care and inpatient care across the nation, especially in underserved areas. However, the geographic location of their practice is limited to the place that sponsored their visa. So a physician in rural Minnesota, where the outbreak of COVID-19 is not severe, cannot travel to hot spots such as New York or Detroit to provide care, even if they have a desire to serve.

For IMGs, the process of obtaining legal status in the US and pertinent immigration policies includes utilizing the H-1B visa program for highly skilled workers¹⁰ or J-1 visas for residencies.¹¹ H-1B visas are usually granted for sponsored positions in underserved or rural areas for at least 3 years, and the healthcare sector must compete with other industries, such as tech, engineering, and other specialty occupations. Physicians working on H-1B visas may apply for permanent work permits, though there is an annual cap for each country and candidates may wait decades to receive one. As a J-1 visa (cultural exchange program) holder, physicians are required to practice in their home country for 2 years prior to working again in the United States. This requirement could be waived by turning to the Conrad 30 Waiver Program¹² or J-1 waivers if they agreed to work in an underserved area in the United States. A limited number of J-1 waivers for each state are dispensed on a first-come, first-served basis (30 IMGs per state per year). This program currently is only authorized through the end of 2020, although legislation has been introduced to extend it, which could expand the slots.¹³ Applying for a J-1 waiver thus becomes a race against time with high-stakes suspense and anxiety for many IMGs. Most, regardless of visa status, dream of a stable and secure life, with permanent resident status as they serve their communities. For some, however, the endgame could mean deportation and the premature demise of dreams. 

Permanent resident status is allotted by country, and there is a long wait for green cards. Three-quarters of skilled workers waiting for green cards are from India. That translates to more than 700,000 people, of which approximately 200,000 are expected to die of old age before being granted green cards.^14,15 In the meantime, while they live with restrictions on both their employment and mobility, many physicians are doing essential medical work in underserved and rural areas throughout the United States.

We urge immigration reform to increase the physician workforce by providing immigrant doctors and IMGs with more flexibility to travel to areas where they are needed the most during this pandemic. There should be a blanket extension of visa deadlines. IMGs on J-1 student visas and H-1B specialty work visas should be exempt from any future immigration bans or limitations during the COVID-19 pandemic. The time is right for accelerating permanent resident status for these highly skilled IMGs. Green cards soon after finishing residency or fellowship training or satisfying a condition of initial visa approval should be the norm instead of a stressful unending wait. Clinicians who serve in underserved communities should be incentivized, and this should include health benefits. Restrictions related to primary and secondary work sites, as well as number of J-1 waivers, should also be relaxed. This flexibility would allow immigrant physicians to care at a variety of locations or by means of telemedicine.

A physician’s role is to heal and to serve their patients, regardless of their own origin. We are the voices of America’s immigrant physicians, particularly hospitalists, serving as frontline workers in our nation’s response to the COVID-19 crisis. The battle against COVID-19 has strained many of our resources, including the need for physicians. Uncertainty and chaos reign professionally and personally for many healthcare workers across America, and more challenges lie ahead for the foreseeable future. Healthcare workers are the unselfish and unwavering wall that stands between COVID-19 and more lives lost in our country. Every effort should be made to preserve and strengthen the healthcare workforce. Immigrant hospitalists, shackled by visa restrictions, could play an even bigger role if their obstacles were removed. It is time to provide them with the sense of security they deserve and rebuild the house of cards into something with a stronger foundation and more stability for our future.

Immigrant physicians and international medical graduates (IMGs) have for decades been very important to the healthcare delivery in the United States. For many currently serving on the front lines, the path has been full of challenges and uncertainties, now acutely worsened by the pandemic at hand. Manpreet Malik, MD, is one of those hospitalists. He grew up in a small city in India. He completed medical school in South India where he met students from all over the world and learned to speak a new language to serve local patients. The multicultural experience inspired him to pursue residency in the United States. Manpreet obtained a J-1 visa for residency and subsequently applied for a J-1 waiver for his first hospitalist job in 2013. Then his employer, a nonprofit organization, applied for H-1B and permanent resident status. He continues on an H-1B status but awaits his green card 7 years later. His wife, a dentist, is also an H-1B visa holder and they have two children. While they have assimilated into American society and flourished professionally, a sense of security eludes them. The COVID-19 pandemic has amplified this for their family. Like many other families, they are both in high-risk occupations and worry about the future, including what would happen if either or both of them contracted the virus. Their carefully planned life feels like a wobbly house of cards.

Immigrant healthcare workers are on the front lines in the fight against COVID-19 in the United States, accounting for 16.4% of healthcare workers amid this pandemic.¹ Of physicians in the United States, 29% are not born in the United States,and of the practicing hospitalists, 32% are IMGs.^1,2 IMGs are physicians who have graduated from medical schools outside of the United States and Canada who lack accreditation by the Liaison Committee on Medical Education.³ IMGs are a heterogeneous group with widely varying cultural, educational, and linguistic backgrounds with around 12,000 IMGs applying yearly for US residency positions.⁴ IMG hospitalists are uniquely positioned at the front lines facing arguably more risks with less recognition.⁵ The top five countries sending physicians to the United States are India, China, the Philippines, South Korea, and Pakistan.⁶ Yet many of these doctors—more than a third of those practicing in this country who graduated from international medical schools—have visa restrictions that limit their ability to work in communities with the greatest need.⁷ Another group of approximately 65,000 IMGs currently living in the United States are not licensed; they have not passed the board exam because they haven’t matched into a residency program to be eligible to take it.⁸ Many are working other jobs such as medical research, even though they could be deployed to serve as scribes or work in triage via telemedicine if their visas permitted.

During the COVID-19 pandemic, immigrant doctors are putting their lives on the line daily to care for patients. Immigrant doctors on visas are not eligible for Medicaid or Social Security benefits. Further, their partners and children are often dependent on them for legal resident status in the United States because of employer-based visa sponsorship. As the primary visa holder, if a non–US-born physician in the United States gets severely ill while fighting the virus, or gets disabled, they may have no benefits to fall back on. These physicians have houses, families, and children who are American citizens, and they are contributing members of society. Physicians on visas pay taxes the same way US citizens do. If their health or employment is jeopardized, their families would be unable to stay in the US legally, becoming undocumented and risking deportation. These physicians, who are fighting COVID-19 today, are helpless to provide a stable structure for their own loved ones.

With the COVID-19 pandemic unfolding, there is a risk of more physician shortages. The US healthcare workforce relies on immigrant physicians to help provide high-quality and accessible patient care. There are challenges for IMGs for getting into residency programs, and this limits the potential workforce during COVID-19. This year, according to the National Resident Matching Program, 4,222 non–US-born IMGs are due to start their US residency training on July 1.⁹ These doctors have the opportunity to serve across the country during this pandemic. According to data from the matching program, IMGs make up a large proportion of the workforce, obtaining 23% of the total number of US residency positions filled, and are in many leading academic institutions. These doctors, many of whom are waiting for their visas to be processed, need to be admitted in order to provide the care that Americans need during this pandemic. A similar number of IMGs will be completing their specialty training and are due to become attending physicians in their chosen field, including areas with critical shortages in this pandemic, such as critical care medicine. These skilled physicians depend on the processing of visa extensions or green cards in order to remain in the United States. Subspecialties like internal medicine and family medicine have a large proportion of actively practicing IMGs,⁷ and therefore provide primary care and inpatient care across the nation, especially in underserved areas. However, the geographic location of their practice is limited to the place that sponsored their visa. So a physician in rural Minnesota, where the outbreak of COVID-19 is not severe, cannot travel to hot spots such as New York or Detroit to provide care, even if they have a desire to serve.

For IMGs, the process of obtaining legal status in the US and pertinent immigration policies includes utilizing the H-1B visa program for highly skilled workers¹⁰ or J-1 visas for residencies.¹¹ H-1B visas are usually granted for sponsored positions in underserved or rural areas for at least 3 years, and the healthcare sector must compete with other industries, such as tech, engineering, and other specialty occupations. Physicians working on H-1B visas may apply for permanent work permits, though there is an annual cap for each country and candidates may wait decades to receive one. As a J-1 visa (cultural exchange program) holder, physicians are required to practice in their home country for 2 years prior to working again in the United States. This requirement could be waived by turning to the Conrad 30 Waiver Program¹² or J-1 waivers if they agreed to work in an underserved area in the United States. A limited number of J-1 waivers for each state are dispensed on a first-come, first-served basis (30 IMGs per state per year). This program currently is only authorized through the end of 2020, although legislation has been introduced to extend it, which could expand the slots.¹³ Applying for a J-1 waiver thus becomes a race against time with high-stakes suspense and anxiety for many IMGs. Most, regardless of visa status, dream of a stable and secure life, with permanent resident status as they serve their communities. For some, however, the endgame could mean deportation and the premature demise of dreams. 

