Hospital Medicine

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

As the population of the United States continues to age, hospitals are seeing an increasing number of older patients with significant medical and social complexity. Medicare data have shown that an increasing number require post–acute care after a hospitalization.¹ Discharges to post–acute care settings are often longer and more costly compared with discharges to other settings, which suggests that targeting quality improvement efforts at this transition period may improve the value of care.²

The transition from the hospital setting to a post–acute care facility can be dangerous and complicated due to lapses in communication, medication errors, and the complexity of medical treatment plans. Suboptimal transitions in care can result in adverse events for the patient, as well as confusion in medication regimens or incomplete plans for follow-up care.³

The Project ECHO (Extension for Community Healthcare Outcomes) model was first developed and launched by Sanjeev Arora, MD, in New Mexico in 2003 to expand access to subspecialist care using videoconferencing.⁴ We first applied this model in 2013 to evaluate the impact of this interdisciplinary videoconferencing tool on the care of patients discharged to post–acute settings.⁵ We found that patients participating in the Extension for Community Healthcare Outcomes–Care Transitions (ECHO-CT) model experienced decreased risk of rehospitalization, decreased skilled nursing facility (SNF) length of stay, and reduced 30-day healthcare costs, compared with those patients not enrolled in this program; these outcomes were likely due to identification and correction of medication-related errors, improved care coordination, improved disease management, and clarification of goals of care.⁶ Though these investigations did identify some issues arising during the care transition process, they did not fully describe the types of problems uncovered. We sought to better characterize the clinical and operational issues identified through the ECHO-CT conference, hereafter known as transition-of-care events (TCEs). These issues may include new or evolving medical concerns, an adverse event, or a “near miss.” Identification and classification of TCEs that may contribute to unsafe or fractured care transitions are critical in developing systematic solutions to improve transitions of care, which can ultimately improve patient safety and potentially avoid preventable errors.

ECHO-CT Multidisciplinary Video Conference

We conducted ECHO-CT at a large, tertiary care academic medical center. The project design for the ECHO-CT program has been previously described.⁵ In brief, the program is a weekly, multidisciplinary videoconference between a hospital-based team and post–acute care providers to discuss patients discharged from inpatient services to post–acute care sites, including SNFs and long-term acute care hospitals (LTACHs), during the preceding week. All patients discharged from the tertiary care inpatient site to one of the eight participating SNFs or LTACHs, from either a medical or surgical service, are eligible to be discussed at this weekly interdisciplinary conference. Long-term care facilities were not included in this study. The ECHO-CT program used HIPAA (Health Insurance Portability and Accountability Act)-compliant videoconferencing technology to connect hospital and post–acute care providers.

During the videoconferences, each patient’s hospital course and discharge documentation are reviewed by a hospitalist, and a pharmacist performs a medication reconciliation of each patient’s admission, discharge, and post–acute care medication list. The discharging attending, primary care providers, residents, other trainees, and subspecialist providers are invited to attend. Typically, the interdisciplinary team at the post–acute care sites includes physicians, nurse practitioners, physical therapists, social workers, and case managers. Between 10 and 20 patients are discussed in a case-based format, which includes a summary of the patient’s hospital course, an update from the post–acute care team on the patient’s care, and an opportunity for a discussion regarding any concerns or questions raised by the post–acute care or inpatient care teams. The content and duration of discussion typically lasts approximately 3 to 10 minutes, depending on the needs of the patient and the care team. Each of the eight post–acute care sites participating in the project are assigned a 10- to 15-minute block. A copy of the ECHO-CT session process document is included in the Appendix.

Data Collection

At each interdisciplinary patient review, TCEs were identified and recorded. These events were categorized in real time by the ECHO-CT data collection team into the following categories: medication related, medical, discharge communication/coordination, or other, and recorded in a secured, deidentified database. For individuals whose TCEs could represent more than one category, authors reviewed the available information about the TCEs and determined the most appropriate category; if more than one category was felt to be applicable to a patient’s situation, the events were reclassified into all applicable categories. Data about individual patients, including gender, age at the time of discharge, and other demographic information, were obtained from hospital databases. Number of diagnoses included any diagnosis billed during the patient’s hospital stay, and these data were obtained from a hospital billing database. Average number of medications at discharge was obtained from a hospital pharmacy database.

A total of 675 patients (experiencing 743 hospitalizations) were discharged from a medical or surgical service to one of the participating post–acute care sites from January 2016 to October 2018, and were discussed at the interdisciplinary conference. During that time, 139 TCEs were recorded for review, involving 132 patients (Table 1). Patients who experienced TCEs were noted to have a slightly higher average number of diagnoses than did those in the non-TCE group (21 vs 18, respectively) and number of medications (18 vs 15).

Representative examples of TCEs are provided in Table 2. Fifty-eight issues were identified as discharge communication or coordination issues (eg, discharge summary was late or missing at time of discharge to facility, transitional issues were unclear, follow-up appointments were not appropriately scheduled or documented). An additional 52 TCEs were identified as pharmacy or medication issues (eg, medications were inadvertently omitted from discharge medication list, prehospital medication list was incorrect). Medical issues accounted for an additional 27 concerns (eg, patient was hypoglycemic on arrival, inadequate pain control, discovery of new acute medical issues or medical diagnoses that were not clearly documented or communicated by the inpatient team). “Other” issues (two) included unaddressed social concerns, such as insurance issues.

