Sunil Kripalani, MD, MSc

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

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

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

Sites and Subjects

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

Survey Development and Deployment

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

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

Health System Adaptations, Testing, and PPE Status

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

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

Characteristics of COVID-19 RIUs

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

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

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

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

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

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

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

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

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

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

Despite increased awareness of Choosing Wisely (CW)^® recommendations to reduce low-value care,¹ there is limited published data about strategies to implement these guidelines or evidence that they have influenced ordering patterns or reduced healthcare spending.^2-6 Implementation science seeks to accelerate the translation of evidence-based interventions into clinical practice and the deimplementation of low-value care.^7-9 Based on established principles of implementation science, we used a prospective, nonrandomized study design to assess a CW intervention to reduce chest X-ray (CXR) ordering in adult intensive care units (ICUs).¹⁰

In ICUs, CXR ordering strategies may be routine (daily) or on-demand (with clinical indication). The former strategy’s principal advantage is the potential to detect life-threatening situations that may otherwise escape diagnosis.¹¹ Disadvantages include cost, radiation exposure, patient inconvenience, false-positive workups, and low diagnostic and therapeutic value.^12,13 On-demand strategies may safely reduce CXR ordering by 32% to 45%.^11-17 Based on this evidence, the Critical Care Societies Collaborative and the American College of Radiology have recommended on-demand CXR ordering.^18,19 Here, we describe the effectiveness of an intervention to reduce CXR ordering in two ICUs while evaluating the deimplementation strategies using a validated framework.

Setting and Design

Vanderbilt University Medical Center (VUMC) is an academic referral center in Nashville, Tennessee. The cardiovascular ICU (CVICU) has 27 beds and the medical ICU (MICU) has 34 beds. Acute care nurse practitioners (ACNPs) and two critical care physicians staff the CVICU; cardiology fellows, anesthesia critical care fellows, and transplant and cardiac surgeons are also active in patient care. The MICU is staffed by two critical care physicians who supervise one team of ACNPs and two teams of medical residents who rotate through the unit every two weeks. Each MICU team is assigned a fellow in pulmonary and critical care.

We conducted a prospective, nonrandomized study in these units from October 2015 to June 2016. The VUMC Institutional Review Board approved the intervention as a quality improvement (QI) activity, waiving the requirement for informed consent.

Intervention

Following the top CW recommendation of the Critical Care Societies Collaborative—“Don’t order diagnostic tests at regular intervals (such as every day), but rather in response to specific clinical questions.”¹⁹—the VUMC resident-led CW Steering Committee designed a multifaceted approach to reduce ordering of routine CXRs in ICUs. The intervention included a didactic session on CW and proper CXR ordering practices, peer champions, data audits, and feedback to providers through weekly e-mails (see Supplemental Materials, 1 – Resident Presentation and 2 – CXR Flyer). ²⁰

In September 2015, CVICU and MICU teams received a didactic session highlighting CW, current CXR ordering rates, and the plan for reducing CXR ordering. On October 5, 2015, teams began receiving weekly e-mails with ordering rates defined as CXRs ordered per patient per day and a brief rationale for reducing unnecessary CXRs. To encourage friendly competition, we provided weekly rates to the MICU teams, allowing for transparent benchmarking against one another. A similar competition strategy was not used in the CVICU due to the lack of multiple teams.

In the CVICU, two ACNPs volunteered as peer champions. These champions coordinated data feedback and advocated for the intervention among their colleagues. In the MICU, three internal medicine residents volunteered as peer champions and fulfilled similar roles.

To facilitate deimplementation, we conducted two Plan-Do-Study-Act (PDSA) cycles, the first from November to mid-December 2015 and the second from mid-December 2015 to mid-January 2016. During these cycles, we tailored our deimplementation strategy based on barriers identified by the peer champions and ICU leaders (described in the Qualitative Results section). Peer champions and the CW Steering Committee generated potential solutions by conversing with stakeholders and using the Expert Recommendations for Implementing Change (ERIC).²⁰ Interventions included disseminating promotional flyers, holding meetings with stakeholders, and providing monthly CXR ordering rates. After the PDSA cycles, we continued reexamining the deimplementation efforts by reviewing ordering rates and soliciting feedback from ICU leaders and peer champions. However, no significant changes to the intervention were made during this time.

Quantitative Evaluation

We extracted data from VUMC’s Enterprise Data Warehouse during the intervention period (October 5, 2015 to May 24, 2016) and a historical control period (October 1, 2014 to October 4, 2015). Within each ICU, descriptive statistics were used to compare patient cohorts in the baseline and intervention periods by age, sex, and race.

The primary outcome was CXRs ordered per patient per day by hospital unit (CVICU or MICU). The baseline period included all data between October 1, 2014 and September 15, 2015. To account for priming of providers from didactic education, we allowed a washout period from September 16, 2015 to October 4, 2015. As a preliminary analysis, we compared CXR rates in the baseline and intervention periods using Wilcoxon rank-sum tests. We then conducted interrupted time-series analyses with segmented linear regression to assess differences in linear trends in CXR rates over the two periods. To account for different staffing models in the MICU, we stratified the impact of the intervention by team—medical resident (physician) or ACNP. R version 3.4.0 was used for statistical analysis.²¹

Qualitative Evaluation

Our qualitative evaluation consisted of embedded observation and semistructured interviews with stakeholders. The qualitative portion was guided by the Consolidated Framework for Implementation Research (CFIR), a widely used framework for design and evaluation of improvement initiatives that helped us to determine major facilitators and barriers to implementation.^22,23

Embedded Observation

From November 2015 to January 2016, we observed morning rounds in the CVICU and MICU one to two times weekly to understand factors facilitating and inhibiting uptake of the intervention. Observations were recorded and organized using a CFIR-based template and directed toward understanding interactions among team members (eg, the decision-making process hierarchy), team workflows and decision-making processes, process of ordering CXRs, and providers’ knowledge and perceptions of the CXR intervention (see Supplemental Material, 3 – CFIR Table).^22,23 After rounds, ICU team members were invited to share suggestions for improving the intervention. All observations occurred during and shortly following morning rounds when the vast majority of routine CXRs are ordered; we did not evaluate night or evening workflows. In the spirit of continuous improvement, we evaluated data in real-time.