Permanent resident status is allotted by country, and there is a long wait for green cards. Three-quarters of skilled workers waiting for green cards are from India. That translates to more than 700,000 people, of which approximately 200,000 are expected to die of old age before being granted green cards.^14,15 In the meantime, while they live with restrictions on both their employment and mobility, many physicians are doing essential medical work in underserved and rural areas throughout the United States.

We urge immigration reform to increase the physician workforce by providing immigrant doctors and IMGs with more flexibility to travel to areas where they are needed the most during this pandemic. There should be a blanket extension of visa deadlines. IMGs on J-1 student visas and H-1B specialty work visas should be exempt from any future immigration bans or limitations during the COVID-19 pandemic. The time is right for accelerating permanent resident status for these highly skilled IMGs. Green cards soon after finishing residency or fellowship training or satisfying a condition of initial visa approval should be the norm instead of a stressful unending wait. Clinicians who serve in underserved communities should be incentivized, and this should include health benefits. Restrictions related to primary and secondary work sites, as well as number of J-1 waivers, should also be relaxed. This flexibility would allow immigrant physicians to care at a variety of locations or by means of telemedicine.

A physician’s role is to heal and to serve their patients, regardless of their own origin. We are the voices of America’s immigrant physicians, particularly hospitalists, serving as frontline workers in our nation’s response to the COVID-19 crisis. The battle against COVID-19 has strained many of our resources, including the need for physicians. Uncertainty and chaos reign professionally and personally for many healthcare workers across America, and more challenges lie ahead for the foreseeable future. Healthcare workers are the unselfish and unwavering wall that stands between COVID-19 and more lives lost in our country. Every effort should be made to preserve and strengthen the healthcare workforce. Immigrant hospitalists, shackled by visa restrictions, could play an even bigger role if their obstacles were removed. It is time to provide them with the sense of security they deserve and rebuild the house of cards into something with a stronger foundation and more stability for our future.

Immigrant physicians and international medical graduates (IMGs) have for decades been very important to the healthcare delivery in the United States. For many currently serving on the front lines, the path has been full of challenges and uncertainties, now acutely worsened by the pandemic at hand. Manpreet Malik, MD, is one of those hospitalists. He grew up in a small city in India. He completed medical school in South India where he met students from all over the world and learned to speak a new language to serve local patients. The multicultural experience inspired him to pursue residency in the United States. Manpreet obtained a J-1 visa for residency and subsequently applied for a J-1 waiver for his first hospitalist job in 2013. Then his employer, a nonprofit organization, applied for H-1B and permanent resident status. He continues on an H-1B status but awaits his green card 7 years later. His wife, a dentist, is also an H-1B visa holder and they have two children. While they have assimilated into American society and flourished professionally, a sense of security eludes them. The COVID-19 pandemic has amplified this for their family. Like many other families, they are both in high-risk occupations and worry about the future, including what would happen if either or both of them contracted the virus. Their carefully planned life feels like a wobbly house of cards.

Immigrant healthcare workers are on the front lines in the fight against COVID-19 in the United States, accounting for 16.4% of healthcare workers amid this pandemic.¹ Of physicians in the United States, 29% are not born in the United States,and of the practicing hospitalists, 32% are IMGs.^1,2 IMGs are physicians who have graduated from medical schools outside of the United States and Canada who lack accreditation by the Liaison Committee on Medical Education.³ IMGs are a heterogeneous group with widely varying cultural, educational, and linguistic backgrounds with around 12,000 IMGs applying yearly for US residency positions.⁴ IMG hospitalists are uniquely positioned at the front lines facing arguably more risks with less recognition.⁵ The top five countries sending physicians to the United States are India, China, the Philippines, South Korea, and Pakistan.⁶ Yet many of these doctors—more than a third of those practicing in this country who graduated from international medical schools—have visa restrictions that limit their ability to work in communities with the greatest need.⁷ Another group of approximately 65,000 IMGs currently living in the United States are not licensed; they have not passed the board exam because they haven’t matched into a residency program to be eligible to take it.⁸ Many are working other jobs such as medical research, even though they could be deployed to serve as scribes or work in triage via telemedicine if their visas permitted.

During the COVID-19 pandemic, immigrant doctors are putting their lives on the line daily to care for patients. Immigrant doctors on visas are not eligible for Medicaid or Social Security benefits. Further, their partners and children are often dependent on them for legal resident status in the United States because of employer-based visa sponsorship. As the primary visa holder, if a non–US-born physician in the United States gets severely ill while fighting the virus, or gets disabled, they may have no benefits to fall back on. These physicians have houses, families, and children who are American citizens, and they are contributing members of society. Physicians on visas pay taxes the same way US citizens do. If their health or employment is jeopardized, their families would be unable to stay in the US legally, becoming undocumented and risking deportation. These physicians, who are fighting COVID-19 today, are helpless to provide a stable structure for their own loved ones.

With the COVID-19 pandemic unfolding, there is a risk of more physician shortages. The US healthcare workforce relies on immigrant physicians to help provide high-quality and accessible patient care. There are challenges for IMGs for getting into residency programs, and this limits the potential workforce during COVID-19. This year, according to the National Resident Matching Program, 4,222 non–US-born IMGs are due to start their US residency training on July 1.⁹ These doctors have the opportunity to serve across the country during this pandemic. According to data from the matching program, IMGs make up a large proportion of the workforce, obtaining 23% of the total number of US residency positions filled, and are in many leading academic institutions. These doctors, many of whom are waiting for their visas to be processed, need to be admitted in order to provide the care that Americans need during this pandemic. A similar number of IMGs will be completing their specialty training and are due to become attending physicians in their chosen field, including areas with critical shortages in this pandemic, such as critical care medicine. These skilled physicians depend on the processing of visa extensions or green cards in order to remain in the United States. Subspecialties like internal medicine and family medicine have a large proportion of actively practicing IMGs,⁷ and therefore provide primary care and inpatient care across the nation, especially in underserved areas. However, the geographic location of their practice is limited to the place that sponsored their visa. So a physician in rural Minnesota, where the outbreak of COVID-19 is not severe, cannot travel to hot spots such as New York or Detroit to provide care, even if they have a desire to serve.

For IMGs, the process of obtaining legal status in the US and pertinent immigration policies includes utilizing the H-1B visa program for highly skilled workers¹⁰ or J-1 visas for residencies.¹¹ H-1B visas are usually granted for sponsored positions in underserved or rural areas for at least 3 years, and the healthcare sector must compete with other industries, such as tech, engineering, and other specialty occupations. Physicians working on H-1B visas may apply for permanent work permits, though there is an annual cap for each country and candidates may wait decades to receive one. As a J-1 visa (cultural exchange program) holder, physicians are required to practice in their home country for 2 years prior to working again in the United States. This requirement could be waived by turning to the Conrad 30 Waiver Program¹² or J-1 waivers if they agreed to work in an underserved area in the United States. A limited number of J-1 waivers for each state are dispensed on a first-come, first-served basis (30 IMGs per state per year). This program currently is only authorized through the end of 2020, although legislation has been introduced to extend it, which could expand the slots.¹³ Applying for a J-1 waiver thus becomes a race against time with high-stakes suspense and anxiety for many IMGs. Most, regardless of visa status, dream of a stable and secure life, with permanent resident status as they serve their communities. For some, however, the endgame could mean deportation and the premature demise of dreams. 

Permanent resident status is allotted by country, and there is a long wait for green cards. Three-quarters of skilled workers waiting for green cards are from India. That translates to more than 700,000 people, of which approximately 200,000 are expected to die of old age before being granted green cards.^14,15 In the meantime, while they live with restrictions on both their employment and mobility, many physicians are doing essential medical work in underserved and rural areas throughout the United States.

We urge immigration reform to increase the physician workforce by providing immigrant doctors and IMGs with more flexibility to travel to areas where they are needed the most during this pandemic. There should be a blanket extension of visa deadlines. IMGs on J-1 student visas and H-1B specialty work visas should be exempt from any future immigration bans or limitations during the COVID-19 pandemic. The time is right for accelerating permanent resident status for these highly skilled IMGs. Green cards soon after finishing residency or fellowship training or satisfying a condition of initial visa approval should be the norm instead of a stressful unending wait. Clinicians who serve in underserved communities should be incentivized, and this should include health benefits. Restrictions related to primary and secondary work sites, as well as number of J-1 waivers, should also be relaxed. This flexibility would allow immigrant physicians to care at a variety of locations or by means of telemedicine.