The ECHO-CT model unites hospital and post–acute care providers to improve transitions of care and is unique in its focus on the transition from hospital to post–acute care rather than to home care. In 2 years of data collection, we identified several TCEs encompassing a range of concerns. Of the 675 patients discussed, 132 (20%) were noted to have a TCE. When these percentages are applied to the 140 million Medicare hospital discharges that took place during 2000 to 2015, we would estimate nearly 5.5 million TCEs, or 375,000 TCEs per year, that may have affected this population.

The majority of TCEs were communication and coordination errors. Missing or incomplete discharge paperwork, inadequate documentation of inpatient care, and confusion about medical devices or postoperative needs (eg, slings, braces, wound care, drains) were commonly reported. Follow-up appointments with specialists were often not appropriately scheduled or communicated. This may have resulted from unstandardized discharge documentation and a lower priority given to documentation in the setting of multiple clinical demands (eg, direct patient care, complex care coordination, and clinical paperwork and charting). Studies have demonstrated that fewer than one-third of discharge summaries are received by outpatient providers before postdischarge follow-up, and additionally that nearly 40% of patients did not undergo recommended workups for medical issues identified during their hospital stay.^7,8 All of this is problematic because appropriate documentation in discharge summaries is associated with a decreased risk of hospital readmission.⁹

Pharmacy issues were the second most common TCE identified. One member of the post–acute care team noted that “omissions, additions, and replacements” relating to medications were common occurrences. Additionally, it was noted that medications were inadvertently continued for longer than planned or not adjusted appropriately with changing clinical parameters, such as renal function. The results of our analysis are consistent with current literature, which suggests that up to 60% of all medication errors occur during the period surrounding transitions of care.¹⁰

There were several limitations to this investigation. Though recording of identified TCEs occurred in real time, analysis of these identified events occurred retrospectively; therefore, investigators had limited ability to retroactively review or recategorize recorded issues, which potentially could have resulted in misclassification or misinterpretation. Additionally, the data were intended to be descriptive; therefore, outcomes such as hospital readmission and patient harm could not be linked to specific TCEs. Furthermore, it is possible that events were not detected by either the postdischarge team or the hospital-based team and, therefore, not captured in this analysis. Further work would be helpful to determine the root causes underlying the identified issues in care transitions, with the goal of improving patient safety and avoiding preventable errors during transitions of care. Although there is comprehensive literature related to errors and medication-related adverse events,¹¹ there is not a consensus of how to classify and report, in a standardized fashion, events arising during the transition period. A validated structure for systematically identifying, monitoring, recording, and reporting issues arising during care transitions will be critical in preventing errors and ensuring patient safety during this high-risk period.

Our model is a unique intervention that uses the expertise and engagement of an interdisciplinary team and seeks to identify and remedy issues arising during transitions of care—in real time—to prevent direct harm to vulnerable patients. We have already implemented interventions to improve care based on our experiences with this videoconference-based program. For example, direct feedback was given to discharging teams to improve the discharge summary and associated documentation, and changes to the medication-ordering system were implemented to address specific medication errors discovered. The TCEs identified in this investigation highlight specific areas for improvement with the goal of providing high-quality care for patients and seamless transitions to post–acute care. As health systems and hospitals face new challenges in communication and care coordination, especially due to the recent COVID-19 pandemic, the technology and communication methods used in the ECHO-CT model may become even more relevant for promoting clear communication and patient safety during transitions of care.

The ECHO CT team thanks Sabrina Carretie for her contributions in data collection and analysis.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Anaphylaxis, an acute, life-threatening allergic response, affects multiple organ systems and manifests variably. Anaphylaxis is likely taking place if one or more of the following occurs: (a) sudden- onset skin and mucosal tissue swelling, (b) skin and mucosal abnormalities or respiratory or gastrointestinal symptoms after exposure to an allergen, or (c) reduced blood pressure after exposure to an allergen. With an estimated lifetime prevalence of up to 5.1%, it is a significant cause of morbidity in adults and children.¹ The 2020 anaphylaxis practice parameter update provides recommendations on treatment, prevention, and assessment of biphasic symptom risk in patients experiencing anaphylaxis.² The guideline provides five key recommendations and four good-practice statements, which we have consolidated into five recommendations for this update.

Recommendation 1. All patients with suspected or confirmed anaphylaxis should be treated with epinephrine. (Good-practice statement)

Self-injectable epinephrine is the first-line treatment for anaphylaxis, with weight-based dosing of 0.15 mg/kg for children weighing less than 30 kg and 0.30 mg/kg for children weighing more than 30 kg and adults. Delayed administration of epinephrine can increase anaphylaxis-associated morbidity and mortality. After epinephrine administration, patients should be observed in a healthcare setting for symptom resolution.

Recommendation 2. For all patients, clinicians should assess the risk for developing biphasic anaphylaxis. (Conditional recommendation, very low quality of evidence)

Biphasic anaphylaxis is defined as the return of anaphylaxis symptoms after an asymptomatic period of at least 1 hour, all during a single instance of anaphylaxis. Biphasic anaphylaxis occurs in up to 20% of patients.³ Biphasic anaphylaxis is more likely among patients receiving repeated doses of epinephrine (odds ratio [OR], 4.82; 95% CI, 2.70-8.58), delayed epinephrine administration greater than 60 minutes (OR, 2.29; 95% CI, 1.09-4.79), or a severe initial presentation (OR, 4.82; 95% CI, 1.23-3.61).² The presence of any of these risk factors raises the risk for developing biphasic anaphylaxis by 17%.⁴ Severe anaphylaxis is characterized by life-threatening symptoms, including loss of consciousness, syncope or dizziness, hypotension, cardiovascular system collapse, or neurologic dysfunction from hypoperfusion or hypoxia after exposure to an allergen.⁵