Semistructured Interviews

Based on the direct observations, we developed semistructured interview questions to further evaluate provider perspectives (eg, “Do you believe ICU patients need a daily CXR?”) and constructs aligning with CFIR (eg, “intervention source—internally vs externally developed;” see Supplemental Material, 4 – Interview Questions).

Stakeholders from both ICUs were recruited through e-mail and in-person requests to participate in semistructured interviews. In the CVICU, we interviewed critical care physicians, anesthesia critical care fellows, and ACNPs. In the MICU, we interviewed medical students, interns, residents, critical care fellows, attending intensivist physicians, and ACNPs. We also interviewed X-ray technologists who routinely perform portable films in the units.

Quantitative Results

Cardiovascular Intensive Care Unit

The median baseline CXR ordering rate in the CVICU was 1.16 CXRs per patient per day, with interquartile range (IQR) 1.06-1.28. During the intervention period, the rate dropped to 1.07 (IQR 0.94-1.21; P < .001; Table 2). The time-series analysis suggested an essentially flat trend during the baseline period, followed by a small but significant drop in ordering rates during the intervention period (P < .001; Table 3 and Figure 1). Ordering rates appeared to increase slightly over the course of the intervention period, but this slight upward trend was not significantly different from the flat trend seen during the baseline period.

Medical Intensive Care Unit

For both physician and ACPN teams, the median baseline CXR ordering rates in the MICU were much lower than the baseline rate in the CVICU (Table 2). For the MICU physician care team, the baseline CXR ordering rate was 0.60 CXRs per patient per day (IQR 0.48-0.73). For the ACNP team, the median rate was 0.39 CXRs per patient per day (IQR 0.21-0.57). Both rates stayed approximately the same during the intervention period (Table 2). The time-series analysis suggested a statistically significant but very slight downward trend in CXR ordering rates during the baseline period, in the physician (P = .011) and ACNP (P = .022) teams (Table 3, Figure 2). Under this model, a small increase in CXR ordering initially occurred during the intervention period for both physician and ACNP teams (P = .010 and P = .055, respectively), after which the rates declined slightly. Trends in ordering rates during the intervention period were not significantly different from the slight downward trends seen during the baseline period.

Qualitative Results

We identified 25 of 39 CFIR constructs as relevant to the initiative (see Supplemental Materials, 3 – CFIR Table.) We determined the major facilitators of deimplementation to be peer champion discussions about CXR ordering on rounds and weekly data feedback, particularly if accompanied by in-person follow-up.

A deimplementation intervention using provider education, peer champions, and data feedback was associated with fewer CXRs in the CVICU (P < .001) but not in the MICU. The CFIR-guided qualitative analysis was valuable for evaluating our deimplementation strategy and for identifying differences between the two ICUs.

Relatively few studies have demonstrated effective interventions that address CW recommendations.^25-28 However, three population-level analyses of insurance claims show mixed results.^3,4,29 Experts have thus proposed using implementation science to improve uptake of CW recommendations.^2,3,7,8 Our study demonstrates the effectiveness of this approach. As expected, providers largely endorsed an on-demand CXR ordering strategy. Using the CFIR, however, we discovered barriers (eg, concern that data feedback did not reflect variations in patients’ needs). Using methods from implementation science allowed us to diagnose and tailor our approaches.

Our qualitative evaluation suggested that the intervention was ineffective mostly due to CFIR’s “inner setting” constructs, including resident fatigue, competing priorities, and decreased investment in QI projects because of the rotating nature of providers in training. Baseline CXR ordering rates in the MICU were also considerably lower than in the CVICU. We observed that CVICU providers ordered many CXRs following the placement of lines or tubes and that ACNPs in the MICU had received education on appropriate CXR ordering practices and had access to an alternate imaging modality in ultrasound. These factors may partially explain the difference in baseline rates.

As noted in a study of cardiac stress testing guidelines, the existence of high-value care recommendations does not mean overuse.³⁰ Indeed, the lack of significant CXR over-ordering in the MICU highlights the importance of baseline measurement and partnering with information technology departments to create the best possible data feedback systems.^30-32 Our experience shows that these systems should provide sufficient pre-implementation data (ideally >1 year), such that teams selecting QI projects can ensure that a project is a good use of institutional resources and change capital.

