Daniel J. Brotman, MD

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

The Centers for Medicare & Medicaid Services (CMS) have sought to reduce readmissions in the 30 days following hospital discharge through penalties applied to hospitals with readmission rates that are higher than expected. Expected readmission rates for Medicare fee‐for‐service beneficiaries are calculated from models that use patient‐level administrative data to account for patient morbidities. Readmitted patients are defined as those who are discharged from the hospital alive and then rehospitalized at any acute care facility within 30 days of discharge. These models explicitly exclude sociodemographic variables that may impact quality of and access to outpatient care. Specific exclusions are also applied based on diagnosis codes so as to avoid penalizing hospitals for rehospitalizations that are likely to have been planned.

More recently, a hospital‐wide readmission measure has been developed, which seeks to provide a comprehensive view of each hospital's readmission rate by including the vast majority of Medicare patients. Like the condition‐specific readmission measures, the hospital‐wide readmission measure also excludes sociodemographic variables and incorporates specific condition‐based exclusions so as to avoid counting planned rehospitalizations (e.g., an admission for cholecystectomy following an admission for biliary sepsis). Although not currently used for pay‐for‐performance, this measure has been included in the CMS Star Report along with other readmission measures.[1] CMS does not currently disseminate a hospital‐wide mortality measure, but does disseminate hospital‐level adjusted 30‐day mortality rates for Medicare beneficiaries with discharge diagnoses of stroke, heart failure, myocardial infarction (MI), chronic obstructive pulmonary disease (COPD) and pneumonia, and principal procedure of coronary artery bypass grafting (CABG).

It is conceivable that aggressive efforts to reduce readmissions might delay life‐saving acute care in some scenarios,[2] and there is prior evidence that heart failure readmissions are inversely (but weakly) related to heart failure mortality.[3] It is also plausible that keeping tenuous patients alive until discharge might result in higher readmission rates. We sought to examine the relationship between hospital‐wide adjusted 30‐day readmissions and death rates across the acute care hospitals in the United States. Lacking a measure of hospital‐wide death rates, we examined the relation between hospital‐wide readmissions and each of the 6 condition‐specific mortality measures. For comparison, we also examined the relationships between condition‐specific readmission rates and mortality rates.

METHODS

We used publically available data published by CMS from July 1, 2011 through June 30, 2014.[4] These data are provided at the hospital level, without any patient‐level data. We included 4452 acute care facilities based on having hospital‐wide readmission rates, but not all facilities contributed data for each mortality measure. We excluded from analysis on a measure‐by‐measure basis those facilities for which outcomes were absent, without imputing missing outcome measures, because low volume of a given condition was the main reason for not reporting a measure. For each mortality measure, we constructed a logistic regression model to quantify the odds of performing in the lowest (best) mortality tertile as a function of hospital‐wide readmission tertile. To account for patient volumes, we included in each model the number of eligible patients at each hospital with the specified condition. We repeated these analyses using condition‐specific readmission rates (rather than the hospital‐wide readmission rates) as the independent variable. Specifications for CMS models for mortality and readmissions are publically available.[5]

RESULTS

After adjustment for patient volumes, hospitals in the highest hospital‐wide readmission tertile were more likely to perform in the lowest (best) mortality tertile for 3 of the 6 mortality measures: heart failure, COPD, and stroke (P < 0.001 for all). For MI, CABG and pneumonia, there was no significant association between high hospital‐wide readmission rates and low mortality (Table 1). Using condition‐specific readmission rates, there remained an inverse association between readmissions and mortality for heart failure and stroke, but not for COPD. In contrast, hospitals with the highest CABG‐specific readmission rates were significantly less likely to have low CABG‐specific mortality (P < 0.001).

DISCUSSION

We found that higher hospital‐wide readmission rates were associated with lower mortality at the hospital level for 3 of the 6 mortality measures we examined. The findings for heart failure parallel the findings of Krumholz and colleagues who examined 3 of these 6 measures (MI, pneumonia, and heart failure) in relation to readmissions for these specific populations.[3] This prior analysis, however, did not include the 3 more recently reported mortality measures (COPD, stroke, and CABG) and did not use hospital‐wide readmissions.

Causal mechanisms underlying the associations between mortality and readmission at the hospital level deserve further exploration. It is certainly possible that global efforts to keep patients out of the hospital might, in some instances, place patients at risk by delaying necessary acute care.[2] It is also possible that unmeasured variables, particularly access to hospice and palliative care services that might facilitate good deaths, could be associated with both reduced readmissions and higher death rates. Additionally, because deceased patients cannot be readmitted, one might expect that readmissions and mortality might be inversely associated, particularly for conditions with a high postdischarge mortality rate. Similarly, a hospital that does a particularly good job keeping chronically ill patients alive until discharge might exhibit a higher readmission rate than a hospital that is less adept at keeping tenuous patients alive until discharge.

Regardless of the mechanisms of these findings, we present these data to raise the concern that using readmission rates, particularly hospital‐wide readmission rates, as a measure of hospital quality is inherently problematic. It is particularly problematic that CMS has applied equal weight to readmissions and mortality in the Star Report.[1] High readmission rates may result from complications and poor handoffs, but may also stem from the legitimate need to care for chronically ill patients in a high‐intensity setting, particularly fragile patients who have been kept alive against the odds. In conclusion, caution is warranted in viewing readmissions as a quality metric until the associations we describe are better explained using patient‐level data and more robust adjustment than is possible with these publically available data.

Disclosures: Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. There was no financial support for this work. Contributions of the authors are as follows: drafting manuscript (Brotman), revision of manuscript for important intellectual content (brotman, Hoyer, Lepley, Deutschendorf, Leung), acquisition of data (Deutschendorf, Leung, Lepley), interpretation of data (Brotman, Hoyer, Lepley, Deutschendorf, Leung), data analysis (Brotman, Hoyer).

The structure and function of academic hospital medicine programs (AHPs) has evolved significantly with the growth of hospital medicine.[1, 2, 3, 4] Many AHPs formed in response to regulatory and financial changes, which drove demand for increased trainee oversight, improved clinical efficiency, and growth in nonteaching services staffed by hospitalists. Differences in local organizational contexts and needs have contributed to great variability in AHP program design and operations. As AHPs have become more established, the need to engage academic hospitalists in scholarship and activities that support professional development and promotion has been recognized. Defining sustainable and successful positions for academic hospitalists is a priority called for by leaders in the field.[5, 6]

In this rapidly evolving context, AHPs have employed a variety of approaches to organizing clinical and academic faculty roles, without guiding evidence or consensus‐based performance benchmarks. A number of AHPs have achieved success along traditional academic metrics of research, scholarship, and education. Currently, it is not known whether specific approaches to AHP organization, structure, or definition of faculty roles are associated with achievement of more traditional markers of academic success.

The Academic Committee of the Society of Hospital Medicine (SHM), and the Academic Hospitalist Task Force of the Society of General Internal Medicine (SGIM) had separately initiated projects to explore characteristics associated with success in AHPs. In 2012, these organizations combined efforts to jointly develop and implement the SCHOLAR (SuCcessful HOspitaLists in Academics and Research) project. The goals were to identify successful AHPs using objective criteria, and to then study those groups in greater detail to generate insights that would be broadly relevant to the field. Efforts to clarify the factors within AHPs linked to success by traditional academic metrics will benefit hospitalists, their leaders, and key stakeholders striving to achieve optimal balance between clinical and academic roles. We describe the initial work of the SCHOLAR project, our definitions of academic success in AHPs, and the characteristics of a cohort of exemplary AHPs who achieved the highest levels on these metrics.