Other risk factors for biphasic anaphylaxis in all ages include a widened pulse pressure, unknown anaphylaxis trigger, and cutaneous signs and symptoms. Drug triggers are also a risk factor in pediatric patients.²

Recommendation 3. All patients with anaphylaxis and risk factors for biphasic anaphylaxis should undergo extended clinical observation in a setting capable of managing anaphylaxis. (Conditional recommendation, very low quality of evidence)

All patients should be monitored for resolution of symptoms prior to discharge, regardless of age or severity at onset. Patients with all three of the following can be discharged 1 hour after symptom resolution because these three factors together have a 95% negative predictive value for biphasic anaphylaxis: nonsevere anaphylaxis, prompt response to epinephrine, and access to medical care.⁵ In contrast, extended observation of at least 6 hours should be offered to patients with increased risk of biphasic reactions. Patients who have potentially fatal underlying illnesses (eg, severe respiratory or cardiac disease), poor access to emergency medical services, poor self-management skills, or inability to access epinephrine should also be considered for extended observation or hospitalization. Evidence is lacking to define the optimal observation time because extended biphasic reactions can occur from 1 to 78 hours after initial anaphylaxis symptoms.⁶

Given the lack of specific evidence around length of observation, there is an opportunity for shared decision-making. Every patient should receive education regarding trigger avoidance, reasons to seek care or activate emergency medical services, and warning signs of biphasic anaphylaxis. Additionally, self-injectable epinephrine and an action plan detailing how and when to administer the epinephrine should be provided. Patients with anaphylaxis should follow up with an allergist.

Recommendation 4. Administration of glucocorticoids or antihistamines for prevention of biphasic anaphylaxis is not recommended. (Conditional recommendation, very low quality of evidence)

This guideline discourages glucocorticoids and antihistamines as a primary treatment as it may delay epinephrine administration. Despite treating the cutaneous manifestations of anaphylaxis, antihistamines fail to treat the life-threatening cardiovascular and respiratory symptoms. No clear evidence exists on whether antihistamines or glucocorticoids prevent biphasic anaphylaxis.

Recommendation 5. In adult patients receiving chemotherapy, premedication with antihistamine and/or glucocorticoid should be used to prevent anaphylaxis or infusion-related reactions for some chemotherapeutic agents in patients with no previous reaction to the drug. (Conditional recommendation, very low quality of evidence)

Premedication with antihistamines and/or glucocorticoids was associated with 51% reduced odds for anaphylaxis and infusion-related reactions to certain chemotherapy agents (pegaspargase, docetaxel, carboplatin, oxaliplatin, and rituximab) in adults who had not previously experienced a reaction to the drug (OR, 0.49; 95% CI, 0.37-0.66).² However, this same benefit was not found with other chemotherapy agents for patients without a prior allergic reaction to the agent, which allows clinicians to defer premedication. The benefit of premedication with antihistamines and/or glucocorticoids to patients with prior anaphylactic reactions to chemotherapy agents was not evaluated in this guideline, nor was the role premedication plays in desensitization to chemotherapy.

This guideline was created by a panel of allergists, clinical immunologists, and methodologists using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach to draft recommendations. Conflicts of interest (COI) were disclosed by all panel members according to the American Academy of Allergy, Asthma, and Immunology (AAAAI) guidelines. The inclusion of many observational studies and meta-analyses improves the generalizability of the guideline. The authors highlighted the low certainty of evidence due to the lack of randomized controlled trials and significant heterogeneity of the included studies.

Some recommendations in the guideline have implications for costs of care. A recent economic analysis looked at cost-effectiveness for extended observation for anaphylaxis and found it was cost-effective only when patients were at increased risk for biphasic anaphylaxis.⁷ Although Recommendation 4 advises against the use of glucocorticoids for prevention of biphasic anaphylaxis, one retrospective cohort study demonstrated that glucocorticoid use was associated with decreased length of stay in children admitted with anaphylaxis.⁸ Therefore, the recommendation to avoid glucocorticoids for prevention of biphasic anaphylaxis could possibly increase hospital length of stay for children. The usefulness of dexamethasone to prevent biphasic anaphylaxis in children 3 to 14 months old is being evaluated in a randomized trial (ClinicalTrials.gov, NCT03523221).

Future research should better characterize risk factors for biphasic reactions to aid in clinical triage and diagnosis. Additional studies are needed to determine the optimal observation duration for patients experiencing anaphylactic reactions or requiring multiple doses of epinephrine. The role of premedication in patients receiving chemotherapy is poorly described, with few studies evaluating the benefit of premedication in patients with previous anaphylactic reactions.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

Osteoporosis is the most prevalent bone disease and a leading cause of morbidity and mortality in older people. According to the National Health and Nutrition Examination Survey, from 2005-2010, there were an estimated 10.2 million adults 50 years and older with osteoporosis and 43.4 million more with low bone mass in the United States.¹ Osteoporotic fracture is a leading cause of hospitalization in the United States for women 55 years or older, ahead of heart attacks, stroke, and breast cancer.² Despite elucidation of the pathogenesis of osteoporosis and the advent of effective and widely available therapies, a “treatment gap” separates the many patients who warrant therapy from the few who receive it. Systematic improvement strategies, such as coordinator-based fracture liaison services, have had a positive impact on addressing this treatment gap.³ There is an opportunity for hospitalists to further narrow this treatment gap.