To inform future work, we informally assessed program costs and savings. We estimate the initiative cost $1,600, including $1,000 for curriculum development and teaching time, $300 for educational materials, and $300 for CXR tracking dashboard development. Hospital charges and reimbursements for CXR vary widely.³³ We calculated savings using a range of rates, from a conservative $23 (the Medicare reimbursement rate for single-view CXR, CPT code 71010, global fee) to $50 (an approximate blended reimbursement rate across payers).^34,35 In the CVICU, we estimate that 51 CXRs were avoided each month, saving $1,173-$2,550 per month or $9,384-$20,400 over eight months of follow-up. Annualizing these figures, we estimate net savings of $12,476-$29,000 in the first year in a 27-bed ICU. Costs to continue the program include education of new employees, booster training, and dashboard maintenance for an estimated annual cost of $1,000. It is difficult to estimate effectiveness over time, but if we conservatively assume that 30 CXRs were avoided each month, then the projected savings would be $8,280-$18,000 per year or an annual net savings of $7,280-$17,000 in the ICU. Although these amounts are modest, providing trainees with experiential learning opportunities in high-value care is valuable in its own right, meets curricular goals, may result in spill-over effects to other diagnostic and therapeutic decisions, and may influence long-term practice patterns. Institutional decisions to pursue projects such as this should take into account these potential benefits.

This evaluation is not without limitations. First, the study was conducted in a single tertiary-care hospital, potentially limiting its generalizability.³⁶ Second, the study design lacked a concurrent control group, and observed outcomes may have been influenced by broader CXR utilization trends, increased awareness of low-value care generally or from previous CW projects at VUMC, seasonal effects, or the Hawthorne effect. Third, the study outcome was all CXRs ordered, rather than CXRs that were unnecessary or not clinically indicated. We chose all CXRs because it was more pragmatic, did not require clinical case review, and could be incorporated promptly into dashboards, enabling timely performance feedback. Other performance measures have taken a similar tack (eg, tracking all-cause readmissions rather than preventable readmissions). Given this approach, we did not track clinical indications for CXRs (eg, central line placement). Fourth, although we compared resident and APRN orders, we did not collect data on other provider characteristics such as years in/out of training or board certification status. These considerations should be addressed in future research.

Finally, the increase in CVICU CXR ordering at the end of the intervention period, which occurred following two adverse events, raises concerns about sustainability. While unrelated to CXR orders, the events resulted in increased ordering of diagnostic tests and showed the difficulty of deimplementation in ICUs. Indeed, some CVICU providers argued that on-demand CXR ordering represented minimal potential cost savings and had not been studied among heart and lung transplant patients. Subsequently, Tonna et al. have shown that on-demand CXR ordering can be safely implemented among such patients.³⁷ Also similar to our study, Tonna et al. observed an initial decrease in CXR ordering, followed by a gradual increase toward baseline ordering rates. These findings highlight the need for sustained awareness and interventions and for the careful selection of high-value projects.

In conclusion, our study shows that a deimplementation intervention based on CW recommendations can reduce CXR ordering and that ongoing evaluation of contextual factors provides insights for both real-time modifications of current interventions and the design of future interventions. We found that messaging about reducing unnecessary tests works well when discussions are framed at the unit level but may be counterproductive if used to question individual ordering decisions.³⁸ Additional lessons learned include the value of participation on rounds to build trust among stakeholders, the utility of monthly rather than weekly statistics for feedback, stakeholder input and peer champions, and differences in approach with physician and ACNP audiences.

Acknowledgments

The authors thank the VUMC Choosing Wisely committee; Mr. Bill Harrell in Advanced Data Analytics for developing the Tableau platform used in our data feedback strategy; Emily Feld, MD, Jerry Zifodya, MD, and Ryan Kindle, MD for assisting with data feedback to providers in the MICU; Todd Rice, MD, Director of the MICU, for his support of the initiative; and Beth Prusaczyk, PhD, MSW and David Stevenson, PhD for providing feedback on earlier drafts of this manuscript.

Disclosures

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, outside the submitted work. All other authors have nothing to disclose.

Funding

This work was supported by an Innovation Grant from the Alliance for Academic Internal Medicine (AAIM, 2016) and by the Departments of Internal Medicine and Graduate Medical Education at Vanderbilt University Medical Center. The AAIM did not have a role in the study design, data collection, data analysis, data interpretation, or manuscript writing.

Despite increased awareness of Choosing Wisely (CW)^® recommendations to reduce low-value care,¹ there is limited published data about strategies to implement these guidelines or evidence that they have influenced ordering patterns or reduced healthcare spending.^2-6 Implementation science seeks to accelerate the translation of evidence-based interventions into clinical practice and the deimplementation of low-value care.^7-9 Based on established principles of implementation science, we used a prospective, nonrandomized study design to assess a CW intervention to reduce chest X-ray (CXR) ordering in adult intensive care units (ICUs).¹⁰

In ICUs, CXR ordering strategies may be routine (daily) or on-demand (with clinical indication). The former strategy’s principal advantage is the potential to detect life-threatening situations that may otherwise escape diagnosis.¹¹ Disadvantages include cost, radiation exposure, patient inconvenience, false-positive workups, and low diagnostic and therapeutic value.^12,13 On-demand strategies may safely reduce CXR ordering by 32% to 45%.^11-17 Based on this evidence, the Critical Care Societies Collaborative and the American College of Radiology have recommended on-demand CXR ordering.^18,19 Here, we describe the effectiveness of an intervention to reduce CXR ordering in two ICUs while evaluating the deimplementation strategies using a validated framework.

Setting and Design

Vanderbilt University Medical Center (VUMC) is an academic referral center in Nashville, Tennessee. The cardiovascular ICU (CVICU) has 27 beds and the medical ICU (MICU) has 34 beds. Acute care nurse practitioners (ACNPs) and two critical care physicians staff the CVICU; cardiology fellows, anesthesia critical care fellows, and transplant and cardiac surgeons are also active in patient care. The MICU is staffed by two critical care physicians who supervise one team of ACNPs and two teams of medical residents who rotate through the unit every two weeks. Each MICU team is assigned a fellow in pulmonary and critical care.