METHODS

RESULTS

Faculty Promotion

Figure 1 demonstrates the proportion of faculty at various academic ranks. The percent of faculty above the rank of assistant professor in the SCHOLAR programs exceeded those in the overall LAHP‐50 by 5% (17.9% vs 12.8%, P = 0.01). Of note, 6% of the hospitalists at AHPs in the SCHOLAR programs were on nonfaculty tracks.

Figure 1

DISCUSSION

Using a definition of academic success that incorporated metrics of grant funding, faculty promotion, and scholarly output, we identified a unique subset of successful AHPsthe SCHOLAR cohort. The programs represented in the SCHOLAR cohort were generally large and relatively mature. Despite this, the cohort consisted of mostly junior faculty, had a paucity of fellowship‐trained hospitalists, and not all reported grant funding.

Prior published work reported complementary findings.[6, 8, 9] A survey of 20 large, well‐established academic hospitalist programs in 2008 found that the majority of hospitalists were junior faculty with a limited publication portfolio. Of the 266 respondents in that study, 86% reported an academic rank at or below assistant professor; funding was not explored.[9] Our similar findings 4 years later add to this work by demonstrating trends over time, and suggest that progress toward creating successful pathways for academic advancement has been slow. In a 2012 survey of the SHM membership, 28% of hospitalists with academic appointments reported no current or future plans to engage in research.[8] These findings suggest that faculty in AHPs may define scholarship through nontraditional pathways, or in some cases choose not to pursue or prioritize scholarship altogether.

Our findings also add to the literature with regard to our assessment of funding, which was variable across the SCHOLAR group. The broad range of funding in the SCHOLAR programs for which we have data (grant dollars $0$15 million per program) suggests that opportunities to improve supported scholarship remain, even among a selected cohort of successful AHPs. The predominance of junior faculty in the SCHOLAR programs may be a reason for this variation. Junior faculty may be engaged in research with funding directed to senior mentors outside their AHP. Alternatively, they may pursue meaningful local hospital quality improvement or educational innovations not supported by external grants, or hold leadership roles in education, quality, or information technology that allow for advancement and promotion without external grant funding. As the scope and impact of these roles increases, senior leaders with alternate sources of support may rely less on research funds; this too may explain some of the differences. Our findings are congruent with results of a study that reviewed original research published by hospitalists, and concluded that the majority of hospitalist research was not externally funded.[8] Our approach for assessing grant funding by adjusting for FTE had the potential to inadvertently favor smaller well‐funded groups over larger ones; however, programs in our sample were similarly represented when ranked by funding/FTE or total grant dollars. As many successful AHPs do concentrate their research funding among a core of focused hospitalist researchers, our definition may not be the ideal metric for some programs.

We chose to define scholarship based on abstract output, rather than peer‐reviewed publications. Although this choice was necessary from a feasibility perspective, it may have excluded programs that prioritize peer‐reviewed publications over abstracts. Although we were unable to incorporate a search strategy to accurately and comprehensively track the publication output attributed specifically to hospitalist researchers and quantify it by program, others have since defined such an approach.[8] However, tracking abstracts theoretically allowed insights into a larger volume of innovative and creative work generated by top AHPs by potentially including work in the earlier stages of development.

We used a consensus‐based definition of success to define our SCHOLAR cohort. There are other ways to measure academic success, which if applied, may have yielded a different sample of programs. For example, over half of the original research articles published in the Journal of Hospital Medicine over a 7‐year span were generated from 5 academic centers.[8] This definition of success may be equally credible, though we note that 4 of these 5 programs were also included in the SCHOLAR cohort. We feel our broader approach was more reflective of the variety of pathways to success available to academic hospitalists. Before our metrics are applied as a benchmarking tool, however, they should ideally be combined with factors not measured in our study to ensure a more comprehensive or balanced reflection of academic success. Factors such as mentorship, level of hospitalist engagement,[10] prevalence of leadership opportunities, operational and fiscal infrastructure, and the impact of local quality, safety, and value efforts should be considered.

Comparison of successfully promoted faculty at AHPs across the country is inherently limited by the wide variation in promotion standards across different institutions; controlling for such differences was not possible with our methodology. For example, it appears that several programs with relatively few senior faculty may have met metrics leading to their inclusion in the SCHOLAR group because of their small program size. Future benchmarking efforts for promotion at AHPs should take scaling into account and consider both total number as well as percentage of senior faculty when evaluating success.

Our methodology has several limitations. Survey data were self‐reported and not independently validated, and as such are subject to recall and reporting biases. Response bias inherently excluded some AHPs that may have met our grant funding or promotions criteria had they participated in the initial LAHP‐50 survey, though we identified and included additional programs through our scholarship metric, increasing the representativeness of the SCHOLAR cohort. Given the dynamic nature of the field, the age of the data we relied upon for analysis limits the generalizability of our specific benchmarks to current practice. However, the development of academic success occurs over the long‐term, and published data on academic hospitalist productivity are consistent with this slower time course.[8] Despite these limitations, our data inform the general topic of gauging performance of AHPs, underscoring the challenges of developing and applying metrics of success, and highlight the variability of performance on selected metrics even among a relatively small group of 17 programs.

In conclusion, we have created a method to quantify academic success that may be useful to academic hospitalists and their group leaders as they set targets for improvement in the field. Even among our SCHOLAR cohort, room for ongoing improvement in development of funded scholarship and a core of senior faculty exists. Further investigation into the unique features of successful groups will offer insight to leaders in academic hospital medicine regarding infrastructure and processes that should be embraced to raise the bar for all AHPs. In addition, efforts to further define and validate nontraditional approaches to scholarship that allow for successful promotion at AHPs would be informative. We view our work less as a singular approach to benchmarking standards for AHPs, and more a call to action to continue efforts to balance scholarly activity and broad professional development of academic hospitalists with increasing clinical demands.

The structure and function of academic hospital medicine programs (AHPs) has evolved significantly with the growth of hospital medicine.[1, 2, 3, 4] Many AHPs formed in response to regulatory and financial changes, which drove demand for increased trainee oversight, improved clinical efficiency, and growth in nonteaching services staffed by hospitalists. Differences in local organizational contexts and needs have contributed to great variability in AHP program design and operations. As AHPs have become more established, the need to engage academic hospitalists in scholarship and activities that support professional development and promotion has been recognized. Defining sustainable and successful positions for academic hospitalists is a priority called for by leaders in the field.[5, 6]

In this rapidly evolving context, AHPs have employed a variety of approaches to organizing clinical and academic faculty roles, without guiding evidence or consensus‐based performance benchmarks. A number of AHPs have achieved success along traditional academic metrics of research, scholarship, and education. Currently, it is not known whether specific approaches to AHP organization, structure, or definition of faculty roles are associated with achievement of more traditional markers of academic success.

The Academic Committee of the Society of Hospital Medicine (SHM), and the Academic Hospitalist Task Force of the Society of General Internal Medicine (SGIM) had separately initiated projects to explore characteristics associated with success in AHPs. In 2012, these organizations combined efforts to jointly develop and implement the SCHOLAR (SuCcessful HOspitaLists in Academics and Research) project. The goals were to identify successful AHPs using objective criteria, and to then study those groups in greater detail to generate insights that would be broadly relevant to the field. Efforts to clarify the factors within AHPs linked to success by traditional academic metrics will benefit hospitalists, their leaders, and key stakeholders striving to achieve optimal balance between clinical and academic roles. We describe the initial work of the SCHOLAR project, our definitions of academic success in AHPs, and the characteristics of a cohort of exemplary AHPs who achieved the highest levels on these metrics.