The American Society of Bone and Mineral Research, in conjunction with the Center for Medical Technology Policy, developed consensus clinical recommendations to address secondary fracture prevention for people 65 years or older who have experienced a hip or vertebral fracture.⁴ We address six of the fundamental and two of the supplemental recommendations as they apply to the practice of hospital medicine.

Recommendations 1 and 2

Communicate key information to the patient and their usual healthcare provider. Patients 65 years or older with a hip or vertebral fracture likely have osteoporosis and are at high risk for subsequent fractures, which can lead to a decline in function and an increase in mortality. Patients must be counseled regarding their diagnosis, their risks, and the actions they can take to manage their disease. Primary care providers must be notified of the occurrence of the fracture, the diagnosis of osteoporosis, and the plans for management.

We recommend hospitalists act as leading advocates for at-risk patients to ensure that this communication occurs during hospitalization. We encourage hospitals and institutions to adopt systematic interventions to facilitate postdischarge care for these patients. These may include implementing a fracture liaison service, with multidisciplinary secondary fracture–prevention strategies using physicians, pharmacists, nurses, social workers, and case managers for care coordination and treatment initiation.

Elderly patients with osteoporotic fragility fractures are at risk for further morbidity and mortality. Coordination of care between the inpatient care team and the primary care provider is necessary to reduce this risk. In addition to verbal communication and especially when verbal communication is not feasible, discharge documents provided to patients and outpatient providers should clearly identify the occurrence of a hip or vertebral fracture and a discharge diagnosis of osteoporosis if not previously documented, regardless of bone mineral density (BMD) results or lack of testing.

Recommendation 3

Regularly assess fall risk. Patients 65 years or older with a current or prior hip or vertebral fracture must be regularly assessed for risk of falls. Hospitalists can assess patients’ ongoing risk for falls at time of admission or during hospitalization. Risk factors include prior falls; advanced age; visual, auditory, or cognitive impairment; decreased muscle strength; gait and balance impairment; diabetes mellitus; use of multiple medications, and others.⁵ Specialist evaluation by a physical therapist or a physiatrist should be considered. Active medications should be reviewed for adverse effects and interactions. The use of diuretics, antipsychotics, antidepressants, benzodiazepines, antiepileptics, and opioids should be minimized.

Recommendations 4, 5, 6, and 11

Offer pharmacologic therapy and initiate calcium and vitamin D supplementation. Recommendations 4 through 6 and 11 advocate pharmacologic interventions including bisphosphonates, denosumab, vitamin D, and/or calcium to reduce the risk of future fractures. Bisphosphonates are the cornerstone of pharmacologic therapy for secondary fracture prevention. The efficacy of these agents for prevention of subsequent fractures outweighs the potential for interference in healing of surgically repaired bones.⁶ Oral bisphosphonate therapy should be initiated in the hospital or at discharge. Parenteral bisphosphonates and denosumab may be utilized in patients unable to tolerate or absorb oral bisphosphonates due to esophageal or other gastrointestinal disease. Initiation of these agents should be delayed until after vitamin D and calcium supplementation have been administered for 2 weeks after the fracture to reduce the risk of precipitating hypocalcemia, and they should not be used in patients with confirmed hypocalcemia until that is resolved. BMD measurement is not necessary prior to pharmacologic therapy initiation because the risk of fracture is elevated for these patients regardless of BMD. Patients without significant dental disease or planned oral or maxillofacial procedures may begin bisphosphonate therapy prior to a full dental assessment because risk of osteonecrosis of the jaw is low.

The guidelines recommend people 65 years or older with a hip or vertebral fracture receive daily supplementation of at least 800 IU vitamin D. Patients unable to achieve an intake of 1,200 mg/day of calcium from food sources should receive daily calcium supplementation. The effect of vitamin D monotherapy on fracture risk is not clear; however, strong evidence suggests that fracture risk is reduced when individuals at high risk of deficiency receive supplementation with vitamin D and calcium. Calcium supplementation alone has not demonstrated reduction in fracture risk. Total daily calcium intake above 1,500 mg has not been shown to provide additional benefit and is potentially harmful.

Recommendation 9

Counsel patients on lifestyle modifications and consider physical therapy. Tobacco has a deleterious effect on bone density and increases risk for osteoporotic fragility fracture.⁷ Hospitalists should obtain tobacco use history from all patients with an osteoporotic fracture and provide tobacco cessation counseling when appropriate. Excessive alcohol consumption increases the risk of fall injuries.⁸ Hospitalists should counsel patients to limit alcohol intake to a maximum of two drinks a day for men and one drink a day for women.

Weight-bearing and strength-training exercises, particularly those involving balance and trunk muscle strength, are associated with reduction in fall-risk. Exercise must be tailored to the patient’s physical capacity. Hospitalists may partner with physical therapists or physiatrists to facilitate development of an exercise plan to maximize benefit and minimize risk of injury.

We found this document to be highly informative and well cited, with ample evidence to support the recommendations.

Methods in Preparing Guidelines

The multistakeholder coalition did not employ a rigorous and standardized methodology for the guideline, such as GRADE (Grading of Recommendations Assessment, Development, and Evaluation); hence, no assessment of evidence quality, benefits and harms of an intervention, or resource use was provided.

Potential Conflicts for Guideline Authors

Eight guideline authors have pharmaceutical relationships with the manufacturer of one of the medications listed on the guidelines (Amgen-denosumab, Novartis-zoledronic acid). There are no disclosures reported from the multistakeholder coalition members who are not listed as guideline authors.