A physician’s role is to heal and to serve their patients, regardless of their own origin. We are the voices of America’s immigrant physicians, particularly hospitalists, serving as frontline workers in our nation’s response to the COVID-19 crisis. The battle against COVID-19 has strained many of our resources, including the need for physicians. Uncertainty and chaos reign professionally and personally for many healthcare workers across America, and more challenges lie ahead for the foreseeable future. Healthcare workers are the unselfish and unwavering wall that stands between COVID-19 and more lives lost in our country. Every effort should be made to preserve and strengthen the healthcare workforce. Immigrant hospitalists, shackled by visa restrictions, could play an even bigger role if their obstacles were removed. It is time to provide them with the sense of security they deserve and rebuild the house of cards into something with a stronger foundation and more stability for our future.

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

Frequent daily laboratory testing for inpatients contributes to excessive costs,¹ anemia,² and unnecessary testing.³ The ABIM Foundation’s Choosing Wisely^® campaign recommends avoiding routine labs, like complete blood counts (CBCs) and basic metabolic panels (BMP), in the face of clinical and laboratory stability.^4,5 Prior interventions have reduced unnecessary labs without adverse outcomes.^6-8

In addition to lab frequency, hospitalized patients face suboptimal lab timing. Labs are often ordered as early as 4 am at many institutions.^9,10 This practice disrupts sleep, undermining patient health.^11-13 While prior interventions have reduced daily phlebotomy, few have optimized lab timing for patient sleep.¹⁰ No study has harnessed the electronic health record (EHR) to optimize frequency and timing of labs simultaneously.¹⁴ We aimed to determine the effectiveness of a multicomponent intervention, called Order SMARTT (Sleep: Making Appropriate Reductions in Testing and Timing), to reduce frequency and optimize timing of daily routine labs for medical inpatients.

Setting

This study was conducted on the University of Chicago Medicine (UCM) general medicine services, which consisted of a resident-covered service supervised by general medicine, subspecialist, or hospitalist attendings and a hospitalist service staffed by hospitalists and advanced practice providers.

Development of Order SMARTT

To inform intervention development, we surveyed providers about lab-ordering preferences with use of questions from a prior survey to provide a benchmark (Appendix Table 2).¹⁵ While reducing lab frequency was supported, the modal response for how frequently a stable patient should receive routine labs was every 48 hours (Appendix Table 2). Therefore, we hypothesized that labs ordered every 48 hours may be popular. Taking labs every 48 hours would not require an urgent 4 am draw, so we created a 48-hour 6 am phlebotomy option to “step down” from daily labs. To promote these options, we created two EHR tools: First, an “Order Sleep” shortcut was launched in March 2018 by which physicians could type “sleep” in routine lab orders and three sleep-friendly options would become available (a 48-hour 6 am draw, a daily 6 am draw, or a daily 10 pm draw), and second, a “4 am Labs” column and icon on the electronic patient list to signal who had 4 am labs ordered was launched May 2018 (Appendix Table 1).

Physician Education

We created a 20-minute presentation on the harms of excessive labs and the benefits of sleep-friendly ordering. Instructional Order SMARTT posters were posted in clinician workrooms that emphasized forgoing labs on stable patients and using the “Order Sleep” shortcut when nonurgent labs were needed.

Labs Utilization Data

We used Epic Systems software (Verona, Wisconsin) and our institutional Tableau scorecard to obtain data on CBC and BMP ordering, patient census, and demographics for medical inpatients between July 1, 2017, and November 1, 2018.

Cost Analysis

Costs of lab tests (actual cost to our institution) were obtained from our institutional phlebotomy services’ estimates of direct variable labor and benefits costs and direct variable supplies cost.

Statistical Analysis

Data analysis was performed with SAS version 9.4 statistical software (Cary, North Carolina, USA) and R version 3.6.2 (Vienna, Austria). Descriptive statistics were used to summarize data. Surveys were analyzed using chi-square tests for categorical variables and two-sample t tests for continuous variables. For lab ordering data, interrupted time series analyses (ITSA) were used to determine the changes in ordering practices with the implementation of the two interventions controlling for service lines (resident vs hospitalist service). ITSA enables examination of changes in lab ordering while controlling for time. The AUTOREG function in SAS was used to build the model and estimate final parameters. This function automatically tests for autocorrelation, heteroscedasticity, and estimates any autoregressive parameters required in the model. Our main model tested the association between our two separate interventions on ordering practices, controlling for service (hospitalist or resident).¹⁶

Of 125 residents, 82 (65.6%) attended the session and completed the survey. Attendance and response rate for hospitalists was 80% (16 of 20). Similar to a prior study, many residents (73.1%) reported they would be comfortable if patients received less daily laboratory testing (Appendix Table 2).

We reviewed data from 7,045 total patients over 50,951 total patient days between July1, 2017, and November 1, 2018 (Appendix Table 3).

Total Lab Draws

After accounting for total patient days, we saw 26.3% reduction on average in total lab draws per patient-day per week postintervention (4.68 before vs 3.45 after; difference, 1.23; 95% CI, 0.82-1.63; P < .05; Appendix Table 3). When total lab draws were stratified by service, we saw 28% reduction on average in total lab draws per patient-day per week on resident services (4.67 before vs 3.36 after; difference, 1.31; 95% CI, 0.88-1.74; P < .05) and 23.9% reduction on average in lab draws/patient-day per week on the hospitalist service (4.73 before vs 3.60 after; difference, 1.13; 95% CI, 0.61-1.64; P < .05; Appendix Table 3).

Sleep-Friendly Labs by Intervention

For patients with routine labs, the proportion of sleep-friendly labs drawn per patient-day increased from 6% preintervention to 21% postintervention (P < .001). ITSA demonstrated both interventions were associated with improving lab timing. There was a statistically significant increase in sleep-friendly labs ordered per patient encounter per week immediately after the launch of “Order Sleep” (intercept, 0.49; standard error (SE), 0.14; P = .001) and the “4 am Labs” column (intercept, 0.32; SE, 0.13; P = .02; Table, Figure A).

Sleep-Friendly Lab Orders by Service

Over the study period, there was no significant difference in total sleep-friendly labs ordered/month between resident and hospitalist services (84.88 vs 86.19; P = .95).

In ITSA, “Order Sleep” was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on resident services (intercept, 1.03; SE, 0.29; P < .001). However, this initial increase was followed by a decrease over time in sleep-friendly lab orders per week (slope change, –0.1; SE, 0.04; P = .02; Table, Figure B). There was no statistically significant change observed on the hospitalist service with “Order Sleep.”

In contrast, the “4 am Labs” column was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on hospitalist service (intercept, 1.17; SE, 0.50; P = .02; Table, Figure B). While there was no immediate change on resident service, we observed a significant increase over time in sleep-friendly orders per encounter per week on resident services with the introduction of the “4 am Labs” column (slope change, 0.11; SE, 0.04; P = .01; Table, Figure B).

Cost Savings

Using an estimated cost of $7.70 for CBCs and $8.01 for BMPs from our laboratory, our intervention saved an estimated $60,278 in lab costs alone over the 16-month study period (Appendix Table 4).

To our knowledge, this is the first study showing a multicomponent intervention using EHR tools can both reduce frequency and optimize timing of routine lab ordering. Our project had two interventions implemented at two different times: First, an “Order Sleep” shortcut was introduced to select sleep-friendly lab timing, including a 6 am draw every 48 hours, and later, a “4 am Labs” column was added to electronic patient lists to passively nudge physicians to consider sleep-friendly labs. The “Order Sleep” tool was associated with a significant immediate increase in sleep-friendly lab ordering on resident services, while the “4 am Labs” column was associated with a significant immediate increase in sleep-friendly lab ordering on the hospitalist service. An overall reduction in total lab draws was seen on both services.

While the “Order Sleep” tool was initially associated with significant increases in sleep-friendly orders on resident services, this change was not sustained. This could have been caused by the short-lived effect of education more than sustained adoption of the tool. In contrast, the “4 am Labs” column on the patient list resulted in a significant sustained increase in sleep-friendly labs on resident services. While residents responded to both tools, both interventions were associated with lasting changes in practice.