We conducted a prospective, nonrandomized study in these units from October 2015 to June 2016. The VUMC Institutional Review Board approved the intervention as a quality improvement (QI) activity, waiving the requirement for informed consent.

Intervention

Following the top CW recommendation of the Critical Care Societies Collaborative—“Don’t order diagnostic tests at regular intervals (such as every day), but rather in response to specific clinical questions.”¹⁹—the VUMC resident-led CW Steering Committee designed a multifaceted approach to reduce ordering of routine CXRs in ICUs. The intervention included a didactic session on CW and proper CXR ordering practices, peer champions, data audits, and feedback to providers through weekly e-mails (see Supplemental Materials, 1 – Resident Presentation and 2 – CXR Flyer). ²⁰

In September 2015, CVICU and MICU teams received a didactic session highlighting CW, current CXR ordering rates, and the plan for reducing CXR ordering. On October 5, 2015, teams began receiving weekly e-mails with ordering rates defined as CXRs ordered per patient per day and a brief rationale for reducing unnecessary CXRs. To encourage friendly competition, we provided weekly rates to the MICU teams, allowing for transparent benchmarking against one another. A similar competition strategy was not used in the CVICU due to the lack of multiple teams.

In the CVICU, two ACNPs volunteered as peer champions. These champions coordinated data feedback and advocated for the intervention among their colleagues. In the MICU, three internal medicine residents volunteered as peer champions and fulfilled similar roles.

To facilitate deimplementation, we conducted two Plan-Do-Study-Act (PDSA) cycles, the first from November to mid-December 2015 and the second from mid-December 2015 to mid-January 2016. During these cycles, we tailored our deimplementation strategy based on barriers identified by the peer champions and ICU leaders (described in the Qualitative Results section). Peer champions and the CW Steering Committee generated potential solutions by conversing with stakeholders and using the Expert Recommendations for Implementing Change (ERIC).²⁰ Interventions included disseminating promotional flyers, holding meetings with stakeholders, and providing monthly CXR ordering rates. After the PDSA cycles, we continued reexamining the deimplementation efforts by reviewing ordering rates and soliciting feedback from ICU leaders and peer champions. However, no significant changes to the intervention were made during this time.

Quantitative Evaluation

We extracted data from VUMC’s Enterprise Data Warehouse during the intervention period (October 5, 2015 to May 24, 2016) and a historical control period (October 1, 2014 to October 4, 2015). Within each ICU, descriptive statistics were used to compare patient cohorts in the baseline and intervention periods by age, sex, and race.

The primary outcome was CXRs ordered per patient per day by hospital unit (CVICU or MICU). The baseline period included all data between October 1, 2014 and September 15, 2015. To account for priming of providers from didactic education, we allowed a washout period from September 16, 2015 to October 4, 2015. As a preliminary analysis, we compared CXR rates in the baseline and intervention periods using Wilcoxon rank-sum tests. We then conducted interrupted time-series analyses with segmented linear regression to assess differences in linear trends in CXR rates over the two periods. To account for different staffing models in the MICU, we stratified the impact of the intervention by team—medical resident (physician) or ACNP. R version 3.4.0 was used for statistical analysis.²¹

Qualitative Evaluation

Our qualitative evaluation consisted of embedded observation and semistructured interviews with stakeholders. The qualitative portion was guided by the Consolidated Framework for Implementation Research (CFIR), a widely used framework for design and evaluation of improvement initiatives that helped us to determine major facilitators and barriers to implementation.^22,23

Embedded Observation

From November 2015 to January 2016, we observed morning rounds in the CVICU and MICU one to two times weekly to understand factors facilitating and inhibiting uptake of the intervention. Observations were recorded and organized using a CFIR-based template and directed toward understanding interactions among team members (eg, the decision-making process hierarchy), team workflows and decision-making processes, process of ordering CXRs, and providers’ knowledge and perceptions of the CXR intervention (see Supplemental Material, 3 – CFIR Table).^22,23 After rounds, ICU team members were invited to share suggestions for improving the intervention. All observations occurred during and shortly following morning rounds when the vast majority of routine CXRs are ordered; we did not evaluate night or evening workflows. In the spirit of continuous improvement, we evaluated data in real-time.

Semistructured Interviews

Based on the direct observations, we developed semistructured interview questions to further evaluate provider perspectives (eg, “Do you believe ICU patients need a daily CXR?”) and constructs aligning with CFIR (eg, “intervention source—internally vs externally developed;” see Supplemental Material, 4 – Interview Questions).

Stakeholders from both ICUs were recruited through e-mail and in-person requests to participate in semistructured interviews. In the CVICU, we interviewed critical care physicians, anesthesia critical care fellows, and ACNPs. In the MICU, we interviewed medical students, interns, residents, critical care fellows, attending intensivist physicians, and ACNPs. We also interviewed X-ray technologists who routinely perform portable films in the units.

Quantitative Results

Cardiovascular Intensive Care Unit

The median baseline CXR ordering rate in the CVICU was 1.16 CXRs per patient per day, with interquartile range (IQR) 1.06-1.28. During the intervention period, the rate dropped to 1.07 (IQR 0.94-1.21; P < .001; Table 2). The time-series analysis suggested an essentially flat trend during the baseline period, followed by a small but significant drop in ordering rates during the intervention period (P < .001; Table 3 and Figure 1). Ordering rates appeared to increase slightly over the course of the intervention period, but this slight upward trend was not significantly different from the flat trend seen during the baseline period.