We anticipate future studies may report outcomes focused on secondary prevention of fractures. Additionally, we would like to see new studies investigating patient-centered outcomes such as improvement in functional status and ambulatory independence based on improved postfracture medical therapies. We see an opportunity for studies assessing real-world outcomes to inform future recommendations, particularly after widespread implementation of secondary fracture prevention therapy either initiated during hospitalization or purposefully planned for after discharge.

We would like to see more trial data comparing the safety and cost-effectiveness of first-line therapy, namely oral bisphosphonates, to alternative treatments, particularly parenteral agents, which may improve treatment compliance because of the convenience in dosing frequency.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

It was a busy night in the emergency department. EMS called to give a heads up—they were on their way with a girl who was “pretty banged up.” They warned us that the story seemed a little fishy. We thought we were ready. The trauma bay was organized; supplies were at the ready and everyone had a role. Within seconds of her arrival, it was clear that no one could ever have been truly prepared. She was unresponsive and unstable. Her injuries were widespread, brutal, and long term. My seasoned attendings would describe it as no less than horrific. There was no question—someone had done this to her.

After she was stabilized, her wounds were gently tended, her body was bathed, her hair was combed. She died several days later. While distressed, many members of her team took consolation in the idea that, after years of torture, she finally got to be loved.

It’s no wonder that every person involved with her care during her hospitalization was so deeply affected by her. How could anyone do this to another person? Or even worse, to an innocent child? “What a monster,” we said. “Only a monster could have done this.”

While anyone would agree that what this abuser—the girl’s mother—did was brutal and wrong, I would also argue that the underlying danger is much more systemic. We call her the “monster,” but I sense that the real monster is still lurking in the shadows, unnamed. I can’t help but try to understand this woman; it is unfair to condemn her without first learning her story. How were her actions guided by her own history of trauma, abuse, and violent discipline as a child? We preach to each other and to our learners that trauma-informed care is essential; you must not ask what’s wrong with you, but what happened to you. Founder and Director of the Equal Justice Initiative Bryan Stevenson has said that “each of us is more than the worst thing we’ve ever done.”¹ It’s inhumane of us to dehumanize her for this atrocity, especially without pausing to ask how her environment, personal trauma, and understanding of child development set the risk.

It is important to understand the cycle of trauma and abuse. Traumatic experiences have been shown to alter neurodevelopment and the body’s stress response, particularly when experiences take place early in life, when they are repeated and long term, and when they are severe. We know that adverse childhood experiences are cumulative and result in adverse outcomes as adults, including increased likelihood of violent or criminal activity. We know that prior history of trauma, specifically child abuse, sexual abuse, or domestic violence, is associated with higher potential for child abuse later on.

The effect of experiencing trauma is such that only 22% of adults who experienced abuse or neglect as children will achieve resiliency.² In a world in which social distancing and isolation have become the new normal, we must be even more aware of the effects of trauma on families. The COVID-19 pandemic has increased known risk factors for child abuse, including financial hardship, unemployment, increased anxiety, increased caregiver responsibilities, and decreased access to mental health services and community resources.³ Furthermore, virtual learning environments may have significant implications on the reporting of child abuse. Among cases of maltreatment of children that received an investigation or alternative response in 2018, 20.5% were reported by education personnel.⁴ While remote learning options may be necessary to minimize risk of viral spread, fewer interactions between children and mandatory reporters may result in child maltreatment going undetected. In the face of these challenges, I urge our healthcare system to use current constraints as fuel for creative interventions including the following:

• Applying advances in telemedicine to create a new opportunity to interface with families, provide mental health support, connect them with resources, and offer gentle guidance about safe parenting.
• Improving both screening methods for parental trauma and distress and referrals for support services.
• Advocating for adequate access to life-sustaining resources including shelter, food, and healthcare for all families. This is a necessary foundation for building resilience.
• Providing bias training for mandatory reporters to ensure that all children and their families are approached with respect and compassion.
• Prioritizing innovation that provides long-lasting, sustainable, and equitable access to support and healing.

To best protect our children, we must heal their adult caregivers; we must help them to conquer their monsters.

Our patient and her family have since visited me in my thoughts and dreams, less often now than before. While I never truly knew her, she has left an open void where there should have been the promise of a healthy, growing, and developing child. Within that void resides fear. I fear for other “hidden children” and the abuse they are at risk for experiencing. I fear that her siblings, now living without their mother, will become victims of the instability of being “in the system.” I fear that by turning to punishment as our only solution, we miss opportunities to prevent such tragedy. Despite the darkness, she also brings me hope. I hope that her siblings can rely on each other as a foundation for resilience. I hope that we as a healthcare system can continue to love our patients without question or condition. I hope that we as a society can invest in breaking the cycle of trauma and in supporting parents. I hope that we can create a system in which children can grow up free from abuse.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

The day after Memorial Day 2020 marked an important transition in the United States’ experience with coronavirus disease 2019 (COVID-19), with many states making initial plans to reopen. Alongside this reopening process, the US healthcare system needed to reopen to provide needed care to communities. This reopening, however, was in the context of several months of staggering financial losses for many medical centers that expected a larger surge than occurred locally and lost profit because of delayed elective procedures, all amid a national economic recession. Each medical center also faced a persistent risk of infection and a call for social equity as each one decided how to reopen. These decisions balanced the risks of reopening from COVID-19 exposure with patients’ medical needs and the healthcare industry’s financial needs.