The “4 am Labs” column on patient lists was associated with increased adoption of sleep-friendly labs for hospitalist services. Hospitalists care for a larger census with more frequent handoffs and greater reliance on the patient list, which makes patient lists in general an important tool to target value improvement.

While other institutions have attempted to shift lab-timing by altering phlebotomy workflows¹⁰ or via conscious decision-making on rounds,⁹ our study differs in several ways. We avoided default options and allowed clinicians to select sleep-friendly labs to promote buy-in. It is sometimes necessary to order 4 am labs for sick patients who need urgent decision-making, which highlights the need to preserve this option for clinicians. Similarly, our intervention did not aim to eliminate lab draws entirely but offer a more judicious frequency of every 48 hours, consistent with the survey preferences noted. This intervention encouraged reappraisal of patients’ overall needs for labs and created variability in ordering times to reduce the volume of labs ordered at 4 am.

Our study had several limitations. First, this was a single center study on adult medicine services, which limits generalizability. Although we considered surgical services, their early rounds made deviations from 4 am undesirable. Given the observational study design, we cannot assume causal relationships or rule out secular trends. There were large swings in sleep-friendly lab ordering during our study that could be attributed to different physicians rotating on the services monthly. We did not obtain objective data on patient sleep or patient satisfaction because of the low response rate to the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey.

In conclusion, a multicomponent intervention using EHR tools can reduce inpatient daily lab frequency and optimize lab timing to help promote patient sleep.

The authors would like to thank The University of Chicago Center for Healthcare Delivery Science and Innovation for sponsoring their annual Choosing Wisely Challenge, which allowed for access to institutional support and resources for this study. We would also like to thank Mary Kate Springman, MHA, and John Fahrenbach, PhD, for their assistance with this project. Dr Tapaskar also received mentorship through the Future Leader Program for the High Value Practice Academic Alliance.

Frequent daily laboratory testing for inpatients contributes to excessive costs,¹ anemia,² and unnecessary testing.³ The ABIM Foundation’s Choosing Wisely^® campaign recommends avoiding routine labs, like complete blood counts (CBCs) and basic metabolic panels (BMP), in the face of clinical and laboratory stability.^4,5 Prior interventions have reduced unnecessary labs without adverse outcomes.^6-8

In addition to lab frequency, hospitalized patients face suboptimal lab timing. Labs are often ordered as early as 4 am at many institutions.^9,10 This practice disrupts sleep, undermining patient health.^11-13 While prior interventions have reduced daily phlebotomy, few have optimized lab timing for patient sleep.¹⁰ No study has harnessed the electronic health record (EHR) to optimize frequency and timing of labs simultaneously.¹⁴ We aimed to determine the effectiveness of a multicomponent intervention, called Order SMARTT (Sleep: Making Appropriate Reductions in Testing and Timing), to reduce frequency and optimize timing of daily routine labs for medical inpatients.

Setting

This study was conducted on the University of Chicago Medicine (UCM) general medicine services, which consisted of a resident-covered service supervised by general medicine, subspecialist, or hospitalist attendings and a hospitalist service staffed by hospitalists and advanced practice providers.

Development of Order SMARTT

To inform intervention development, we surveyed providers about lab-ordering preferences with use of questions from a prior survey to provide a benchmark (Appendix Table 2).¹⁵ While reducing lab frequency was supported, the modal response for how frequently a stable patient should receive routine labs was every 48 hours (Appendix Table 2). Therefore, we hypothesized that labs ordered every 48 hours may be popular. Taking labs every 48 hours would not require an urgent 4 am draw, so we created a 48-hour 6 am phlebotomy option to “step down” from daily labs. To promote these options, we created two EHR tools: First, an “Order Sleep” shortcut was launched in March 2018 by which physicians could type “sleep” in routine lab orders and three sleep-friendly options would become available (a 48-hour 6 am draw, a daily 6 am draw, or a daily 10 pm draw), and second, a “4 am Labs” column and icon on the electronic patient list to signal who had 4 am labs ordered was launched May 2018 (Appendix Table 1).

Physician Education

We created a 20-minute presentation on the harms of excessive labs and the benefits of sleep-friendly ordering. Instructional Order SMARTT posters were posted in clinician workrooms that emphasized forgoing labs on stable patients and using the “Order Sleep” shortcut when nonurgent labs were needed.

Labs Utilization Data

We used Epic Systems software (Verona, Wisconsin) and our institutional Tableau scorecard to obtain data on CBC and BMP ordering, patient census, and demographics for medical inpatients between July 1, 2017, and November 1, 2018.

Cost Analysis

Costs of lab tests (actual cost to our institution) were obtained from our institutional phlebotomy services’ estimates of direct variable labor and benefits costs and direct variable supplies cost.

Statistical Analysis

Data analysis was performed with SAS version 9.4 statistical software (Cary, North Carolina, USA) and R version 3.6.2 (Vienna, Austria). Descriptive statistics were used to summarize data. Surveys were analyzed using chi-square tests for categorical variables and two-sample t tests for continuous variables. For lab ordering data, interrupted time series analyses (ITSA) were used to determine the changes in ordering practices with the implementation of the two interventions controlling for service lines (resident vs hospitalist service). ITSA enables examination of changes in lab ordering while controlling for time. The AUTOREG function in SAS was used to build the model and estimate final parameters. This function automatically tests for autocorrelation, heteroscedasticity, and estimates any autoregressive parameters required in the model. Our main model tested the association between our two separate interventions on ordering practices, controlling for service (hospitalist or resident).¹⁶

Of 125 residents, 82 (65.6%) attended the session and completed the survey. Attendance and response rate for hospitalists was 80% (16 of 20). Similar to a prior study, many residents (73.1%) reported they would be comfortable if patients received less daily laboratory testing (Appendix Table 2).

We reviewed data from 7,045 total patients over 50,951 total patient days between July1, 2017, and November 1, 2018 (Appendix Table 3).

Total Lab Draws

After accounting for total patient days, we saw 26.3% reduction on average in total lab draws per patient-day per week postintervention (4.68 before vs 3.45 after; difference, 1.23; 95% CI, 0.82-1.63; P < .05; Appendix Table 3). When total lab draws were stratified by service, we saw 28% reduction on average in total lab draws per patient-day per week on resident services (4.67 before vs 3.36 after; difference, 1.31; 95% CI, 0.88-1.74; P < .05) and 23.9% reduction on average in lab draws/patient-day per week on the hospitalist service (4.73 before vs 3.60 after; difference, 1.13; 95% CI, 0.61-1.64; P < .05; Appendix Table 3).

Sleep-Friendly Labs by Intervention

For patients with routine labs, the proportion of sleep-friendly labs drawn per patient-day increased from 6% preintervention to 21% postintervention (P < .001). ITSA demonstrated both interventions were associated with improving lab timing. There was a statistically significant increase in sleep-friendly labs ordered per patient encounter per week immediately after the launch of “Order Sleep” (intercept, 0.49; standard error (SE), 0.14; P = .001) and the “4 am Labs” column (intercept, 0.32; SE, 0.13; P = .02; Table, Figure A).

Sleep-Friendly Lab Orders by Service

Over the study period, there was no significant difference in total sleep-friendly labs ordered/month between resident and hospitalist services (84.88 vs 86.19; P = .95).

In ITSA, “Order Sleep” was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on resident services (intercept, 1.03; SE, 0.29; P < .001). However, this initial increase was followed by a decrease over time in sleep-friendly lab orders per week (slope change, –0.1; SE, 0.04; P = .02; Table, Figure B). There was no statistically significant change observed on the hospitalist service with “Order Sleep.”

In contrast, the “4 am Labs” column was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on hospitalist service (intercept, 1.17; SE, 0.50; P = .02; Table, Figure B). While there was no immediate change on resident service, we observed a significant increase over time in sleep-friendly orders per encounter per week on resident services with the introduction of the “4 am Labs” column (slope change, 0.11; SE, 0.04; P = .01; Table, Figure B).

Cost Savings

Using an estimated cost of $7.70 for CBCs and $8.01 for BMPs from our laboratory, our intervention saved an estimated $60,278 in lab costs alone over the 16-month study period (Appendix Table 4).

To our knowledge, this is the first study showing a multicomponent intervention using EHR tools can both reduce frequency and optimize timing of routine lab ordering. Our project had two interventions implemented at two different times: First, an “Order Sleep” shortcut was introduced to select sleep-friendly lab timing, including a 6 am draw every 48 hours, and later, a “4 am Labs” column was added to electronic patient lists to passively nudge physicians to consider sleep-friendly labs. The “Order Sleep” tool was associated with a significant immediate increase in sleep-friendly lab ordering on resident services, while the “4 am Labs” column was associated with a significant immediate increase in sleep-friendly lab ordering on the hospitalist service. An overall reduction in total lab draws was seen on both services.