METHODS

RESULTS

Faculty Promotion

Figure 1 demonstrates the proportion of faculty at various academic ranks. The percent of faculty above the rank of assistant professor in the SCHOLAR programs exceeded those in the overall LAHP‐50 by 5% (17.9% vs 12.8%, P = 0.01). Of note, 6% of the hospitalists at AHPs in the SCHOLAR programs were on nonfaculty tracks.

Figure 1

DISCUSSION

Using a definition of academic success that incorporated metrics of grant funding, faculty promotion, and scholarly output, we identified a unique subset of successful AHPsthe SCHOLAR cohort. The programs represented in the SCHOLAR cohort were generally large and relatively mature. Despite this, the cohort consisted of mostly junior faculty, had a paucity of fellowship‐trained hospitalists, and not all reported grant funding.

Prior published work reported complementary findings.[6, 8, 9] A survey of 20 large, well‐established academic hospitalist programs in 2008 found that the majority of hospitalists were junior faculty with a limited publication portfolio. Of the 266 respondents in that study, 86% reported an academic rank at or below assistant professor; funding was not explored.[9] Our similar findings 4 years later add to this work by demonstrating trends over time, and suggest that progress toward creating successful pathways for academic advancement has been slow. In a 2012 survey of the SHM membership, 28% of hospitalists with academic appointments reported no current or future plans to engage in research.[8] These findings suggest that faculty in AHPs may define scholarship through nontraditional pathways, or in some cases choose not to pursue or prioritize scholarship altogether.

Our findings also add to the literature with regard to our assessment of funding, which was variable across the SCHOLAR group. The broad range of funding in the SCHOLAR programs for which we have data (grant dollars $0$15 million per program) suggests that opportunities to improve supported scholarship remain, even among a selected cohort of successful AHPs. The predominance of junior faculty in the SCHOLAR programs may be a reason for this variation. Junior faculty may be engaged in research with funding directed to senior mentors outside their AHP. Alternatively, they may pursue meaningful local hospital quality improvement or educational innovations not supported by external grants, or hold leadership roles in education, quality, or information technology that allow for advancement and promotion without external grant funding. As the scope and impact of these roles increases, senior leaders with alternate sources of support may rely less on research funds; this too may explain some of the differences. Our findings are congruent with results of a study that reviewed original research published by hospitalists, and concluded that the majority of hospitalist research was not externally funded.[8] Our approach for assessing grant funding by adjusting for FTE had the potential to inadvertently favor smaller well‐funded groups over larger ones; however, programs in our sample were similarly represented when ranked by funding/FTE or total grant dollars. As many successful AHPs do concentrate their research funding among a core of focused hospitalist researchers, our definition may not be the ideal metric for some programs.

We chose to define scholarship based on abstract output, rather than peer‐reviewed publications. Although this choice was necessary from a feasibility perspective, it may have excluded programs that prioritize peer‐reviewed publications over abstracts. Although we were unable to incorporate a search strategy to accurately and comprehensively track the publication output attributed specifically to hospitalist researchers and quantify it by program, others have since defined such an approach.[8] However, tracking abstracts theoretically allowed insights into a larger volume of innovative and creative work generated by top AHPs by potentially including work in the earlier stages of development.

We used a consensus‐based definition of success to define our SCHOLAR cohort. There are other ways to measure academic success, which if applied, may have yielded a different sample of programs. For example, over half of the original research articles published in the Journal of Hospital Medicine over a 7‐year span were generated from 5 academic centers.[8] This definition of success may be equally credible, though we note that 4 of these 5 programs were also included in the SCHOLAR cohort. We feel our broader approach was more reflective of the variety of pathways to success available to academic hospitalists. Before our metrics are applied as a benchmarking tool, however, they should ideally be combined with factors not measured in our study to ensure a more comprehensive or balanced reflection of academic success. Factors such as mentorship, level of hospitalist engagement,[10] prevalence of leadership opportunities, operational and fiscal infrastructure, and the impact of local quality, safety, and value efforts should be considered.

Comparison of successfully promoted faculty at AHPs across the country is inherently limited by the wide variation in promotion standards across different institutions; controlling for such differences was not possible with our methodology. For example, it appears that several programs with relatively few senior faculty may have met metrics leading to their inclusion in the SCHOLAR group because of their small program size. Future benchmarking efforts for promotion at AHPs should take scaling into account and consider both total number as well as percentage of senior faculty when evaluating success.

Our methodology has several limitations. Survey data were self‐reported and not independently validated, and as such are subject to recall and reporting biases. Response bias inherently excluded some AHPs that may have met our grant funding or promotions criteria had they participated in the initial LAHP‐50 survey, though we identified and included additional programs through our scholarship metric, increasing the representativeness of the SCHOLAR cohort. Given the dynamic nature of the field, the age of the data we relied upon for analysis limits the generalizability of our specific benchmarks to current practice. However, the development of academic success occurs over the long‐term, and published data on academic hospitalist productivity are consistent with this slower time course.[8] Despite these limitations, our data inform the general topic of gauging performance of AHPs, underscoring the challenges of developing and applying metrics of success, and highlight the variability of performance on selected metrics even among a relatively small group of 17 programs.

In conclusion, we have created a method to quantify academic success that may be useful to academic hospitalists and their group leaders as they set targets for improvement in the field. Even among our SCHOLAR cohort, room for ongoing improvement in development of funded scholarship and a core of senior faculty exists. Further investigation into the unique features of successful groups will offer insight to leaders in academic hospital medicine regarding infrastructure and processes that should be embraced to raise the bar for all AHPs. In addition, efforts to further define and validate nontraditional approaches to scholarship that allow for successful promotion at AHPs would be informative. We view our work less as a singular approach to benchmarking standards for AHPs, and more a call to action to continue efforts to balance scholarly activity and broad professional development of academic hospitalists with increasing clinical demands.

The structure and function of academic hospital medicine programs (AHPs) has evolved significantly with the growth of hospital medicine.[1, 2, 3, 4] Many AHPs formed in response to regulatory and financial changes, which drove demand for increased trainee oversight, improved clinical efficiency, and growth in nonteaching services staffed by hospitalists. Differences in local organizational contexts and needs have contributed to great variability in AHP program design and operations. As AHPs have become more established, the need to engage academic hospitalists in scholarship and activities that support professional development and promotion has been recognized. Defining sustainable and successful positions for academic hospitalists is a priority called for by leaders in the field.[5, 6]

In this rapidly evolving context, AHPs have employed a variety of approaches to organizing clinical and academic faculty roles, without guiding evidence or consensus‐based performance benchmarks. A number of AHPs have achieved success along traditional academic metrics of research, scholarship, and education. Currently, it is not known whether specific approaches to AHP organization, structure, or definition of faculty roles are associated with achievement of more traditional markers of academic success.