This year’s widespread healthcare closures were necessary to reduce COVID-19 transmission and prepare for a future patient surge, but these closures had unintended consequences. Nearly half of adults polled said they or someone in their household had foregone or delayed care since the outbreak began.¹ This was especially true for visits to emergency departments and doctors’ offices for strokes, heart attacks, and routine medical care.² In a survey across 49 states, only 7% of primary care practices considered scheduling preventive visits as a high priority.³ Eleven percent of polled adults reported delaying care worsened their condition,¹ and in hard-hit areas such as New York City, non-COVID mortality was 22% higher than expected.⁴

Avoidance of the medical system decreased not only use of necessary, high-value care but also use of low-value care. Low-value services are those in which the “potential for harms exceed the potential benefits,”⁵ such as unnecessary hospitalizations, avoidable emergency department or clinic visits, unwarranted or excessive diagnostic testing (eg, annual physicals), and certain procedures (eg, spinal fusion surgery for low-back pain). Low-value care is costly, with $75.7 to $101.2 billion of the gross domestic product (GDP) spent on overuse.⁶ This care risks contributing to financial and, in turn, clinical harm for patients because the average health plan deductible exceeds a typical family’s available savings⁷and 25% of Americans say they have foregone treatment for a serious medical condition in the past year because of these costs.⁸ Medical centers’ significant financial losses are a sobering reminder of how much our system relies on fee-for-service billing that encourages high-margin profitable services regardless of necessity.⁹ We must avoid quick reactions of increasing these procedures to respond to the sudden financial loses.Medical centers across the country are choosing how to “reboot”—either deliberately changing how services are organized and delivered or returning to prior practices. Medical centers are facing potential for their own Renaissance in transitioning their organizations to modern healthcare delivery. In the 15th century ad, after experiencing the bubonic plague, Europe similarly transitioned toward modernity and great social change. Through the initial pandemic wave, we learned that even the largest health system could change their practices rapidly. COVID-19 achieved in 8 weeks what years of research, policy initiatives (eg, Choosing Wisely®, RightCare, Less Is More), and emphasizing value in reimbursement could not: stopping the delivery of a wide range of low-value services. We share three lessons learned from medical centers that have begun reopening services that can help us to better ensure higher-value, more affordable care that meets patients’ needs.

Medical centers can better focus on high-value care by defining their high-risk patient populations; high-value treatments, procedures, and preventive care; and phases of reopening. During the first pandemic wave, medical centers tried to reassure patients about emergency care, such as coming in for chest pain or neurologic symptoms, through personal outreach and media campaigns. Outpatient virtual visits also continued, including primary care, specialty services, mental health treatment, and physical therapy. While reopening, some medical centers have assessed disparities by relying on their data analytics and, if available, embedded health services researchers to understand what care was stopped and what populations were most affected.

The University of California health systems, for example, had a learning collaborative focused on sharing methods to restore care delivery that prioritizes patient needs. Some campuses conducted analyses using both electronic health record data and input from patients and their care teams to identify clinical needs and determine patient outreach plans. Some approaches used machine learning models to identify patients at highest risk of hospitalization or emergency department visits over the next 12 months and to conduct additional outreach to schedule these patients in primary and specialty care if clinically appropriate. Similarly, surgical specialties identified the highest-priority nonemergent surgeries for scheduling, including cancer resection, radiation therapy, and pain-management procedures. Similar guidance toward the most meaningful care has been prioritized within the United States Department of Veterans Affairs.

The rapid deployment of telehealth and payment models that reimburse video and in-clinic visits equally created new opportunities for medical centers to expand high-value care in lower-cost home settings. Similarly, new infrastructure is being developed to help define smarter use of virtual visits and home-based lab collection and monitoring.

Medical centers also must pay careful attention to redeploying service capacity for underused, high-value services. The pandemic uncovered existing staff that could be redeployed to support these changes. For example, with an “all hands on deck” mentality during the pandemic, in some medical centers, analysts or care managers from less-prioritized or duplicative areas were reassigned to vital COVID-19 efforts. Medical centers may realize that this staff can provide more value in the future by supporting increased high-value, affordable healthcare.

During the intial wave of the pandemic, medical centers greatly reduced the care they provided, often focusing on delivering essential care. This preparation for a surge of COVID patients had the effect of halting many unnecessary services by moving care from the clinic to home under new reimbursement changes, such as those affecting telehealth payments. The experience of reducing low-value medical services and visits can be extended to limiting unnecessary diagnostic testing. Medical centers could, for example, focus only on tests that advance care plans; reduce unnecessary blood draws, procedures, and vital sign checks on stable patients; shift to medications with less-frequent dosing intervals; and consolidate visits by treatment teams.^10,11

Medical centers, however, now face continued pressures to increase revenue because 75% report their organization’s top priority is focused on increasing patient volume.¹²Nearly 95% of healthcare payments have been based on fee-for-service models,¹³ and the COVID-19 pandemic highlighted the financial vulnerability of our health system when we reduce in-person care, especially among rural medical centers who often have no financial reserve.¹⁴ Similarly, nearly half of hospitals’ revenue comes from surgical admissions, though not all of these are necessary.^15-18 The fiscal realities facing medical centers make it challenging to not simply “ramp up” all service, regardless of necessity, in the context of payment models dependent on fee for service, which are present in most areas of the country.