Medical Intensive Care Unit

For both physician and ACPN teams, the median baseline CXR ordering rates in the MICU were much lower than the baseline rate in the CVICU (Table 2). For the MICU physician care team, the baseline CXR ordering rate was 0.60 CXRs per patient per day (IQR 0.48-0.73). For the ACNP team, the median rate was 0.39 CXRs per patient per day (IQR 0.21-0.57). Both rates stayed approximately the same during the intervention period (Table 2). The time-series analysis suggested a statistically significant but very slight downward trend in CXR ordering rates during the baseline period, in the physician (P = .011) and ACNP (P = .022) teams (Table 3, Figure 2). Under this model, a small increase in CXR ordering initially occurred during the intervention period for both physician and ACNP teams (P = .010 and P = .055, respectively), after which the rates declined slightly. Trends in ordering rates during the intervention period were not significantly different from the slight downward trends seen during the baseline period.

Qualitative Results

We identified 25 of 39 CFIR constructs as relevant to the initiative (see Supplemental Materials, 3 – CFIR Table.) We determined the major facilitators of deimplementation to be peer champion discussions about CXR ordering on rounds and weekly data feedback, particularly if accompanied by in-person follow-up.

A deimplementation intervention using provider education, peer champions, and data feedback was associated with fewer CXRs in the CVICU (P < .001) but not in the MICU. The CFIR-guided qualitative analysis was valuable for evaluating our deimplementation strategy and for identifying differences between the two ICUs.

Relatively few studies have demonstrated effective interventions that address CW recommendations.^25-28 However, three population-level analyses of insurance claims show mixed results.^3,4,29 Experts have thus proposed using implementation science to improve uptake of CW recommendations.^2,3,7,8 Our study demonstrates the effectiveness of this approach. As expected, providers largely endorsed an on-demand CXR ordering strategy. Using the CFIR, however, we discovered barriers (eg, concern that data feedback did not reflect variations in patients’ needs). Using methods from implementation science allowed us to diagnose and tailor our approaches.

Our qualitative evaluation suggested that the intervention was ineffective mostly due to CFIR’s “inner setting” constructs, including resident fatigue, competing priorities, and decreased investment in QI projects because of the rotating nature of providers in training. Baseline CXR ordering rates in the MICU were also considerably lower than in the CVICU. We observed that CVICU providers ordered many CXRs following the placement of lines or tubes and that ACNPs in the MICU had received education on appropriate CXR ordering practices and had access to an alternate imaging modality in ultrasound. These factors may partially explain the difference in baseline rates.

As noted in a study of cardiac stress testing guidelines, the existence of high-value care recommendations does not mean overuse.³⁰ Indeed, the lack of significant CXR over-ordering in the MICU highlights the importance of baseline measurement and partnering with information technology departments to create the best possible data feedback systems.^30-32 Our experience shows that these systems should provide sufficient pre-implementation data (ideally >1 year), such that teams selecting QI projects can ensure that a project is a good use of institutional resources and change capital.

To inform future work, we informally assessed program costs and savings. We estimate the initiative cost $1,600, including $1,000 for curriculum development and teaching time, $300 for educational materials, and $300 for CXR tracking dashboard development. Hospital charges and reimbursements for CXR vary widely.³³ We calculated savings using a range of rates, from a conservative $23 (the Medicare reimbursement rate for single-view CXR, CPT code 71010, global fee) to $50 (an approximate blended reimbursement rate across payers).^34,35 In the CVICU, we estimate that 51 CXRs were avoided each month, saving $1,173-$2,550 per month or $9,384-$20,400 over eight months of follow-up. Annualizing these figures, we estimate net savings of $12,476-$29,000 in the first year in a 27-bed ICU. Costs to continue the program include education of new employees, booster training, and dashboard maintenance for an estimated annual cost of $1,000. It is difficult to estimate effectiveness over time, but if we conservatively assume that 30 CXRs were avoided each month, then the projected savings would be $8,280-$18,000 per year or an annual net savings of $7,280-$17,000 in the ICU. Although these amounts are modest, providing trainees with experiential learning opportunities in high-value care is valuable in its own right, meets curricular goals, may result in spill-over effects to other diagnostic and therapeutic decisions, and may influence long-term practice patterns. Institutional decisions to pursue projects such as this should take into account these potential benefits.

This evaluation is not without limitations. First, the study was conducted in a single tertiary-care hospital, potentially limiting its generalizability.³⁶ Second, the study design lacked a concurrent control group, and observed outcomes may have been influenced by broader CXR utilization trends, increased awareness of low-value care generally or from previous CW projects at VUMC, seasonal effects, or the Hawthorne effect. Third, the study outcome was all CXRs ordered, rather than CXRs that were unnecessary or not clinically indicated. We chose all CXRs because it was more pragmatic, did not require clinical case review, and could be incorporated promptly into dashboards, enabling timely performance feedback. Other performance measures have taken a similar tack (eg, tracking all-cause readmissions rather than preventable readmissions). Given this approach, we did not track clinical indications for CXRs (eg, central line placement). Fourth, although we compared resident and APRN orders, we did not collect data on other provider characteristics such as years in/out of training or board certification status. These considerations should be addressed in future research.