The Academic Committee of the Society of Hospital Medicine (SHM), and the Academic Hospitalist Task Force of the Society of General Internal Medicine (SGIM) had separately initiated projects to explore characteristics associated with success in AHPs. In 2012, these organizations combined efforts to jointly develop and implement the SCHOLAR (SuCcessful HOspitaLists in Academics and Research) project. The goals were to identify successful AHPs using objective criteria, and to then study those groups in greater detail to generate insights that would be broadly relevant to the field. Efforts to clarify the factors within AHPs linked to success by traditional academic metrics will benefit hospitalists, their leaders, and key stakeholders striving to achieve optimal balance between clinical and academic roles. We describe the initial work of the SCHOLAR project, our definitions of academic success in AHPs, and the characteristics of a cohort of exemplary AHPs who achieved the highest levels on these metrics.

METHODS

RESULTS

Faculty Promotion

Figure 1 demonstrates the proportion of faculty at various academic ranks. The percent of faculty above the rank of assistant professor in the SCHOLAR programs exceeded those in the overall LAHP‐50 by 5% (17.9% vs 12.8%, P = 0.01). Of note, 6% of the hospitalists at AHPs in the SCHOLAR programs were on nonfaculty tracks.

Figure 1

DISCUSSION

Using a definition of academic success that incorporated metrics of grant funding, faculty promotion, and scholarly output, we identified a unique subset of successful AHPsthe SCHOLAR cohort. The programs represented in the SCHOLAR cohort were generally large and relatively mature. Despite this, the cohort consisted of mostly junior faculty, had a paucity of fellowship‐trained hospitalists, and not all reported grant funding.

Prior published work reported complementary findings.[6, 8, 9] A survey of 20 large, well‐established academic hospitalist programs in 2008 found that the majority of hospitalists were junior faculty with a limited publication portfolio. Of the 266 respondents in that study, 86% reported an academic rank at or below assistant professor; funding was not explored.[9] Our similar findings 4 years later add to this work by demonstrating trends over time, and suggest that progress toward creating successful pathways for academic advancement has been slow. In a 2012 survey of the SHM membership, 28% of hospitalists with academic appointments reported no current or future plans to engage in research.[8] These findings suggest that faculty in AHPs may define scholarship through nontraditional pathways, or in some cases choose not to pursue or prioritize scholarship altogether.

Our findings also add to the literature with regard to our assessment of funding, which was variable across the SCHOLAR group. The broad range of funding in the SCHOLAR programs for which we have data (grant dollars $0$15 million per program) suggests that opportunities to improve supported scholarship remain, even among a selected cohort of successful AHPs. The predominance of junior faculty in the SCHOLAR programs may be a reason for this variation. Junior faculty may be engaged in research with funding directed to senior mentors outside their AHP. Alternatively, they may pursue meaningful local hospital quality improvement or educational innovations not supported by external grants, or hold leadership roles in education, quality, or information technology that allow for advancement and promotion without external grant funding. As the scope and impact of these roles increases, senior leaders with alternate sources of support may rely less on research funds; this too may explain some of the differences. Our findings are congruent with results of a study that reviewed original research published by hospitalists, and concluded that the majority of hospitalist research was not externally funded.[8] Our approach for assessing grant funding by adjusting for FTE had the potential to inadvertently favor smaller well‐funded groups over larger ones; however, programs in our sample were similarly represented when ranked by funding/FTE or total grant dollars. As many successful AHPs do concentrate their research funding among a core of focused hospitalist researchers, our definition may not be the ideal metric for some programs.

We chose to define scholarship based on abstract output, rather than peer‐reviewed publications. Although this choice was necessary from a feasibility perspective, it may have excluded programs that prioritize peer‐reviewed publications over abstracts. Although we were unable to incorporate a search strategy to accurately and comprehensively track the publication output attributed specifically to hospitalist researchers and quantify it by program, others have since defined such an approach.[8] However, tracking abstracts theoretically allowed insights into a larger volume of innovative and creative work generated by top AHPs by potentially including work in the earlier stages of development.

We used a consensus‐based definition of success to define our SCHOLAR cohort. There are other ways to measure academic success, which if applied, may have yielded a different sample of programs. For example, over half of the original research articles published in the Journal of Hospital Medicine over a 7‐year span were generated from 5 academic centers.[8] This definition of success may be equally credible, though we note that 4 of these 5 programs were also included in the SCHOLAR cohort. We feel our broader approach was more reflective of the variety of pathways to success available to academic hospitalists. Before our metrics are applied as a benchmarking tool, however, they should ideally be combined with factors not measured in our study to ensure a more comprehensive or balanced reflection of academic success. Factors such as mentorship, level of hospitalist engagement,[10] prevalence of leadership opportunities, operational and fiscal infrastructure, and the impact of local quality, safety, and value efforts should be considered.

Comparison of successfully promoted faculty at AHPs across the country is inherently limited by the wide variation in promotion standards across different institutions; controlling for such differences was not possible with our methodology. For example, it appears that several programs with relatively few senior faculty may have met metrics leading to their inclusion in the SCHOLAR group because of their small program size. Future benchmarking efforts for promotion at AHPs should take scaling into account and consider both total number as well as percentage of senior faculty when evaluating success.

Our methodology has several limitations. Survey data were self‐reported and not independently validated, and as such are subject to recall and reporting biases. Response bias inherently excluded some AHPs that may have met our grant funding or promotions criteria had they participated in the initial LAHP‐50 survey, though we identified and included additional programs through our scholarship metric, increasing the representativeness of the SCHOLAR cohort. Given the dynamic nature of the field, the age of the data we relied upon for analysis limits the generalizability of our specific benchmarks to current practice. However, the development of academic success occurs over the long‐term, and published data on academic hospitalist productivity are consistent with this slower time course.[8] Despite these limitations, our data inform the general topic of gauging performance of AHPs, underscoring the challenges of developing and applying metrics of success, and highlight the variability of performance on selected metrics even among a relatively small group of 17 programs.

In conclusion, we have created a method to quantify academic success that may be useful to academic hospitalists and their group leaders as they set targets for improvement in the field. Even among our SCHOLAR cohort, room for ongoing improvement in development of funded scholarship and a core of senior faculty exists. Further investigation into the unique features of successful groups will offer insight to leaders in academic hospital medicine regarding infrastructure and processes that should be embraced to raise the bar for all AHPs. In addition, efforts to further define and validate nontraditional approaches to scholarship that allow for successful promotion at AHPs would be informative. We view our work less as a singular approach to benchmarking standards for AHPs, and more a call to action to continue efforts to balance scholarly activity and broad professional development of academic hospitalists with increasing clinical demands.

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

RESULTS

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Table 2.Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status