While the “Order Sleep” tool was initially associated with significant increases in sleep-friendly orders on resident services, this change was not sustained. This could have been caused by the short-lived effect of education more than sustained adoption of the tool. In contrast, the “4 am Labs” column on the patient list resulted in a significant sustained increase in sleep-friendly labs on resident services. While residents responded to both tools, both interventions were associated with lasting changes in practice.

The “4 am Labs” column on patient lists was associated with increased adoption of sleep-friendly labs for hospitalist services. Hospitalists care for a larger census with more frequent handoffs and greater reliance on the patient list, which makes patient lists in general an important tool to target value improvement.

While other institutions have attempted to shift lab-timing by altering phlebotomy workflows¹⁰ or via conscious decision-making on rounds,⁹ our study differs in several ways. We avoided default options and allowed clinicians to select sleep-friendly labs to promote buy-in. It is sometimes necessary to order 4 am labs for sick patients who need urgent decision-making, which highlights the need to preserve this option for clinicians. Similarly, our intervention did not aim to eliminate lab draws entirely but offer a more judicious frequency of every 48 hours, consistent with the survey preferences noted. This intervention encouraged reappraisal of patients’ overall needs for labs and created variability in ordering times to reduce the volume of labs ordered at 4 am.

Our study had several limitations. First, this was a single center study on adult medicine services, which limits generalizability. Although we considered surgical services, their early rounds made deviations from 4 am undesirable. Given the observational study design, we cannot assume causal relationships or rule out secular trends. There were large swings in sleep-friendly lab ordering during our study that could be attributed to different physicians rotating on the services monthly. We did not obtain objective data on patient sleep or patient satisfaction because of the low response rate to the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey.

In conclusion, a multicomponent intervention using EHR tools can reduce inpatient daily lab frequency and optimize lab timing to help promote patient sleep.

The authors would like to thank The University of Chicago Center for Healthcare Delivery Science and Innovation for sponsoring their annual Choosing Wisely Challenge, which allowed for access to institutional support and resources for this study. We would also like to thank Mary Kate Springman, MHA, and John Fahrenbach, PhD, for their assistance with this project. Dr Tapaskar also received mentorship through the Future Leader Program for the High Value Practice Academic Alliance.

Frequent daily laboratory testing for inpatients contributes to excessive costs,¹ anemia,² and unnecessary testing.³ The ABIM Foundation’s Choosing Wisely^® campaign recommends avoiding routine labs, like complete blood counts (CBCs) and basic metabolic panels (BMP), in the face of clinical and laboratory stability.^4,5 Prior interventions have reduced unnecessary labs without adverse outcomes.^6-8

In addition to lab frequency, hospitalized patients face suboptimal lab timing. Labs are often ordered as early as 4 am at many institutions.^9,10 This practice disrupts sleep, undermining patient health.^11-13 While prior interventions have reduced daily phlebotomy, few have optimized lab timing for patient sleep.¹⁰ No study has harnessed the electronic health record (EHR) to optimize frequency and timing of labs simultaneously.¹⁴ We aimed to determine the effectiveness of a multicomponent intervention, called Order SMARTT (Sleep: Making Appropriate Reductions in Testing and Timing), to reduce frequency and optimize timing of daily routine labs for medical inpatients.

Setting

This study was conducted on the University of Chicago Medicine (UCM) general medicine services, which consisted of a resident-covered service supervised by general medicine, subspecialist, or hospitalist attendings and a hospitalist service staffed by hospitalists and advanced practice providers.

Development of Order SMARTT

To inform intervention development, we surveyed providers about lab-ordering preferences with use of questions from a prior survey to provide a benchmark (Appendix Table 2).¹⁵ While reducing lab frequency was supported, the modal response for how frequently a stable patient should receive routine labs was every 48 hours (Appendix Table 2). Therefore, we hypothesized that labs ordered every 48 hours may be popular. Taking labs every 48 hours would not require an urgent 4 am draw, so we created a 48-hour 6 am phlebotomy option to “step down” from daily labs. To promote these options, we created two EHR tools: First, an “Order Sleep” shortcut was launched in March 2018 by which physicians could type “sleep” in routine lab orders and three sleep-friendly options would become available (a 48-hour 6 am draw, a daily 6 am draw, or a daily 10 pm draw), and second, a “4 am Labs” column and icon on the electronic patient list to signal who had 4 am labs ordered was launched May 2018 (Appendix Table 1).

Physician Education

We created a 20-minute presentation on the harms of excessive labs and the benefits of sleep-friendly ordering. Instructional Order SMARTT posters were posted in clinician workrooms that emphasized forgoing labs on stable patients and using the “Order Sleep” shortcut when nonurgent labs were needed.

Labs Utilization Data

We used Epic Systems software (Verona, Wisconsin) and our institutional Tableau scorecard to obtain data on CBC and BMP ordering, patient census, and demographics for medical inpatients between July 1, 2017, and November 1, 2018.

Cost Analysis

Costs of lab tests (actual cost to our institution) were obtained from our institutional phlebotomy services’ estimates of direct variable labor and benefits costs and direct variable supplies cost.

Statistical Analysis

Data analysis was performed with SAS version 9.4 statistical software (Cary, North Carolina, USA) and R version 3.6.2 (Vienna, Austria). Descriptive statistics were used to summarize data. Surveys were analyzed using chi-square tests for categorical variables and two-sample t tests for continuous variables. For lab ordering data, interrupted time series analyses (ITSA) were used to determine the changes in ordering practices with the implementation of the two interventions controlling for service lines (resident vs hospitalist service). ITSA enables examination of changes in lab ordering while controlling for time. The AUTOREG function in SAS was used to build the model and estimate final parameters. This function automatically tests for autocorrelation, heteroscedasticity, and estimates any autoregressive parameters required in the model. Our main model tested the association between our two separate interventions on ordering practices, controlling for service (hospitalist or resident).¹⁶

Of 125 residents, 82 (65.6%) attended the session and completed the survey. Attendance and response rate for hospitalists was 80% (16 of 20). Similar to a prior study, many residents (73.1%) reported they would be comfortable if patients received less daily laboratory testing (Appendix Table 2).

We reviewed data from 7,045 total patients over 50,951 total patient days between July1, 2017, and November 1, 2018 (Appendix Table 3).

Total Lab Draws

After accounting for total patient days, we saw 26.3% reduction on average in total lab draws per patient-day per week postintervention (4.68 before vs 3.45 after; difference, 1.23; 95% CI, 0.82-1.63; P < .05; Appendix Table 3). When total lab draws were stratified by service, we saw 28% reduction on average in total lab draws per patient-day per week on resident services (4.67 before vs 3.36 after; difference, 1.31; 95% CI, 0.88-1.74; P < .05) and 23.9% reduction on average in lab draws/patient-day per week on the hospitalist service (4.73 before vs 3.60 after; difference, 1.13; 95% CI, 0.61-1.64; P < .05; Appendix Table 3).

Sleep-Friendly Labs by Intervention

For patients with routine labs, the proportion of sleep-friendly labs drawn per patient-day increased from 6% preintervention to 21% postintervention (P < .001). ITSA demonstrated both interventions were associated with improving lab timing. There was a statistically significant increase in sleep-friendly labs ordered per patient encounter per week immediately after the launch of “Order Sleep” (intercept, 0.49; standard error (SE), 0.14; P = .001) and the “4 am Labs” column (intercept, 0.32; SE, 0.13; P = .02; Table, Figure A).

Sleep-Friendly Lab Orders by Service

Over the study period, there was no significant difference in total sleep-friendly labs ordered/month between resident and hospitalist services (84.88 vs 86.19; P = .95).

In ITSA, “Order Sleep” was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on resident services (intercept, 1.03; SE, 0.29; P < .001). However, this initial increase was followed by a decrease over time in sleep-friendly lab orders per week (slope change, –0.1; SE, 0.04; P = .02; Table, Figure B). There was no statistically significant change observed on the hospitalist service with “Order Sleep.”

In contrast, the “4 am Labs” column was associated with a statistically significant immediate increase in sleep-friendly lab orders per patient encounter per week on hospitalist service (intercept, 1.17; SE, 0.50; P = .02; Table, Figure B). While there was no immediate change on resident service, we observed a significant increase over time in sleep-friendly orders per encounter per week on resident services with the introduction of the “4 am Labs” column (slope change, 0.11; SE, 0.04; P = .01; Table, Figure B).