Finally, the increase in CVICU CXR ordering at the end of the intervention period, which occurred following two adverse events, raises concerns about sustainability. While unrelated to CXR orders, the events resulted in increased ordering of diagnostic tests and showed the difficulty of deimplementation in ICUs. Indeed, some CVICU providers argued that on-demand CXR ordering represented minimal potential cost savings and had not been studied among heart and lung transplant patients. Subsequently, Tonna et al. have shown that on-demand CXR ordering can be safely implemented among such patients.³⁷ Also similar to our study, Tonna et al. observed an initial decrease in CXR ordering, followed by a gradual increase toward baseline ordering rates. These findings highlight the need for sustained awareness and interventions and for the careful selection of high-value projects.

In conclusion, our study shows that a deimplementation intervention based on CW recommendations can reduce CXR ordering and that ongoing evaluation of contextual factors provides insights for both real-time modifications of current interventions and the design of future interventions. We found that messaging about reducing unnecessary tests works well when discussions are framed at the unit level but may be counterproductive if used to question individual ordering decisions.³⁸ Additional lessons learned include the value of participation on rounds to build trust among stakeholders, the utility of monthly rather than weekly statistics for feedback, stakeholder input and peer champions, and differences in approach with physician and ACNP audiences.

Acknowledgments

The authors thank the VUMC Choosing Wisely committee; Mr. Bill Harrell in Advanced Data Analytics for developing the Tableau platform used in our data feedback strategy; Emily Feld, MD, Jerry Zifodya, MD, and Ryan Kindle, MD for assisting with data feedback to providers in the MICU; Todd Rice, MD, Director of the MICU, for his support of the initiative; and Beth Prusaczyk, PhD, MSW and David Stevenson, PhD for providing feedback on earlier drafts of this manuscript.

Disclosures

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, outside the submitted work. All other authors have nothing to disclose.

Funding

This work was supported by an Innovation Grant from the Alliance for Academic Internal Medicine (AAIM, 2016) and by the Departments of Internal Medicine and Graduate Medical Education at Vanderbilt University Medical Center. The AAIM did not have a role in the study design, data collection, data analysis, data interpretation, or manuscript writing.

Despite increased awareness of Choosing Wisely (CW)^® recommendations to reduce low-value care,¹ there is limited published data about strategies to implement these guidelines or evidence that they have influenced ordering patterns or reduced healthcare spending.^2-6 Implementation science seeks to accelerate the translation of evidence-based interventions into clinical practice and the deimplementation of low-value care.^7-9 Based on established principles of implementation science, we used a prospective, nonrandomized study design to assess a CW intervention to reduce chest X-ray (CXR) ordering in adult intensive care units (ICUs).¹⁰

In ICUs, CXR ordering strategies may be routine (daily) or on-demand (with clinical indication). The former strategy’s principal advantage is the potential to detect life-threatening situations that may otherwise escape diagnosis.¹¹ Disadvantages include cost, radiation exposure, patient inconvenience, false-positive workups, and low diagnostic and therapeutic value.^12,13 On-demand strategies may safely reduce CXR ordering by 32% to 45%.^11-17 Based on this evidence, the Critical Care Societies Collaborative and the American College of Radiology have recommended on-demand CXR ordering.^18,19 Here, we describe the effectiveness of an intervention to reduce CXR ordering in two ICUs while evaluating the deimplementation strategies using a validated framework.

Setting and Design

Vanderbilt University Medical Center (VUMC) is an academic referral center in Nashville, Tennessee. The cardiovascular ICU (CVICU) has 27 beds and the medical ICU (MICU) has 34 beds. Acute care nurse practitioners (ACNPs) and two critical care physicians staff the CVICU; cardiology fellows, anesthesia critical care fellows, and transplant and cardiac surgeons are also active in patient care. The MICU is staffed by two critical care physicians who supervise one team of ACNPs and two teams of medical residents who rotate through the unit every two weeks. Each MICU team is assigned a fellow in pulmonary and critical care.

We conducted a prospective, nonrandomized study in these units from October 2015 to June 2016. The VUMC Institutional Review Board approved the intervention as a quality improvement (QI) activity, waiving the requirement for informed consent.

Intervention

Following the top CW recommendation of the Critical Care Societies Collaborative—“Don’t order diagnostic tests at regular intervals (such as every day), but rather in response to specific clinical questions.”¹⁹—the VUMC resident-led CW Steering Committee designed a multifaceted approach to reduce ordering of routine CXRs in ICUs. The intervention included a didactic session on CW and proper CXR ordering practices, peer champions, data audits, and feedback to providers through weekly e-mails (see Supplemental Materials, 1 – Resident Presentation and 2 – CXR Flyer). ²⁰

In September 2015, CVICU and MICU teams received a didactic session highlighting CW, current CXR ordering rates, and the plan for reducing CXR ordering. On October 5, 2015, teams began receiving weekly e-mails with ordering rates defined as CXRs ordered per patient per day and a brief rationale for reducing unnecessary CXRs. To encourage friendly competition, we provided weekly rates to the MICU teams, allowing for transparent benchmarking against one another. A similar competition strategy was not used in the CVICU due to the lack of multiple teams.

In the CVICU, two ACNPs volunteered as peer champions. These champions coordinated data feedback and advocated for the intervention among their colleagues. In the MICU, three internal medicine residents volunteered as peer champions and fulfilled similar roles.

