Elizabeth H. Bradley, PhD

With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]

In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.

Although previous research has examined strategies used by hospitals enrolled in H2H,¹⁰ we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.

Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.

METHODS

RESULTS

DISCUSSION

We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.

Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.

The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.

Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.

The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.

In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.

With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]

In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.

Although previous research has examined strategies used by hospitals enrolled in H2H,¹⁰ we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.

Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.

METHODS

RESULTS

DISCUSSION

We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.

Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.

The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.

Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.

The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.

In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.

With US hospital readmission rates within 30 days of discharge approaching 20%,[1] reducing readmissions has become a national priority. Hospitalists are frequently involved in quality improvement efforts to improve transitions from hospital to home,[2, 3] and they play critical roles in implementing recommended strategies to support effective discharge transitions.[4, 5] Initiatives such as Better Outcomes for Older Adults through Safe Transitions[6] and the adaptable Transitions Tool[7] from the Society of Hospital Medicine provide important approaches and checklists for helping hospitals improve strategies.[8]

In addition to these initiatives, multiple quality collaboratives and campaigns are underway to help hospitals reduce their readmission rates. Two of the more prominent efforts are the STAAR (STate Action on Avoidable Rehospitalization) initiative,[9] a learning collaborative launched in the fall of 2009 and led by the Institute for Healthcare Improvement (IHI) and funded in part by The Commonwealth Fund, and H2H (Hospital‐to‐Home), a national quality campaign led by the American College of Cardiology and IHI with support from several professional associations and partners. Together, these serve more than 1000 hospitals nationally. The STAAR initiative is a state‐based collaborative that partnered with more than 500 community groups across 4 states selected for their diverse readmissions performance and support for improvement efforts, including Massachusetts, Michigan, and Washington. After July 2011, efforts expanded to include Ohio. STAAR was designed to work with leadership at the state level including representatives from hospital associations, government payers, private payers, state governments, provider organizations, employers, and business groups. H2H, in contrast, employs a national quality campaign model and focuses on the care of patients with heart failure or acute myocardial infarction. H2H hospitals are encouraged to participate in a set of H2H Challenges, which provide hospitals with recommended strategies and tools for reducing unnecessary readmission and improve transitions of care. Each Challenge project is 6 to 8 months and consists of success metrics, 3 webinars, and 1 tool kit.

Although previous research has examined strategies used by hospitals enrolled in H2H,¹⁰ we know little about strategies used by STAAR hospitals within 1 year of enrollment. Such data across these 2 prominent initiatives at baseline can provide a snapshot of strategies used prior to the major efforts to reduce readmission rates nationally and identify gaps in practice to target for improvement. Furthermore, given the distinct designs of STAAR (a state‐based learning collaborative in selected regions) and H2H (an open, national campaign), future evaluations will likely compare the effectiveness of these alternative approaches for reducing readmissions.

Accordingly, we sought to describe and compare the reported use of recommended strategies to reduce readmission strategies among STAAR and H2H hospitals. Our findings provide a contemporary view of a large set of hospitals working to reduce readmissions. Findings from this study can provide insight into the strategies used by hospitals that enrolled in a state‐based learning collaborative versus a national campaign as well as document a baseline against which future improvements can be measured and evaluated.

METHODS

RESULTS

DISCUSSION

We found that many hospitals enrolled in the STAAR or the H2H initiative were not implementing strategies commonly recommended to reduce readmission in 2010 to 2011, indicating substantial opportunities for improvement. The gaps were apparent among both the STAAR and the H2H hospitals. Previous literature has shown that discharged patients often do not have timely posthospitalization follow‐up visits, and that discharge summaries are infrequently completed prior to the follow‐up visit.[4, 19, 31] Studies have also demonstrated weaknesses in the medication reconciliation process[32] and overall communication between hospital‐based and primary care physicians.[33, 34] Our survey adds to this existing literature by employing a more comprehensive survey of hospital strategies and reporting results for a larger, national sample of hospitals.

Encouraging the use of strategies recommended by quality initiatives is difficult for several reasons. First, the evidence base for their effectiveness is not yet solid, making it difficult for institutions to prioritize and select interventions and to foster enthusiasm for change. Second, the organizational challenges of these interventions are often substantial, requiring coordination across disciplines, departments, and settings (hospital, home, nursing facility). Third, some literature suggests[3] that multipronged strategies may be most effective, increasing the complexity of readmission reduction activities. Last, important financial barriers must be overcome, including the cost of interventions as well as lost revenue from reduced readmissions. Input from hospitalists who are often critical links among inpatient and outpatient care and between patients and their families is strongly needed to ensure hospitals focus on what strategies are most effective for successful transitions from hospital to home.