Cost Savings

Using an estimated cost of $7.70 for CBCs and $8.01 for BMPs from our laboratory, our intervention saved an estimated $60,278 in lab costs alone over the 16-month study period (Appendix Table 4).

To our knowledge, this is the first study showing a multicomponent intervention using EHR tools can both reduce frequency and optimize timing of routine lab ordering. Our project had two interventions implemented at two different times: First, an “Order Sleep” shortcut was introduced to select sleep-friendly lab timing, including a 6 am draw every 48 hours, and later, a “4 am Labs” column was added to electronic patient lists to passively nudge physicians to consider sleep-friendly labs. The “Order Sleep” tool was associated with a significant immediate increase in sleep-friendly lab ordering on resident services, while the “4 am Labs” column was associated with a significant immediate increase in sleep-friendly lab ordering on the hospitalist service. An overall reduction in total lab draws was seen on both services.

While the “Order Sleep” tool was initially associated with significant increases in sleep-friendly orders on resident services, this change was not sustained. This could have been caused by the short-lived effect of education more than sustained adoption of the tool. In contrast, the “4 am Labs” column on the patient list resulted in a significant sustained increase in sleep-friendly labs on resident services. While residents responded to both tools, both interventions were associated with lasting changes in practice.

The “4 am Labs” column on patient lists was associated with increased adoption of sleep-friendly labs for hospitalist services. Hospitalists care for a larger census with more frequent handoffs and greater reliance on the patient list, which makes patient lists in general an important tool to target value improvement.

While other institutions have attempted to shift lab-timing by altering phlebotomy workflows¹⁰ or via conscious decision-making on rounds,⁹ our study differs in several ways. We avoided default options and allowed clinicians to select sleep-friendly labs to promote buy-in. It is sometimes necessary to order 4 am labs for sick patients who need urgent decision-making, which highlights the need to preserve this option for clinicians. Similarly, our intervention did not aim to eliminate lab draws entirely but offer a more judicious frequency of every 48 hours, consistent with the survey preferences noted. This intervention encouraged reappraisal of patients’ overall needs for labs and created variability in ordering times to reduce the volume of labs ordered at 4 am.

Our study had several limitations. First, this was a single center study on adult medicine services, which limits generalizability. Although we considered surgical services, their early rounds made deviations from 4 am undesirable. Given the observational study design, we cannot assume causal relationships or rule out secular trends. There were large swings in sleep-friendly lab ordering during our study that could be attributed to different physicians rotating on the services monthly. We did not obtain objective data on patient sleep or patient satisfaction because of the low response rate to the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey.

In conclusion, a multicomponent intervention using EHR tools can reduce inpatient daily lab frequency and optimize lab timing to help promote patient sleep.

The authors would like to thank The University of Chicago Center for Healthcare Delivery Science and Innovation for sponsoring their annual Choosing Wisely Challenge, which allowed for access to institutional support and resources for this study. We would also like to thank Mary Kate Springman, MHA, and John Fahrenbach, PhD, for their assistance with this project. Dr Tapaskar also received mentorship through the Future Leader Program for the High Value Practice Academic Alliance.

A large body of evidence has demonstrated significant gender disparities in academic medicine. Women are less likely than men to reach the rank of full professor, be speakers at Grand Rounds, and author studies in medical journals.^1-4 Gender-based differences in these achievements reduce the visibility of women role models in all academic medicine domains, including research, education, health systems leadership, and clinical excellence. Clinical problem-solving exercises are an opportunity to highlight the skills of women physicians as master clinicians and to establish women as clinician role models.

Clinical problem-solving exercises are highly visible demonstrations of clinical excellence in the medical literature. These exercises follow a specific format in which a clinician analyzes a diagnostic dilemma in a step-by-step manner in response to sequential segments of clinical data. The clinical problem-­solving format was introduced in 1992 in the New England Journal of Medicine and has been adopted by other journals.⁵ (The clinical problem-solving format differs from the clinical pathologic conference format, in which an entire case is presented followed by an extended analysis). Clinical problem-­solving publications are forums for learners of all levels to witness an expert clinician reason through a case.

Authorship teams on clinical reasoning exercises typically include the patient’s physician(s), specialists relevant to the final diagnosis, and the invited discussant who analyzes the clinical dilemma. Journals stipulate in the author instructions, series introductions, or standardized manuscript text of the series that the discussant be a skilled and experienced clinician.^5,6 The patient’s physicians who initiate the clinical reasoning manuscript typically select the discussant; in some journals, the series editors may provide input on discussant choice. To our knowledge, this is the only author role in the medical literature in which authors are invited specifically for their diagnostic reasoning ability.

While women have been authors on fewer original research articles and guest editorials than men have,³ the proportion of women among authors of published clinical reasoning exercises is unknown. This represents a gap in our understanding of the landscape of gender inequity in academic medicine. We sought to determine the proportion of women authors in major clinical problem-solving series and examine the change in women authorship over time.

We selected published clinical problem-solving series targeting a general medicine audience. We excluded general medicine journals in which authors were restricted to one institution or those in which the clinical problem-solving format was not a regular series. Series which met these criteria were the Clinical Problem-Solving series in the New England Journal of Medicine (NEJM), the Clinical Care Conundrums series in the Journal of Hospital Medicine (JHM), and the Exercises in Clinical Reasoning series in the Journal of General Internal Medicine (JGIM). We analyzed the proportion of women authors in each clinical reasoning series from the inaugural articles (1992 for NEJM, 2006 for JHM, and 2010 for JGIM) until July 2019. We also analyzed the change in proportion of women authors from year to year by using data up to 2018 to avoid including a partial year.

We used the gender-guesser python library⁷ to categorize the gender of first, last, and all authors based on their first names. The library uses a database of approximately 40,000 names⁸ and maps first names to the genders they are associated with across languages, classifying each name as “man,” “woman,” “mostly man,” ”mostly woman,” “androgynous,” or “unknown.” When a name is commonly associated with multiple genders, or is associated with different genders in different languages, it is classified either as mostly man, mostly woman, or androgynous. When a name is not found in the database, it is classified as unknown. For all names classified by the database as unknown, androgynous, or mostly man/mostly woman, we determined gender identities by finding the authors’ institutional webpages and consulting their listed gender pronouns. We used gender based on first name to best approximate what a reader would interpret as the author’s gender. We used gender rather than biological sex because authors may have changed their names to better express their gender identity, which may differ from sex assigned at birth.

To test for the statistical significance of changes in the proportion of women authors over time, we performed the Cochran-­Armitage trend test. A P value less than .05 was considered significant.

We analyzed 402 articles: 280 from NEJM, 83 from JHM, and 39 from JGIM. There were 1,026 authors of clinical reasoning articles from NEJM, 362 from JHM, and 168 from JGIM. The Table shows the number of total articles, total authors, and women among first, last, and all authors by journal and by year (inaugural year and 2018). Data for all years are shown in the Appendix Table.

Over the entire time period studied, the percentage of women across the three journals was lowest for last authors (28/280 [10.0%] for NEJM, 6/83 [7.2%] for JHM, and 9/39 [23.1%] for JGIM) and highest for first authors (80/280 [28.6%] for NEJM, 36/83 [43.4%] for JHM, and 13/39 [33.3%] for JGIM). The percentage of women among all authors was similar for all three journals: 224/1,026 (21.8%) for NEJM, 83/362 (22.9%) for JHM, and 36/168 (21.4%) for JGIM.

The Figure shows the change in percentage of women authors from year to year through 2018. There was a significant increase in the proportion of women first authors in NEJM (from 0/12 [0.0%] in 1992 to 4/12 [33.3%] in 2018; P < .0001) and JHM (from 2/5 [40.0%] in 2006 to 7/9 [77.8%] in 2018 P = .01). There was also a significant increase in the proportion of women among all authors in NEJM (from 0/17 [0.0%] in 1992 to 17/59 [28.8%] in 2018; P < .0001) and JHM (from 3/19 [15.8%] in 2006 to 14/37 [37.8%] in 2018; P = .005). There was no significant change in the proportion of women last authors in any of the three journals. There were no statistically significant changes in JGIM authorship over time.