To facilitate deimplementation, we conducted two Plan-Do-Study-Act (PDSA) cycles, the first from November to mid-December 2015 and the second from mid-December 2015 to mid-January 2016. During these cycles, we tailored our deimplementation strategy based on barriers identified by the peer champions and ICU leaders (described in the Qualitative Results section). Peer champions and the CW Steering Committee generated potential solutions by conversing with stakeholders and using the Expert Recommendations for Implementing Change (ERIC).²⁰ Interventions included disseminating promotional flyers, holding meetings with stakeholders, and providing monthly CXR ordering rates. After the PDSA cycles, we continued reexamining the deimplementation efforts by reviewing ordering rates and soliciting feedback from ICU leaders and peer champions. However, no significant changes to the intervention were made during this time.

Quantitative Evaluation

We extracted data from VUMC’s Enterprise Data Warehouse during the intervention period (October 5, 2015 to May 24, 2016) and a historical control period (October 1, 2014 to October 4, 2015). Within each ICU, descriptive statistics were used to compare patient cohorts in the baseline and intervention periods by age, sex, and race.

The primary outcome was CXRs ordered per patient per day by hospital unit (CVICU or MICU). The baseline period included all data between October 1, 2014 and September 15, 2015. To account for priming of providers from didactic education, we allowed a washout period from September 16, 2015 to October 4, 2015. As a preliminary analysis, we compared CXR rates in the baseline and intervention periods using Wilcoxon rank-sum tests. We then conducted interrupted time-series analyses with segmented linear regression to assess differences in linear trends in CXR rates over the two periods. To account for different staffing models in the MICU, we stratified the impact of the intervention by team—medical resident (physician) or ACNP. R version 3.4.0 was used for statistical analysis.²¹

Qualitative Evaluation

Our qualitative evaluation consisted of embedded observation and semistructured interviews with stakeholders. The qualitative portion was guided by the Consolidated Framework for Implementation Research (CFIR), a widely used framework for design and evaluation of improvement initiatives that helped us to determine major facilitators and barriers to implementation.^22,23

Embedded Observation

From November 2015 to January 2016, we observed morning rounds in the CVICU and MICU one to two times weekly to understand factors facilitating and inhibiting uptake of the intervention. Observations were recorded and organized using a CFIR-based template and directed toward understanding interactions among team members (eg, the decision-making process hierarchy), team workflows and decision-making processes, process of ordering CXRs, and providers’ knowledge and perceptions of the CXR intervention (see Supplemental Material, 3 – CFIR Table).^22,23 After rounds, ICU team members were invited to share suggestions for improving the intervention. All observations occurred during and shortly following morning rounds when the vast majority of routine CXRs are ordered; we did not evaluate night or evening workflows. In the spirit of continuous improvement, we evaluated data in real-time.

Semistructured Interviews

Based on the direct observations, we developed semistructured interview questions to further evaluate provider perspectives (eg, “Do you believe ICU patients need a daily CXR?”) and constructs aligning with CFIR (eg, “intervention source—internally vs externally developed;” see Supplemental Material, 4 – Interview Questions).

Stakeholders from both ICUs were recruited through e-mail and in-person requests to participate in semistructured interviews. In the CVICU, we interviewed critical care physicians, anesthesia critical care fellows, and ACNPs. In the MICU, we interviewed medical students, interns, residents, critical care fellows, attending intensivist physicians, and ACNPs. We also interviewed X-ray technologists who routinely perform portable films in the units.

Quantitative Results

Cardiovascular Intensive Care Unit

The median baseline CXR ordering rate in the CVICU was 1.16 CXRs per patient per day, with interquartile range (IQR) 1.06-1.28. During the intervention period, the rate dropped to 1.07 (IQR 0.94-1.21; P < .001; Table 2). The time-series analysis suggested an essentially flat trend during the baseline period, followed by a small but significant drop in ordering rates during the intervention period (P < .001; Table 3 and Figure 1). Ordering rates appeared to increase slightly over the course of the intervention period, but this slight upward trend was not significantly different from the flat trend seen during the baseline period.

Medical Intensive Care Unit

For both physician and ACPN teams, the median baseline CXR ordering rates in the MICU were much lower than the baseline rate in the CVICU (Table 2). For the MICU physician care team, the baseline CXR ordering rate was 0.60 CXRs per patient per day (IQR 0.48-0.73). For the ACNP team, the median rate was 0.39 CXRs per patient per day (IQR 0.21-0.57). Both rates stayed approximately the same during the intervention period (Table 2). The time-series analysis suggested a statistically significant but very slight downward trend in CXR ordering rates during the baseline period, in the physician (P = .011) and ACNP (P = .022) teams (Table 3, Figure 2). Under this model, a small increase in CXR ordering initially occurred during the intervention period for both physician and ACNP teams (P = .010 and P = .055, respectively), after which the rates declined slightly. Trends in ordering rates during the intervention period were not significantly different from the slight downward trends seen during the baseline period.

Qualitative Results

We identified 25 of 39 CFIR constructs as relevant to the initiative (see Supplemental Materials, 3 – CFIR Table.) We determined the major facilitators of deimplementation to be peer champion discussions about CXR ordering on rounds and weekly data feedback, particularly if accompanied by in-person follow-up.

A deimplementation intervention using provider education, peer champions, and data feedback was associated with fewer CXRs in the CVICU (P < .001) but not in the MICU. The CFIR-guided qualitative analysis was valuable for evaluating our deimplementation strategy and for identifying differences between the two ICUs.

Relatively few studies have demonstrated effective interventions that address CW recommendations.^25-28 However, three population-level analyses of insurance claims show mixed results.^3,4,29 Experts have thus proposed using implementation science to improve uptake of CW recommendations.^2,3,7,8 Our study demonstrates the effectiveness of this approach. As expected, providers largely endorsed an on-demand CXR ordering strategy. Using the CFIR, however, we discovered barriers (eg, concern that data feedback did not reflect variations in patients’ needs). Using methods from implementation science allowed us to diagnose and tailor our approaches.