Characteristic	Bivariable Analysis*		Multivariable Analysis*
	OR (95% CI)	P Value	OR (95% CI)	P Value
NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.
Age, 10 y	1.09 (1.08 to 1.09)	<0.001	0.97 (0.95 to 0.98)	<0.001
Male	1.13 (1.09 to 1.17)	<0.001	1.01 (0.97 to 1.05)	0.76
Race
Caucasian	Referent		Referent
African American	1.21 (1.17 to 1.26)	<0.001	1.01 (0.96 to 1.05)	0.74
Other	0.84 (0.79 to 0.90)	<0.001	0.92 (0.86 to 0.98)	0.01
Payer
Medicare	Referent		Referent
Medicaid	0.74 (0.70 to 0.77)	<0.001	1.03 (0.97 to 1.09)	0.42
Other	0.57 (0.55 to 0.60)	<0.001	0.86 (0.82 to 0.91)	<0.001
Hospital service
Medicine	Referent		Referent
Gynecologyobstetrics	0.18 (0.16 to 0.20)	<0.001	0.50 (0.45 to 0.56)	<0.001
Neurosciences	0.43 (0.40 to 0.46)	<0.001	0.76 (0.70 to 0.82)	<0.001
Oncology	1.14 (1.07 to 1.22)	<0.001	1.18 (1.10 to 1.28)	<0.001
Pediatrics	0.54 (0.51 to 0.57)	<0.001	0.77 (0.71 to 0.83)	<0.001
Surgical sciences	0.57 (0.54 to 0.60)	<0.001	0.92 (0.87 to 0.97)	0.002
Discharge location
Home			Referent
Facility (SNF, rehabilitation facility)	1.90 (1.77 to 2.03)	<0.001	1.11 (1.02 to 1.19)	0.009
Home with homecare or hospice	1.73 (1.64 to 1.83)	<0.001	1.26 (1.19 to 1.34)	<0.001
Other	1.89 (1.78 to 2.00)	<0.001	1.25 (1.18 to 1.34)	<0.001
Length of stay, d	1.03 (1.02 to 1.03)	<0.001	1.00 (1.00 to 1.01)	<0.001
APRDRG‐SOI expected readmission rate, %	1.08 (1.07 to 1.08)	<0.001	1.06 (1.06 to 1.06)	<0.001
AHRQ Comorbidity Index (1 point)	1.27 (1.26 to 1.28)	<0.001	1.11 (1.09 to 1.12)	<0.001
Discharge summary completed >3 days	1.09 (1.04 to 1.14)	<0.001	1.09 (1.05 to 1.14)	<0.001

Figure 1

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

RESULTS

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Table 2.Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status

Characteristic	Bivariable Analysis*		Multivariable Analysis*
	OR (95% CI)	P Value	OR (95% CI)	P Value
NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.
Age, 10 y	1.09 (1.08 to 1.09)	<0.001	0.97 (0.95 to 0.98)	<0.001
Male	1.13 (1.09 to 1.17)	<0.001	1.01 (0.97 to 1.05)	0.76
Race
Caucasian	Referent		Referent
African American	1.21 (1.17 to 1.26)	<0.001	1.01 (0.96 to 1.05)	0.74
Other	0.84 (0.79 to 0.90)	<0.001	0.92 (0.86 to 0.98)	0.01
Payer
Medicare	Referent		Referent
Medicaid	0.74 (0.70 to 0.77)	<0.001	1.03 (0.97 to 1.09)	0.42
Other	0.57 (0.55 to 0.60)	<0.001	0.86 (0.82 to 0.91)	<0.001
Hospital service
Medicine	Referent		Referent
Gynecologyobstetrics	0.18 (0.16 to 0.20)	<0.001	0.50 (0.45 to 0.56)	<0.001
Neurosciences	0.43 (0.40 to 0.46)	<0.001	0.76 (0.70 to 0.82)	<0.001
Oncology	1.14 (1.07 to 1.22)	<0.001	1.18 (1.10 to 1.28)	<0.001
Pediatrics	0.54 (0.51 to 0.57)	<0.001	0.77 (0.71 to 0.83)	<0.001
Surgical sciences	0.57 (0.54 to 0.60)	<0.001	0.92 (0.87 to 0.97)	0.002
Discharge location
Home			Referent
Facility (SNF, rehabilitation facility)	1.90 (1.77 to 2.03)	<0.001	1.11 (1.02 to 1.19)	0.009
Home with homecare or hospice	1.73 (1.64 to 1.83)	<0.001	1.26 (1.19 to 1.34)	<0.001
Other	1.89 (1.78 to 2.00)	<0.001	1.25 (1.18 to 1.34)	<0.001
Length of stay, d	1.03 (1.02 to 1.03)	<0.001	1.00 (1.00 to 1.01)	<0.001
APRDRG‐SOI expected readmission rate, %	1.08 (1.07 to 1.08)	<0.001	1.06 (1.06 to 1.06)	<0.001
AHRQ Comorbidity Index (1 point)	1.27 (1.26 to 1.28)	<0.001	1.11 (1.09 to 1.12)	<0.001
Discharge summary completed >3 days	1.09 (1.04 to 1.14)	<0.001	1.09 (1.05 to 1.14)	<0.001

Figure 1

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

RESULTS

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Table 2.Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status

Characteristic	Bivariable Analysis*		Multivariable Analysis*
	OR (95% CI)	P Value	OR (95% CI)	P Value
NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.
Age, 10 y	1.09 (1.08 to 1.09)	<0.001	0.97 (0.95 to 0.98)	<0.001
Male	1.13 (1.09 to 1.17)	<0.001	1.01 (0.97 to 1.05)	0.76
Race
Caucasian	Referent		Referent
African American	1.21 (1.17 to 1.26)	<0.001	1.01 (0.96 to 1.05)	0.74
Other	0.84 (0.79 to 0.90)	<0.001	0.92 (0.86 to 0.98)	0.01
Payer
Medicare	Referent		Referent
Medicaid	0.74 (0.70 to 0.77)	<0.001	1.03 (0.97 to 1.09)	0.42
Other	0.57 (0.55 to 0.60)	<0.001	0.86 (0.82 to 0.91)	<0.001
Hospital service
Medicine	Referent		Referent
Gynecologyobstetrics	0.18 (0.16 to 0.20)	<0.001	0.50 (0.45 to 0.56)	<0.001
Neurosciences	0.43 (0.40 to 0.46)	<0.001	0.76 (0.70 to 0.82)	<0.001
Oncology	1.14 (1.07 to 1.22)	<0.001	1.18 (1.10 to 1.28)	<0.001
Pediatrics	0.54 (0.51 to 0.57)	<0.001	0.77 (0.71 to 0.83)	<0.001
Surgical sciences	0.57 (0.54 to 0.60)	<0.001	0.92 (0.87 to 0.97)	0.002
Discharge location
Home			Referent
Facility (SNF, rehabilitation facility)	1.90 (1.77 to 2.03)	<0.001	1.11 (1.02 to 1.19)	0.009
Home with homecare or hospice	1.73 (1.64 to 1.83)	<0.001	1.26 (1.19 to 1.34)	<0.001
Other	1.89 (1.78 to 2.00)	<0.001	1.25 (1.18 to 1.34)	<0.001
Length of stay, d	1.03 (1.02 to 1.03)	<0.001	1.00 (1.00 to 1.01)	<0.001
APRDRG‐SOI expected readmission rate, %	1.08 (1.07 to 1.08)	<0.001	1.06 (1.06 to 1.06)	<0.001
AHRQ Comorbidity Index (1 point)	1.27 (1.26 to 1.28)	<0.001	1.11 (1.09 to 1.12)	<0.001
Discharge summary completed >3 days	1.09 (1.04 to 1.14)	<0.001	1.09 (1.05 to 1.14)	<0.001

Figure 1

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Annually, more than 35 million patients are hospitalized in the United States, with many experiencing hospital‐acquired impairments in physical functioning during their in‐patient stay.[1, 2, 3, 4] Such impairments include difficulties performing basic activities of daily living, such as rising from a chair, toileting, or ambulating. This functional decline may result in increased length of stay (LOS), nursing home placement, and decreased mobility and participation in community activities even years after hospitalization.[1, 2, 3, 5, 6, 7] Ameliorating this hospital‐acquired functional impairment is important to improving patient outcomes and reducing healthcare utilization. Even the sickest hospitalized patients (eg, those in the intensive care unit [ICU]), can safely and feasibly benefit from early mobilization.[6, 8, 9, 10, 11] In the non‐ICU setting there is also evidence that patient mobilization reduces LOS and hospital costs, while improving patient satisfaction and physical and psychological outcomes.[12, 13, 14, 15, 16] These studies are, however, difficult to replicate as part of routine clinical care, because they often do not present the details of how early mobility was incorporated into daily practice, require additional hospital resources (eg, specially trained providers or additional staff), or are focused only on a select patient population.