As medical centers reboot, operational and clinical leaders must proactively view changes through an equity lens to avoid exacerbating health disparities among vulnerable populations. The pandemic has focused national attention on the severity and pervasiveness of disparities and created an imperative for substantive action to evaluate how every decision will affect health equity. For example, medical centers are expanding use of telehealth to improve patient outreach. However, in a survey of primary care physicians, 72% said they have patients who are unable to access telehealth because they do not have access to technology.³ Exclusion of these patients from programs risks worsening health disparities. In a recent survey, nearly 65% of medical centers report reexamining existing policies, protocols, and practices for patients at risk of disparities.¹² Medical centers now have an opportunity to strengthen, not eliminate, existing services such as education and community outreach programs that support vulnerable patients to improve trust among patients and improved downstream health outcomes even with recent financial losses in mind.

Medical centers nationwide will need payment reform that provides greater financial stability beyond the pandemic to support high-value care delivery. They also will need flexibility to invest in prevention and to deliver the appropriate intensity of care to meet patients’ and communities’ needs.^15-17 Options to provide this support include prospective population-based payments that may create more resilience in protecting access to care when it is most needed. Models can include fully capitated payment for physician practices.^19,20 For example, after Vermont entered a single accountable care organization (ACO) model with the Centers for Medicare & Medicaid Services (CMS) in 2018, they not only generated a $97 million Medicaid savings, but also had a financial cushion that was later used in their COVID-19 response.^21,22 The advanced payments allowed primary care practices and community agencies to invest in a digital tool to support outreach to patient at high risk for virus complications.

Hospitals similarly can adapt global budgets that incentivize financial stewardship by encouraging clinicians to resume necessary services and not unnecessary ones.¹⁶ For example, CMS partnered with Pennsylvania’s Department of Health to provide prospective all-payer global budgets for rural hospitals and Maryland’s Health Services Cost Review Commission that negotiates a budget with each hospital. During the COVID-19 pandemic, hospitals in these programs have had more financial protection from fluctuating finances by allowing for easier shifts in service delivery location and adjustments in rates to compensate for declines in visits and procedures.²³

Policy makers and payers also can hold medical centers accountable to evidence-based guidelines and appropriate use of care, especially when necessary but expensive (eg, percutaneous coronary interventions, spinal surgeries, or cancer care). Funding agencies, additionally, can support these efforts by focusing on research, dissemination, and reliable implementation of these practices.

The COVID-19 crisis presents a tremendous opportunity for each medical center to revitalize healthcare. This opportunity can be seized only with reform by policy makers, payers, and regulatory agencies who encourage restarting high-value care without low-value services. We must take deliberate action so the nation’s medical centers can better meet patients’ needs to make healthcare more resilient, efficient, and fair.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.

Professional upheavals caused by the coronavirus disease 2019 (COVID-19) pandemic have affected the academic productivity of many physicians. This is due in part to rapid changes in clinical care and medical education: physician-researchers have been redeployed to frontline clinical care; clinician-educators have been forced to rapidly transition in-person curricula to virtual platforms; and primary care physicians and subspecialists have been forced to transition to telehealth-based practices. In addition to these changes in clinical and educational responsibilities, the COVID-19 pandemic has substantially altered the personal lives of physicians. During the height of the pandemic, clinicians simultaneously wrestled with a lack of available childcare, unexpected home-schooling responsibilities, decreased income, and many other COVID-19-related stresses.¹ Additionally, the ever-present “second pandemic” of structural racism, persistent health disparities, and racial inequity has further increased the personal and professional demands facing academic faculty.²

In particular, the pandemic has placed personal and professional pressure on female and minority faculty members. In spite of these pressures, however, the academic promotions process still requires rigid accounting of scholarly productivity. As the focus of academic practices has shifted to support clinical care during the pandemic, scholarly productivity has suffered for clinicians on the frontline. As a result, academic clinical faculty have expressed significant stress and concerns about failing to meet benchmarks for promotion (eg, publications, curricula development, national presentations). To counter these shifts (and the inherent inequity that they create for female clinicians and for men and women who are Black, Indigenous, and/or of color), academic institutions should not only recognize the effects the COVID-19 pandemic has had on faculty, but also adopt immediate solutions to more equitably account for such disruptions to academic portfolios. In this paper, we explore populations whose career trajectories are most at-risk and propose a framework to capture novel and nontraditional contributions while also acknowledging the rapid changes the COVID-19 pandemic has brought to academic medicine.

Even before the COVID-19 pandemic, physician mothers, underrepresented racial/ethnic minority groups, and junior faculty were most at-risk for career disruptions. The closure of daycare facilities and schools and shift to online learning resulting from the pandemic, along with the common challenges of parenting, have taken a significant toll on the lives of working parents. Because women tend to carry a disproportionate share of childcare and household responsibilities, these changes have inequitably leveraged themselves as a “mommy tax” on working women.^3,4

As underrepresented medicine faculty (particularly Black, Hispanic, Latino, and Native American clinicians) comprise only 8% of the academic medical workforce,they currently face a variety of personal and professional challenges.⁵ This is especially true for Black and Latinx physicians who have been experiencing an increased COVID-19 burden in their communities, while concurrently fighting entrenched structural racism and police violence. In academia, these challenges have worsened because of the “minority tax”—the toll of often uncompensated extra responsibilities (time or money) placed on minority faculty in the name of achieving diversity. The unintended consequences of these responsibilities result in having fewer mentors,⁶ caring for underserved populations,⁷ and performing more clinical care⁸ than non-underrepresented minority faculty. Because minority faculty are unlikely to be in leadership positions, it is reasonable to conclude they have been shouldering heavier clinical obligations and facing greater career disruption of scholarly work due to the COVID-19 pandemic.