Clinical problem-solving exercises provide a forum for physicians to demonstrate diagnostic reasoning skills and clinical acumen. In this study, we focused on three prominent clinical problem-solving series in general medicine journals. We found that women authors were underrepresented in each series. The percentage of women authors has increased over time, especially among first and all authors; however, there was no change in the last author position. In all three series women still constituted less than 40% of all authors and less than 25% of last authors. In comparison, women currently constitute about 40% of general internal medicine physicians, and this proportion has been rapidly growing over time; women now represent over half of all medical school graduates as opposed to 6% in 1960.^9,10 Our findings are consistent with the large body of evidence that describes gender-based differences in opportunities within academic medicine.

Prior studies have shown that gender inequities in academic medicine stem from a longstanding culture of sexism; these inequities are perpetuated in part by having too few visible women role models and mentors.¹¹ These factors may lead to editorial practices that favor articles written by men. In addition, women may be less likely to be invited as expert discussants if other authors have a bias of associating clinical expertise with men physicians. This is consistent with data showing that women are less likely to be invited to write commentaries in peer-reviewed journals.¹²

Gender-based differences in authorship of clinical problem-solving publications also have important implications for women in medicine. In order to address the gender gap in academic achievement, women need visible role models and mentors.¹³ Including more women authors of clinical reasoning publications has the potential to establish more women as master clinicians and role models.

There are a number of actions that can help establish more women clinical problem-solving authors. Editorial boards and editors in chief should track their review and publication practices to hold themselves accountable to author diversity. For example, JHM has announced plans to analyze author representation of women and racial and ethnic minorities, including those among first and senior authors.¹⁴ Clinicians who are assembling author teams for clinical problem-solving manuscripts should also strongly consider if an equal number of men and women have been invited to serve as specialty consultants and case discussants.

Our study has limitations. We used a python library to classify author gender based on first name (supplemented by internet searches), which may have misclassified authors and did not take into account nonbinary gender identities. Because there is no convention for assigning the expert discussant to a specific author position, we could not determine the gender distribution of the discussants. However, given that women were underrepresented among first, last, and all authors in all three journals, they are likely a minority of discussants as well.

A preponderance of male voices in clinical reasoning exercises, in which learners see clinical role models, may perpetuate a culture in which women are not seen—and do not see themselves—as having the potential to be master clinicians. Including more women in clinical reasoning exercises is an opportunity to amplify the voices of women as master clinicians and combat gender discrimination in medicine.

A large body of evidence has demonstrated significant gender disparities in academic medicine. Women are less likely than men to reach the rank of full professor, be speakers at Grand Rounds, and author studies in medical journals.^1-4 Gender-based differences in these achievements reduce the visibility of women role models in all academic medicine domains, including research, education, health systems leadership, and clinical excellence. Clinical problem-solving exercises are an opportunity to highlight the skills of women physicians as master clinicians and to establish women as clinician role models.

Clinical problem-solving exercises are highly visible demonstrations of clinical excellence in the medical literature. These exercises follow a specific format in which a clinician analyzes a diagnostic dilemma in a step-by-step manner in response to sequential segments of clinical data. The clinical problem-­solving format was introduced in 1992 in the New England Journal of Medicine and has been adopted by other journals.⁵ (The clinical problem-solving format differs from the clinical pathologic conference format, in which an entire case is presented followed by an extended analysis). Clinical problem-­solving publications are forums for learners of all levels to witness an expert clinician reason through a case.

Authorship teams on clinical reasoning exercises typically include the patient’s physician(s), specialists relevant to the final diagnosis, and the invited discussant who analyzes the clinical dilemma. Journals stipulate in the author instructions, series introductions, or standardized manuscript text of the series that the discussant be a skilled and experienced clinician.^5,6 The patient’s physicians who initiate the clinical reasoning manuscript typically select the discussant; in some journals, the series editors may provide input on discussant choice. To our knowledge, this is the only author role in the medical literature in which authors are invited specifically for their diagnostic reasoning ability.

While women have been authors on fewer original research articles and guest editorials than men have,³ the proportion of women among authors of published clinical reasoning exercises is unknown. This represents a gap in our understanding of the landscape of gender inequity in academic medicine. We sought to determine the proportion of women authors in major clinical problem-solving series and examine the change in women authorship over time.

We selected published clinical problem-solving series targeting a general medicine audience. We excluded general medicine journals in which authors were restricted to one institution or those in which the clinical problem-solving format was not a regular series. Series which met these criteria were the Clinical Problem-Solving series in the New England Journal of Medicine (NEJM), the Clinical Care Conundrums series in the Journal of Hospital Medicine (JHM), and the Exercises in Clinical Reasoning series in the Journal of General Internal Medicine (JGIM). We analyzed the proportion of women authors in each clinical reasoning series from the inaugural articles (1992 for NEJM, 2006 for JHM, and 2010 for JGIM) until July 2019. We also analyzed the change in proportion of women authors from year to year by using data up to 2018 to avoid including a partial year.

We used the gender-guesser python library⁷ to categorize the gender of first, last, and all authors based on their first names. The library uses a database of approximately 40,000 names⁸ and maps first names to the genders they are associated with across languages, classifying each name as “man,” “woman,” “mostly man,” ”mostly woman,” “androgynous,” or “unknown.” When a name is commonly associated with multiple genders, or is associated with different genders in different languages, it is classified either as mostly man, mostly woman, or androgynous. When a name is not found in the database, it is classified as unknown. For all names classified by the database as unknown, androgynous, or mostly man/mostly woman, we determined gender identities by finding the authors’ institutional webpages and consulting their listed gender pronouns. We used gender based on first name to best approximate what a reader would interpret as the author’s gender. We used gender rather than biological sex because authors may have changed their names to better express their gender identity, which may differ from sex assigned at birth.

To test for the statistical significance of changes in the proportion of women authors over time, we performed the Cochran-­Armitage trend test. A P value less than .05 was considered significant.

We analyzed 402 articles: 280 from NEJM, 83 from JHM, and 39 from JGIM. There were 1,026 authors of clinical reasoning articles from NEJM, 362 from JHM, and 168 from JGIM. The Table shows the number of total articles, total authors, and women among first, last, and all authors by journal and by year (inaugural year and 2018). Data for all years are shown in the Appendix Table.

Over the entire time period studied, the percentage of women across the three journals was lowest for last authors (28/280 [10.0%] for NEJM, 6/83 [7.2%] for JHM, and 9/39 [23.1%] for JGIM) and highest for first authors (80/280 [28.6%] for NEJM, 36/83 [43.4%] for JHM, and 13/39 [33.3%] for JGIM). The percentage of women among all authors was similar for all three journals: 224/1,026 (21.8%) for NEJM, 83/362 (22.9%) for JHM, and 36/168 (21.4%) for JGIM.

The Figure shows the change in percentage of women authors from year to year through 2018. There was a significant increase in the proportion of women first authors in NEJM (from 0/12 [0.0%] in 1992 to 4/12 [33.3%] in 2018; P < .0001) and JHM (from 2/5 [40.0%] in 2006 to 7/9 [77.8%] in 2018 P = .01). There was also a significant increase in the proportion of women among all authors in NEJM (from 0/17 [0.0%] in 1992 to 17/59 [28.8%] in 2018; P < .0001) and JHM (from 3/19 [15.8%] in 2006 to 14/37 [37.8%] in 2018; P = .005). There was no significant change in the proportion of women last authors in any of the three journals. There were no statistically significant changes in JGIM authorship over time.

Clinical problem-solving exercises provide a forum for physicians to demonstrate diagnostic reasoning skills and clinical acumen. In this study, we focused on three prominent clinical problem-solving series in general medicine journals. We found that women authors were underrepresented in each series. The percentage of women authors has increased over time, especially among first and all authors; however, there was no change in the last author position. In all three series women still constituted less than 40% of all authors and less than 25% of last authors. In comparison, women currently constitute about 40% of general internal medicine physicians, and this proportion has been rapidly growing over time; women now represent over half of all medical school graduates as opposed to 6% in 1960.^9,10 Our findings are consistent with the large body of evidence that describes gender-based differences in opportunities within academic medicine.

Prior studies have shown that gender inequities in academic medicine stem from a longstanding culture of sexism; these inequities are perpetuated in part by having too few visible women role models and mentors.¹¹ These factors may lead to editorial practices that favor articles written by men. In addition, women may be less likely to be invited as expert discussants if other authors have a bias of associating clinical expertise with men physicians. This is consistent with data showing that women are less likely to be invited to write commentaries in peer-reviewed journals.¹²

Gender-based differences in authorship of clinical problem-solving publications also have important implications for women in medicine. In order to address the gender gap in academic achievement, women need visible role models and mentors.¹³ Including more women authors of clinical reasoning publications has the potential to establish more women as master clinicians and role models.