The Johns Hopkins medical ICU started early rehabilitation quality‐improvement (QI) work in 2007, which has demonstrated ongoing reductions in LOS and been transformative in terms of helping to foster a culture of mobility at our institution. Previous research suggests that ICU‐based rehabilitation interventions are often not carried over to the ward setting, even in post‐ICU patients.[17] Moreover, trends for sicker patients being admitted in our general medicine units,[18] growing reports of patients spending most of their time in bed,[2, 19, 20] and healthcare policies emphasizing the importance of improving inpatient outcomes motivated the need for QI to improve patient mobility in this setting. Experience from the medical ICU‐based early rehabilitation program helped drive multidisciplinary collaboration of stakeholders to develop this nurse‐driven, mobility promotion QI project on 2 general medicine hospital units. The main goals of the project were to see whether a QI framework can be used in a general medicine setting to increase patient mobility and reduce LOS.[21, 22]

METHODS

RESULTS

During the QI project period, 3352 patients were admitted to the 2 general medicine units. Twelve (0.4%) patients expired on the units, but their data were retained in the analysis. Mean (standard deviation [SD]) age of the patients was 54.4 (18.3) years, with 47% male, and 54% African American. A total of 1896 of 6654 (28%) patients on the QI units were 65 years old. Patient characteristics were similar during the QI period versus 12 months prior (Table 1).

During the 12‐month QI project, there were a total of 13,815 patient‐days of documented mobility data and the median (interquartile range [IQR]) number of days of documentation for each hospital admission was 3 (25) days. Compliance with daily documentation of JH‐HLM was 85.0% over the entire 12‐month QI project. Documentation compliance started at 83% during the ramp‐up phase and increased to 89% during the last 4 months of the project (late‐QI phase, P < 0.001).

Comparing the ramp‐up phase versus post‐QI phase, the percentage of patient‐days in which patients ambulated (JH‐HLM 6) increased from 43% to 70% (P < 0.001), and the percentage of patients who experienced an improvement in their mobility scores between admission and discharge increased from 32% to 45% (P < 0.001), as shown in Table 2. In the sensitivity analysis imputing missing daily JH‐HLM scores and comparing the ramp‐up versus post‐QI phases, the results were similar to the primary analysis; the percent of patient‐days where patients ambulated increased from 60% to 78% (P < 0.001), and the percent of patients who experienced an improvement in their mobility scores increased from 26% to 48% (P < 0.001).

LOS during the 12‐month QI project versus the 12‐months immediately prior was shorter (Table 3), with an unadjusted median (IQR) LOS of 3 (26) versus 4 (27) days (P < 0.001) and an unadjusted mean (SD) LOS of 5.1 (5.6) versus 6.0 (7.6) (P < 0.001).

Table 3 displays the change in adjusted median LOS for the project months versus the 12 months prior among the QI units. We found that for all patients, there was an overall reduction in adjusted median LOS of 0.40 (95% confidence interval [CI]: 0.57 to 0.21, P<0.001) days. When we divided patients into tertiles based on their UHC expected LOS (ELOS), we observed that patients with longer ELOS had greater reductions in adjusted median LOS. Patients on the QI units with ELOS <4 days (lowest tertile) did not show a significant reduction in adjusted median LOS (0.09 days, 95% CI: 0.13 to 0.32, P = 0.42); however, patients with UHC ELOS 4 to 7 days (middle tertile) and ELOS >7 days (highest tertile) had a significant reduction in adjusted median LOS by 0.30 (95% CI: 0.57 to 0.01, P = 0.04) and 1.11 (95% CI: 1.53 to 0.65, P < 0.001) days during the QI project versus 12 months prior, respectively.

Lastly, we found that there was no difference in the rate of injurious falls on the QI units during QI period compared to 12 months prior (QI: 0.34 per 1000 patient‐days versus 12 months prior: 0.48 per 1000 patient‐days, P = 0.73).

DISCUSSION

We conducted a nurse‐driven, multidisciplinary mobility promotion QI project on 2 general medicine units at a large teaching hospital. The 12‐month QI project, conducted between March 1, 2013 and February 28, 2014, was associated with patients ambulating more frequently, with improved mobility status between hospital admission and discharge. These improvements in mobility were not associated with increased rates of injurious falls, and were sustained for at least 4 months after project completion. The QI project was associated with overall significant reduction in LOS for more complex patients with longer expected LOS (4 days or longer). Hence, such QI efforts may be important for maintaining or improving patients' functional status during hospitalization in a safe and cost‐effective manner.

Our findings are consistent with previous studies showing that mobility promotion in the acute hospital setting is feasible, can reduce length of stay, and can be applied to a diverse population including vulnerable medical patients with multiple comorbidities and the elderly.[12, 16, 37, 38, 39, 40, 41, 42] These studies provide valuable evidence of the benefits of mobility promotion; however, it is difficult to translate these prior results into routine clinical practice because they used specially trained staff to mobilize patients, focused on a select patient population, or did not specify how the mobility intervention was delivered within daily clinical workflows. Research in the medical ICU at our institution has previously described the use of a structured QI model to successfully implement an early rehabilitation program.[22, 24] Here, we successfully adapted the same QI framework to a general medicine setting. Hence, our study contributes to the literature with respect to (1) use of a structured QI framework to develop a successful patient mobility program in a general medicine patient population, and (2) sharing best practices from 1 clinical setting, such as the ICU, as a source of learning and knowledge translation for other care settings, with the addition of novel tools, such as the JH‐HLM scale.

There may have been several factors that contributed to shorter stays in the hospital we observed during the QI project. First, we increased the number of ambulatory patient‐days, which may have helped prevent physiological complications of bed rest, such as muscle weakness, atelectasis, insulin resistance, vascular dysfunction, contractures, and pressure ulcers.[43] As such, mobility promotion has been associated with reduced rates of other hospital‐acquired complications, such as deep venous thrombosis, pneumonia, and delirium.[44, 45, 46] In our study, we saw the greatest LOS reduction in more complex patients who were expected to spend a longer time in the hospital and are at greater risk of developing complications from bed rest. Second, our early mobility project may have had a direct impact on care‐coordination processes as reported in prior studies.[47, 48, 49] An important component of our intervention was incorporating functional status into multidisciplinary discussions, either through nurse‐to‐therapist huddles or care‐coordination rounds between nurses, therapists, physicians, social workers, and case managers. During care‐coordination rounds, JH‐HLM scores were reported to expedite appropriate physical and occupational therapy consultations and assist in determining appropriate discharge location. During the QI project, we transitioned from a unit‐based daily huddle between nursing and rehabilitation therapists to a system where mobility status was discussed primarily during care coordination rounds 5 times per week. We saw that mobility scores were maintained after QI project completion, suggesting that reporting on patient function in a multidisciplinary setting is a potentially sustainable mechanism to improve care‐coordination processes that are affected by functional status.