Junior faculty (eg, instructors and assistant professors) also remain professionally vulnerable during the COVID-19 pandemic. Because junior faculty are often more clinically focused and less likely to hold leadership positions than senior faculty, they are more likely to have assumed frontline clinical positions, which come at the expense of academic work. Junior faculty are also at a critical building phase in their academic career—a time when they benefit from the opportunity to share their scholarly work and network at conferences. Unfortunately, many conferences have been canceled or moved to a virtual platform. Given that some institutions may be freezing academic funding for conferences due to budgetary shortfalls from the pandemic, junior faculty may be particularly at risk if they are not able to present their work. In addition, junior faculty often face disproportionate struggles at home, trying to balance demands of work and caring for young children. Considering the unique needs of each of these groups, it is especially important to consider intersectionality, or the compounded issues for individuals who exist in multiple disproportionately affected groups (eg, a Black female junior faculty member who is also a mother).

The typical format of a professional curriculum vitae (CV) at most academic institutions does not allow one to document potential disruptions or novel contributions, including those that occurred during the COVID-19 pandemic. As a group of academic clinicians, educators, and researchers whose careers have been affected by the pandemic, we created a COVID-19 CV matrix, a potential framework to serve as a supplement for faculty. In this matrix, faculty members may document their contributions, disruptions that affected their work, and caregiving responsibilities during this time period, while also providing a rubric for promotions and tenure committees to equitably evaluate the pandemic period on an academic CV. Our COVID-19 CV matrix consists of six domains: (1) clinical care, (2) research, (3) education, (4) service, (5) advocacy/media, and (6) social media. These domains encompass traditional and nontraditional contributions made by healthcare professionals during the pandemic (Table). This matrix broadens the ability of both faculty and institutions to determine the actual impact of individuals during the pandemic.

ACCOUNT FOR YOUR (NEW) IMPACT

Throughout the COVID-19 pandemic, academic faculty have been innovative, contributing in novel ways not routinely captured by promotions committees—eg, the digital health researcher who now directs the telemedicine response for their institution and the health disparities researcher who now leads daily webinar sessions on structural racism to medical students. Other novel contributions include advancing COVID-19 innovations and engaging in media and community advocacy (eg, organizing large-scale donations of equipment and funds to support organizations in need). While such nontraditional contributions may not have been readily captured or thought “CV worthy” in the past, faculty should now account for them. More importantly, promotions committees need to recognize that these pivots or alterations in career paths are not signals of professional failure, but rather evidence of a shifting landscape and the respective response of the individual. Furthermore, because these pivots often help fulfill an institutional mission, they are impactful.

ACKNOWLEDGE THE DISRUPTION

It is important for promotions and tenure committees to recognize the impact and disruption COVID-19 has had on traditional academic work, acknowledging the time and energy required for a faculty member to make needed work adjustments. This enables a leader to better assess how a faculty member’s academic portfolio has been affected. For example, researchers have had to halt studies, medical educators have had to redevelop and transition curricula to virtual platforms, and physicians have had to discontinue clinician quality improvement initiatives due to competing hospital priorities. Faculty members who document such unintentional alterations in their academic career path can explain to their institution how they have continued to positively influence their field and the community during the pandemic. This approach is analogous to the current model of accounting for clinical time when judging faculty members’ contributions in scholarly achievement.

The COVID-19 CV matrix has the potential to be annotated to explain the burden of one’s personal situation, which is often “invisible” in the professional environment. For example, many physicians have had to assume additional childcare responsibilities, tend to sick family members, friends, and even themselves. It is also possible that a faculty member has a partner who is also an essential worker, one who had to self-isolate due to COVID-19 exposure or illness, or who has been working overtime due to high patient volumes.

INSTITUTIONAL RESPONSE

How can institutions respond to the altered academic landscape caused by the COVID-19 pandemic? Promotions committees typically have two main tools at their disposal: adjusting the tenure clock or the benchmarks. Extending the period of time available to qualify for tenure is commonplace in the “publish-or-perish” academic tracks of university research professors. Clock adjustments are typically granted to faculty following the birth of a child or for other specific family- or health-related hardships, in accordance with the Family and Medical Leave Act. Unfortunately, tenure-clock extensions for female faculty members can exacerbate gender inequity: Data on tenure-clock extensions show a higher rate of tenure granted to male faculty compared to female faculty.⁹ For this reason, it is also important to explore adjustments or modifications to benchmark criteria. This could be accomplished by broadening the criteria for promotion, recognizing that impact occurs in many forms, thereby enabling meeting a benchmark. It can also occur by examining the trajectory of an individual within a promotion pathway before it was disrupted to determine impact. To avoid exacerbating social and gender inequities within academia, institutions should use these professional levers and create new ones to provide parity and equality across the promotional playing field. While the CV matrix openly acknowledges the disruptions and tangents the COVID-19 pandemic has had on academic careers, it remains important for academic institutions to recognize these disruptions and innovate the manner in which they acknowledge scholarly contributions.

While academic rigidity and known social taxes (minority and mommy taxes) are particularly problematic in the current climate, these issues have always been at play in evaluating academic success. Improved documentation of novel contributions, disruptions, caregiving, and other challenges can enable more holistic and timely professional advancement for all faculty, regardless of their sex, race, ethnicity, or social background. Ultimately, we hope this framework initiates further conversations among academic institutions on how to define productivity in an age where journal impact factor or number of publications is not the fullest measure of one’s impact in their field.