Our qualitative evaluation suggested that the intervention was ineffective mostly due to CFIR’s “inner setting” constructs, including resident fatigue, competing priorities, and decreased investment in QI projects because of the rotating nature of providers in training. Baseline CXR ordering rates in the MICU were also considerably lower than in the CVICU. We observed that CVICU providers ordered many CXRs following the placement of lines or tubes and that ACNPs in the MICU had received education on appropriate CXR ordering practices and had access to an alternate imaging modality in ultrasound. These factors may partially explain the difference in baseline rates.

As noted in a study of cardiac stress testing guidelines, the existence of high-value care recommendations does not mean overuse.³⁰ Indeed, the lack of significant CXR over-ordering in the MICU highlights the importance of baseline measurement and partnering with information technology departments to create the best possible data feedback systems.^30-32 Our experience shows that these systems should provide sufficient pre-implementation data (ideally >1 year), such that teams selecting QI projects can ensure that a project is a good use of institutional resources and change capital.

To inform future work, we informally assessed program costs and savings. We estimate the initiative cost $1,600, including $1,000 for curriculum development and teaching time, $300 for educational materials, and $300 for CXR tracking dashboard development. Hospital charges and reimbursements for CXR vary widely.³³ We calculated savings using a range of rates, from a conservative $23 (the Medicare reimbursement rate for single-view CXR, CPT code 71010, global fee) to $50 (an approximate blended reimbursement rate across payers).^34,35 In the CVICU, we estimate that 51 CXRs were avoided each month, saving $1,173-$2,550 per month or $9,384-$20,400 over eight months of follow-up. Annualizing these figures, we estimate net savings of $12,476-$29,000 in the first year in a 27-bed ICU. Costs to continue the program include education of new employees, booster training, and dashboard maintenance for an estimated annual cost of $1,000. It is difficult to estimate effectiveness over time, but if we conservatively assume that 30 CXRs were avoided each month, then the projected savings would be $8,280-$18,000 per year or an annual net savings of $7,280-$17,000 in the ICU. Although these amounts are modest, providing trainees with experiential learning opportunities in high-value care is valuable in its own right, meets curricular goals, may result in spill-over effects to other diagnostic and therapeutic decisions, and may influence long-term practice patterns. Institutional decisions to pursue projects such as this should take into account these potential benefits.

This evaluation is not without limitations. First, the study was conducted in a single tertiary-care hospital, potentially limiting its generalizability.³⁶ Second, the study design lacked a concurrent control group, and observed outcomes may have been influenced by broader CXR utilization trends, increased awareness of low-value care generally or from previous CW projects at VUMC, seasonal effects, or the Hawthorne effect. Third, the study outcome was all CXRs ordered, rather than CXRs that were unnecessary or not clinically indicated. We chose all CXRs because it was more pragmatic, did not require clinical case review, and could be incorporated promptly into dashboards, enabling timely performance feedback. Other performance measures have taken a similar tack (eg, tracking all-cause readmissions rather than preventable readmissions). Given this approach, we did not track clinical indications for CXRs (eg, central line placement). Fourth, although we compared resident and APRN orders, we did not collect data on other provider characteristics such as years in/out of training or board certification status. These considerations should be addressed in future research.

Finally, the increase in CVICU CXR ordering at the end of the intervention period, which occurred following two adverse events, raises concerns about sustainability. While unrelated to CXR orders, the events resulted in increased ordering of diagnostic tests and showed the difficulty of deimplementation in ICUs. Indeed, some CVICU providers argued that on-demand CXR ordering represented minimal potential cost savings and had not been studied among heart and lung transplant patients. Subsequently, Tonna et al. have shown that on-demand CXR ordering can be safely implemented among such patients.³⁷ Also similar to our study, Tonna et al. observed an initial decrease in CXR ordering, followed by a gradual increase toward baseline ordering rates. These findings highlight the need for sustained awareness and interventions and for the careful selection of high-value projects.

In conclusion, our study shows that a deimplementation intervention based on CW recommendations can reduce CXR ordering and that ongoing evaluation of contextual factors provides insights for both real-time modifications of current interventions and the design of future interventions. We found that messaging about reducing unnecessary tests works well when discussions are framed at the unit level but may be counterproductive if used to question individual ordering decisions.³⁸ Additional lessons learned include the value of participation on rounds to build trust among stakeholders, the utility of monthly rather than weekly statistics for feedback, stakeholder input and peer champions, and differences in approach with physician and ACNP audiences.

Acknowledgments

The authors thank the VUMC Choosing Wisely committee; Mr. Bill Harrell in Advanced Data Analytics for developing the Tableau platform used in our data feedback strategy; Emily Feld, MD, Jerry Zifodya, MD, and Ryan Kindle, MD for assisting with data feedback to providers in the MICU; Todd Rice, MD, Director of the MICU, for his support of the initiative; and Beth Prusaczyk, PhD, MSW and David Stevenson, PhD for providing feedback on earlier drafts of this manuscript.

Disclosures

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, outside the submitted work. All other authors have nothing to disclose.

Funding

This work was supported by an Innovation Grant from the Alliance for Academic Internal Medicine (AAIM, 2016) and by the Departments of Internal Medicine and Graduate Medical Education at Vanderbilt University Medical Center. The AAIM did not have a role in the study design, data collection, data analysis, data interpretation, or manuscript writing.