Our study has several potential limitations. First, this is a single‐site study in 2 general medicine units of a large academic hospital. Further research is needed to determine if this structured QI intervention and its benefits can be generalized to different settings and different patient populations. Second, because the documentation was initially an optional element in the electronic medical record system, we observed higher rates of missing documentation during the first 4 months of the project versus the comparison period at 4 months after project completion. However, a sensitivity analysis conducted of these missing data demonstrated similar results to our primary analysis. Third, our nonrandomized pre‐post study design does not allow us to conclude a direct cause‐and‐effect relationship between our intervention and increased mobility and reduced LOS. Although patient characteristics were similar between the 2 periods and adjusted for in our multivariable regression analysis, we cannot rule out the possibility of secular trends in LOS on the project units and that broader QI efforts at our institution also contributed to reduction in LOS. Fourth, we do not have data on 30‐day readmissions and discharge location. Future studies should explore the impact of hospital‐based mobility interventions on these outcomes.[50] Fifth, although nurses consistently documented the highest level of mobility on a daily basis, these data did not capture other potentially important information about patient mobility such as the daily frequency that patients were mobilized, the length of time a patient was engaged in a mobility event (ie, number of hours sitting in a chair), or the mobility that occurred during physical therapy or occupational therapy sessions. Hence, although we used JH‐HLM as a marker of improved mobility during our QI project it is likely that our data cannot fully describe the total mobility and activity that patients experienced during hospitalization. Lastly, although the front‐line staff and QI team found the JH‐HLM scale to be a useful tool to measure and advance patient mobility, further studies are needed to evaluate the reliability and validity of this scale.

CONCLUSION

A structured QI process can improve patient mobility and may contribute to reduction in LOS, particularly for more complex patients in this setting. Active prevention of decline in physical function that commonly occurs during hospitalization may prove valuable for improving patient outcomes and reducing healthcare resource utilization.

Annually, more than 35 million patients are hospitalized in the United States, with many experiencing hospital‐acquired impairments in physical functioning during their in‐patient stay.[1, 2, 3, 4] Such impairments include difficulties performing basic activities of daily living, such as rising from a chair, toileting, or ambulating. This functional decline may result in increased length of stay (LOS), nursing home placement, and decreased mobility and participation in community activities even years after hospitalization.[1, 2, 3, 5, 6, 7] Ameliorating this hospital‐acquired functional impairment is important to improving patient outcomes and reducing healthcare utilization. Even the sickest hospitalized patients (eg, those in the intensive care unit [ICU]), can safely and feasibly benefit from early mobilization.[6, 8, 9, 10, 11] In the non‐ICU setting there is also evidence that patient mobilization reduces LOS and hospital costs, while improving patient satisfaction and physical and psychological outcomes.[12, 13, 14, 15, 16] These studies are, however, difficult to replicate as part of routine clinical care, because they often do not present the details of how early mobility was incorporated into daily practice, require additional hospital resources (eg, specially trained providers or additional staff), or are focused only on a select patient population.

The Johns Hopkins medical ICU started early rehabilitation quality‐improvement (QI) work in 2007, which has demonstrated ongoing reductions in LOS and been transformative in terms of helping to foster a culture of mobility at our institution. Previous research suggests that ICU‐based rehabilitation interventions are often not carried over to the ward setting, even in post‐ICU patients.[17] Moreover, trends for sicker patients being admitted in our general medicine units,[18] growing reports of patients spending most of their time in bed,[2, 19, 20] and healthcare policies emphasizing the importance of improving inpatient outcomes motivated the need for QI to improve patient mobility in this setting. Experience from the medical ICU‐based early rehabilitation program helped drive multidisciplinary collaboration of stakeholders to develop this nurse‐driven, mobility promotion QI project on 2 general medicine hospital units. The main goals of the project were to see whether a QI framework can be used in a general medicine setting to increase patient mobility and reduce LOS.[21, 22]

METHODS

RESULTS

During the QI project period, 3352 patients were admitted to the 2 general medicine units. Twelve (0.4%) patients expired on the units, but their data were retained in the analysis. Mean (standard deviation [SD]) age of the patients was 54.4 (18.3) years, with 47% male, and 54% African American. A total of 1896 of 6654 (28%) patients on the QI units were 65 years old. Patient characteristics were similar during the QI period versus 12 months prior (Table 1).

During the 12‐month QI project, there were a total of 13,815 patient‐days of documented mobility data and the median (interquartile range [IQR]) number of days of documentation for each hospital admission was 3 (25) days. Compliance with daily documentation of JH‐HLM was 85.0% over the entire 12‐month QI project. Documentation compliance started at 83% during the ramp‐up phase and increased to 89% during the last 4 months of the project (late‐QI phase, P < 0.001).

Comparing the ramp‐up phase versus post‐QI phase, the percentage of patient‐days in which patients ambulated (JH‐HLM 6) increased from 43% to 70% (P < 0.001), and the percentage of patients who experienced an improvement in their mobility scores between admission and discharge increased from 32% to 45% (P < 0.001), as shown in Table 2. In the sensitivity analysis imputing missing daily JH‐HLM scores and comparing the ramp‐up versus post‐QI phases, the results were similar to the primary analysis; the percent of patient‐days where patients ambulated increased from 60% to 78% (P < 0.001), and the percent of patients who experienced an improvement in their mobility scores increased from 26% to 48% (P < 0.001).

LOS during the 12‐month QI project versus the 12‐months immediately prior was shorter (Table 3), with an unadjusted median (IQR) LOS of 3 (26) versus 4 (27) days (P < 0.001) and an unadjusted mean (SD) LOS of 5.1 (5.6) versus 6.0 (7.6) (P < 0.001).

Table 3 displays the change in adjusted median LOS for the project months versus the 12 months prior among the QI units. We found that for all patients, there was an overall reduction in adjusted median LOS of 0.40 (95% confidence interval [CI]: 0.57 to 0.21, P<0.001) days. When we divided patients into tertiles based on their UHC expected LOS (ELOS), we observed that patients with longer ELOS had greater reductions in adjusted median LOS. Patients on the QI units with ELOS <4 days (lowest tertile) did not show a significant reduction in adjusted median LOS (0.09 days, 95% CI: 0.13 to 0.32, P = 0.42); however, patients with UHC ELOS 4 to 7 days (middle tertile) and ELOS >7 days (highest tertile) had a significant reduction in adjusted median LOS by 0.30 (95% CI: 0.57 to 0.01, P = 0.04) and 1.11 (95% CI: 1.53 to 0.65, P < 0.001) days during the QI project versus 12 months prior, respectively.

Lastly, we found that there was no difference in the rate of injurious falls on the QI units during QI period compared to 12 months prior (QI: 0.34 per 1000 patient‐days versus 12 months prior: 0.48 per 1000 patient‐days, P = 0.73).

DISCUSSION

We conducted a nurse‐driven, multidisciplinary mobility promotion QI project on 2 general medicine units at a large teaching hospital. The 12‐month QI project, conducted between March 1, 2013 and February 28, 2014, was associated with patients ambulating more frequently, with improved mobility status between hospital admission and discharge. These improvements in mobility were not associated with increased rates of injurious falls, and were sustained for at least 4 months after project completion. The QI project was associated with overall significant reduction in LOS for more complex patients with longer expected LOS (4 days or longer). Hence, such QI efforts may be important for maintaining or improving patients' functional status during hospitalization in a safe and cost‐effective manner.