There are a number of actions that can help establish more women clinical problem-solving authors. Editorial boards and editors in chief should track their review and publication practices to hold themselves accountable to author diversity. For example, JHM has announced plans to analyze author representation of women and racial and ethnic minorities, including those among first and senior authors.¹⁴ Clinicians who are assembling author teams for clinical problem-solving manuscripts should also strongly consider if an equal number of men and women have been invited to serve as specialty consultants and case discussants.

Our study has limitations. We used a python library to classify author gender based on first name (supplemented by internet searches), which may have misclassified authors and did not take into account nonbinary gender identities. Because there is no convention for assigning the expert discussant to a specific author position, we could not determine the gender distribution of the discussants. However, given that women were underrepresented among first, last, and all authors in all three journals, they are likely a minority of discussants as well.

A preponderance of male voices in clinical reasoning exercises, in which learners see clinical role models, may perpetuate a culture in which women are not seen—and do not see themselves—as having the potential to be master clinicians. Including more women in clinical reasoning exercises is an opportunity to amplify the voices of women as master clinicians and combat gender discrimination in medicine.

A large body of evidence has demonstrated significant gender disparities in academic medicine. Women are less likely than men to reach the rank of full professor, be speakers at Grand Rounds, and author studies in medical journals.^1-4 Gender-based differences in these achievements reduce the visibility of women role models in all academic medicine domains, including research, education, health systems leadership, and clinical excellence. Clinical problem-solving exercises are an opportunity to highlight the skills of women physicians as master clinicians and to establish women as clinician role models.

Clinical problem-solving exercises are highly visible demonstrations of clinical excellence in the medical literature. These exercises follow a specific format in which a clinician analyzes a diagnostic dilemma in a step-by-step manner in response to sequential segments of clinical data. The clinical problem-­solving format was introduced in 1992 in the New England Journal of Medicine and has been adopted by other journals.⁵ (The clinical problem-solving format differs from the clinical pathologic conference format, in which an entire case is presented followed by an extended analysis). Clinical problem-­solving publications are forums for learners of all levels to witness an expert clinician reason through a case.

Authorship teams on clinical reasoning exercises typically include the patient’s physician(s), specialists relevant to the final diagnosis, and the invited discussant who analyzes the clinical dilemma. Journals stipulate in the author instructions, series introductions, or standardized manuscript text of the series that the discussant be a skilled and experienced clinician.^5,6 The patient’s physicians who initiate the clinical reasoning manuscript typically select the discussant; in some journals, the series editors may provide input on discussant choice. To our knowledge, this is the only author role in the medical literature in which authors are invited specifically for their diagnostic reasoning ability.

While women have been authors on fewer original research articles and guest editorials than men have,³ the proportion of women among authors of published clinical reasoning exercises is unknown. This represents a gap in our understanding of the landscape of gender inequity in academic medicine. We sought to determine the proportion of women authors in major clinical problem-solving series and examine the change in women authorship over time.

We selected published clinical problem-solving series targeting a general medicine audience. We excluded general medicine journals in which authors were restricted to one institution or those in which the clinical problem-solving format was not a regular series. Series which met these criteria were the Clinical Problem-Solving series in the New England Journal of Medicine (NEJM), the Clinical Care Conundrums series in the Journal of Hospital Medicine (JHM), and the Exercises in Clinical Reasoning series in the Journal of General Internal Medicine (JGIM). We analyzed the proportion of women authors in each clinical reasoning series from the inaugural articles (1992 for NEJM, 2006 for JHM, and 2010 for JGIM) until July 2019. We also analyzed the change in proportion of women authors from year to year by using data up to 2018 to avoid including a partial year.

We used the gender-guesser python library⁷ to categorize the gender of first, last, and all authors based on their first names. The library uses a database of approximately 40,000 names⁸ and maps first names to the genders they are associated with across languages, classifying each name as “man,” “woman,” “mostly man,” ”mostly woman,” “androgynous,” or “unknown.” When a name is commonly associated with multiple genders, or is associated with different genders in different languages, it is classified either as mostly man, mostly woman, or androgynous. When a name is not found in the database, it is classified as unknown. For all names classified by the database as unknown, androgynous, or mostly man/mostly woman, we determined gender identities by finding the authors’ institutional webpages and consulting their listed gender pronouns. We used gender based on first name to best approximate what a reader would interpret as the author’s gender. We used gender rather than biological sex because authors may have changed their names to better express their gender identity, which may differ from sex assigned at birth.

To test for the statistical significance of changes in the proportion of women authors over time, we performed the Cochran-­Armitage trend test. A P value less than .05 was considered significant.

We analyzed 402 articles: 280 from NEJM, 83 from JHM, and 39 from JGIM. There were 1,026 authors of clinical reasoning articles from NEJM, 362 from JHM, and 168 from JGIM. The Table shows the number of total articles, total authors, and women among first, last, and all authors by journal and by year (inaugural year and 2018). Data for all years are shown in the Appendix Table.

Over the entire time period studied, the percentage of women across the three journals was lowest for last authors (28/280 [10.0%] for NEJM, 6/83 [7.2%] for JHM, and 9/39 [23.1%] for JGIM) and highest for first authors (80/280 [28.6%] for NEJM, 36/83 [43.4%] for JHM, and 13/39 [33.3%] for JGIM). The percentage of women among all authors was similar for all three journals: 224/1,026 (21.8%) for NEJM, 83/362 (22.9%) for JHM, and 36/168 (21.4%) for JGIM.

The Figure shows the change in percentage of women authors from year to year through 2018. There was a significant increase in the proportion of women first authors in NEJM (from 0/12 [0.0%] in 1992 to 4/12 [33.3%] in 2018; P < .0001) and JHM (from 2/5 [40.0%] in 2006 to 7/9 [77.8%] in 2018 P = .01). There was also a significant increase in the proportion of women among all authors in NEJM (from 0/17 [0.0%] in 1992 to 17/59 [28.8%] in 2018; P < .0001) and JHM (from 3/19 [15.8%] in 2006 to 14/37 [37.8%] in 2018; P = .005). There was no significant change in the proportion of women last authors in any of the three journals. There were no statistically significant changes in JGIM authorship over time.

Clinical problem-solving exercises provide a forum for physicians to demonstrate diagnostic reasoning skills and clinical acumen. In this study, we focused on three prominent clinical problem-solving series in general medicine journals. We found that women authors were underrepresented in each series. The percentage of women authors has increased over time, especially among first and all authors; however, there was no change in the last author position. In all three series women still constituted less than 40% of all authors and less than 25% of last authors. In comparison, women currently constitute about 40% of general internal medicine physicians, and this proportion has been rapidly growing over time; women now represent over half of all medical school graduates as opposed to 6% in 1960.^9,10 Our findings are consistent with the large body of evidence that describes gender-based differences in opportunities within academic medicine.

Prior studies have shown that gender inequities in academic medicine stem from a longstanding culture of sexism; these inequities are perpetuated in part by having too few visible women role models and mentors.¹¹ These factors may lead to editorial practices that favor articles written by men. In addition, women may be less likely to be invited as expert discussants if other authors have a bias of associating clinical expertise with men physicians. This is consistent with data showing that women are less likely to be invited to write commentaries in peer-reviewed journals.¹²

Gender-based differences in authorship of clinical problem-solving publications also have important implications for women in medicine. In order to address the gender gap in academic achievement, women need visible role models and mentors.¹³ Including more women authors of clinical reasoning publications has the potential to establish more women as master clinicians and role models.

There are a number of actions that can help establish more women clinical problem-solving authors. Editorial boards and editors in chief should track their review and publication practices to hold themselves accountable to author diversity. For example, JHM has announced plans to analyze author representation of women and racial and ethnic minorities, including those among first and senior authors.¹⁴ Clinicians who are assembling author teams for clinical problem-solving manuscripts should also strongly consider if an equal number of men and women have been invited to serve as specialty consultants and case discussants.

Our study has limitations. We used a python library to classify author gender based on first name (supplemented by internet searches), which may have misclassified authors and did not take into account nonbinary gender identities. Because there is no convention for assigning the expert discussant to a specific author position, we could not determine the gender distribution of the discussants. However, given that women were underrepresented among first, last, and all authors in all three journals, they are likely a minority of discussants as well.

A preponderance of male voices in clinical reasoning exercises, in which learners see clinical role models, may perpetuate a culture in which women are not seen—and do not see themselves—as having the potential to be master clinicians. Including more women in clinical reasoning exercises is an opportunity to amplify the voices of women as master clinicians and combat gender discrimination in medicine.