Our findings are consistent with previous studies showing that mobility promotion in the acute hospital setting is feasible, can reduce length of stay, and can be applied to a diverse population including vulnerable medical patients with multiple comorbidities and the elderly.[12, 16, 37, 38, 39, 40, 41, 42] These studies provide valuable evidence of the benefits of mobility promotion; however, it is difficult to translate these prior results into routine clinical practice because they used specially trained staff to mobilize patients, focused on a select patient population, or did not specify how the mobility intervention was delivered within daily clinical workflows. Research in the medical ICU at our institution has previously described the use of a structured QI model to successfully implement an early rehabilitation program.[22, 24] Here, we successfully adapted the same QI framework to a general medicine setting. Hence, our study contributes to the literature with respect to (1) use of a structured QI framework to develop a successful patient mobility program in a general medicine patient population, and (2) sharing best practices from 1 clinical setting, such as the ICU, as a source of learning and knowledge translation for other care settings, with the addition of novel tools, such as the JH‐HLM scale.

There may have been several factors that contributed to shorter stays in the hospital we observed during the QI project. First, we increased the number of ambulatory patient‐days, which may have helped prevent physiological complications of bed rest, such as muscle weakness, atelectasis, insulin resistance, vascular dysfunction, contractures, and pressure ulcers.[43] As such, mobility promotion has been associated with reduced rates of other hospital‐acquired complications, such as deep venous thrombosis, pneumonia, and delirium.[44, 45, 46] In our study, we saw the greatest LOS reduction in more complex patients who were expected to spend a longer time in the hospital and are at greater risk of developing complications from bed rest. Second, our early mobility project may have had a direct impact on care‐coordination processes as reported in prior studies.[47, 48, 49] An important component of our intervention was incorporating functional status into multidisciplinary discussions, either through nurse‐to‐therapist huddles or care‐coordination rounds between nurses, therapists, physicians, social workers, and case managers. During care‐coordination rounds, JH‐HLM scores were reported to expedite appropriate physical and occupational therapy consultations and assist in determining appropriate discharge location. During the QI project, we transitioned from a unit‐based daily huddle between nursing and rehabilitation therapists to a system where mobility status was discussed primarily during care coordination rounds 5 times per week. We saw that mobility scores were maintained after QI project completion, suggesting that reporting on patient function in a multidisciplinary setting is a potentially sustainable mechanism to improve care‐coordination processes that are affected by functional status.

Our study has several potential limitations. First, this is a single‐site study in 2 general medicine units of a large academic hospital. Further research is needed to determine if this structured QI intervention and its benefits can be generalized to different settings and different patient populations. Second, because the documentation was initially an optional element in the electronic medical record system, we observed higher rates of missing documentation during the first 4 months of the project versus the comparison period at 4 months after project completion. However, a sensitivity analysis conducted of these missing data demonstrated similar results to our primary analysis. Third, our nonrandomized pre‐post study design does not allow us to conclude a direct cause‐and‐effect relationship between our intervention and increased mobility and reduced LOS. Although patient characteristics were similar between the 2 periods and adjusted for in our multivariable regression analysis, we cannot rule out the possibility of secular trends in LOS on the project units and that broader QI efforts at our institution also contributed to reduction in LOS. Fourth, we do not have data on 30‐day readmissions and discharge location. Future studies should explore the impact of hospital‐based mobility interventions on these outcomes.[50] Fifth, although nurses consistently documented the highest level of mobility on a daily basis, these data did not capture other potentially important information about patient mobility such as the daily frequency that patients were mobilized, the length of time a patient was engaged in a mobility event (ie, number of hours sitting in a chair), or the mobility that occurred during physical therapy or occupational therapy sessions. Hence, although we used JH‐HLM as a marker of improved mobility during our QI project it is likely that our data cannot fully describe the total mobility and activity that patients experienced during hospitalization. Lastly, although the front‐line staff and QI team found the JH‐HLM scale to be a useful tool to measure and advance patient mobility, further studies are needed to evaluate the reliability and validity of this scale.

CONCLUSION

A structured QI process can improve patient mobility and may contribute to reduction in LOS, particularly for more complex patients in this setting. Active prevention of decline in physical function that commonly occurs during hospitalization may prove valuable for improving patient outcomes and reducing healthcare resource utilization.

Annually, more than 35 million patients are hospitalized in the United States, with many experiencing hospital‐acquired impairments in physical functioning during their in‐patient stay.[1, 2, 3, 4] Such impairments include difficulties performing basic activities of daily living, such as rising from a chair, toileting, or ambulating. This functional decline may result in increased length of stay (LOS), nursing home placement, and decreased mobility and participation in community activities even years after hospitalization.[1, 2, 3, 5, 6, 7] Ameliorating this hospital‐acquired functional impairment is important to improving patient outcomes and reducing healthcare utilization. Even the sickest hospitalized patients (eg, those in the intensive care unit [ICU]), can safely and feasibly benefit from early mobilization.[6, 8, 9, 10, 11] In the non‐ICU setting there is also evidence that patient mobilization reduces LOS and hospital costs, while improving patient satisfaction and physical and psychological outcomes.[12, 13, 14, 15, 16] These studies are, however, difficult to replicate as part of routine clinical care, because they often do not present the details of how early mobility was incorporated into daily practice, require additional hospital resources (eg, specially trained providers or additional staff), or are focused only on a select patient population.

The Johns Hopkins medical ICU started early rehabilitation quality‐improvement (QI) work in 2007, which has demonstrated ongoing reductions in LOS and been transformative in terms of helping to foster a culture of mobility at our institution. Previous research suggests that ICU‐based rehabilitation interventions are often not carried over to the ward setting, even in post‐ICU patients.[17] Moreover, trends for sicker patients being admitted in our general medicine units,[18] growing reports of patients spending most of their time in bed,[2, 19, 20] and healthcare policies emphasizing the importance of improving inpatient outcomes motivated the need for QI to improve patient mobility in this setting. Experience from the medical ICU‐based early rehabilitation program helped drive multidisciplinary collaboration of stakeholders to develop this nurse‐driven, mobility promotion QI project on 2 general medicine hospital units. The main goals of the project were to see whether a QI framework can be used in a general medicine setting to increase patient mobility and reduce LOS.[21, 22]

METHODS

RESULTS

During the QI project period, 3352 patients were admitted to the 2 general medicine units. Twelve (0.4%) patients expired on the units, but their data were retained in the analysis. Mean (standard deviation [SD]) age of the patients was 54.4 (18.3) years, with 47% male, and 54% African American. A total of 1896 of 6654 (28%) patients on the QI units were 65 years old. Patient characteristics were similar during the QI period versus 12 months prior (Table 1).

During the 12‐month QI project, there were a total of 13,815 patient‐days of documented mobility data and the median (interquartile range [IQR]) number of days of documentation for each hospital admission was 3 (25) days. Compliance with daily documentation of JH‐HLM was 85.0% over the entire 12‐month QI project. Documentation compliance started at 83% during the ramp‐up phase and increased to 89% during the last 4 months of the project (late‐QI phase, P < 0.001).

Comparing the ramp‐up phase versus post‐QI phase, the percentage of patient‐days in which patients ambulated (JH‐HLM 6) increased from 43% to 70% (P < 0.001), and the percentage of patients who experienced an improvement in their mobility scores between admission and discharge increased from 32% to 45% (P < 0.001), as shown in Table 2. In the sensitivity analysis imputing missing daily JH‐HLM scores and comparing the ramp‐up versus post‐QI phases, the results were similar to the primary analysis; the percent of patient‐days where patients ambulated increased from 60% to 78% (P < 0.001), and the percent of patients who experienced an improvement in their mobility scores increased from 26% to 48% (P < 0.001).