The prevalence of several strategies differed between STAAR and H2H hospitals; however, these differences were largely attenuated by geographic region. The finding that significant differences among hospitals in strategies was explained in large part by geographical region is consistent with previous research that has documented substantial regional differences in many kinds of practice patterns[35, 36, 37] as well as geographic differences in readmission rates.[38, 39, 40] The results suggest regionally focused initiatives may be most effective in tailoring interventions to practice needs and norms within specific areas.

Among the strategies that differed significantly between the hospitals in STAAR compared with H2H, the variation may be attributable in part to the focus of the initiatives themselves. For instance, 1 strategy that was significantly more prevalent among H2H compared with STAAR hospitals is central to the quality of care for patients with heart failure and acute myocardial infarction, the focus of H2H: referral patterns to cardiac rehabilitation services after discharge. H2H hospitals may have been particularly attuned to this practice, as H2H focused on cardiovascular‐related readmissions, whereas STAAR focused on all readmissions.

The study has several limitations. First, data were self‐reported, and we did not have the resources to verify these reports with onsite evaluations. Nevertheless, the methods for obtaining the data were the same for H2H and STAAR hospitals, and therefore measurement errors are unlikely to have varied systematically between the 2 groups of hospitals. Second, a single respondent at each hospital completed the survey; however, we did instruct respondents to attain information from a broad range of relevant staff to reflect a more comprehensive perspective in the survey. Third, the sample size of STAAR hospitals was modest and therefore may have lacked statistical power to detect important differences; however, we did include all hospitals that had enrolled in STAAR by the study date. Fourth, hospitals that enrolled in STAAR and H2H initiatives represent a selected group, and results may differ among nonenrolled hospitals. Last, we have data on strategies used during the 2010 to 2011 time frame and therefore cannot evaluate the impact of the quality initiatives from these baseline data. Studies that examine the associations between changes in the use of strategies and subsequent changes in readmission rates would be valuable. Nevertheless, this study establishes a baseline against which future progress can be evaluated.

In sum, we found that many STAAR and H2H hospitals were not implementing many of the recommended strategies for reducing readmissions as of 2010 to 2011, suggesting continued opportunities for improvement. Hospitalists will have opportunities to play leadership roles as hospitals look for meaningful ways to reduce readmissions. At the same time, although hospitalists have a key role in implementing hospital‐based programs, much of the care transitions work must also engage teams across the continuum of care. Furthermore, priority should be given to augmenting the evidence base about which strategies are most effective in reducing readmissions, as this evidence is currently underdeveloped.

The Centers for Medicare & Medicaid Services (CMS) publicly reports hospital‐specific, 30‐day risk‐standardized mortality and readmission rates for Medicare fee‐for‐service patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.1 These measures are intended to reflect hospital performance on quality of care provided to patients during and after hospitalization.2, 3

Quality‐of‐care measures for a given disease are often assumed to reflect the quality of care for that particular condition. However, studies have found limited association between condition‐specific process measures and either mortality or readmission rates for those conditions.46 Mortality and readmission rates may instead reflect broader hospital‐wide or specialty‐wide structure, culture, and practice. For example, studies have previously found that hospitals differ in mortality or readmission rates according to organizational structure,7 financial structure,8 culture,9, 10 information technology,11 patient volume,1214 academic status,12 and other institution‐wide factors.12 There is now a strong policy push towards developing hospital‐wide (all‐condition) measures, beginning with readmission.15

It is not clear how much of the quality of care for a given condition is attributable to hospital‐wide influences that affect all conditions rather than disease‐specific factors. If readmission or mortality performance for a particular condition reflects, in large part, broader institutional characteristics, then improvement efforts might better be focused on hospital‐wide activities, such as team training or implementing electronic medical records. On the other hand, if the disease‐specific measures reflect quality strictly for those conditions, then improvement efforts would be better focused on disease‐specific care, such as early identification of the relevant patient population or standardizing disease‐specific care. As hospitals work to improve performance across an increasingly wide variety of conditions, it is becoming more important for hospitals to prioritize and focus their activities effectively and efficiently.

One means of determining the relative contribution of hospital versus disease factors is to explore whether outcome rates are consistent among different conditions cared for in the same hospital. If mortality (or readmission) rates across different conditions are highly correlated, it would suggest that hospital‐wide factors may play a substantive role in outcomes. Some studies have found that mortality for a particular surgical condition is a useful proxy for mortality for other surgical conditions,16, 17 while other studies have found little correlation among mortality rates for various medical conditions.18, 19 It is also possible that correlation varies according to hospital characteristics; for example, smaller or nonteaching hospitals might be more homogenous in their care than larger, less homogeneous institutions. No studies have been performed using publicly reported estimates of risk‐standardized mortality or readmission rates. In this study we use the publicly reported measures of 30‐day mortality and 30‐day readmission for AMI, HF, and pneumonia to examine whether, and to what degree, mortality rates track together within US hospitals, and separately, to what degree readmission rates track together within US hospitals.

METHODS

RESULTS

The mortality cohort included 4559 hospitals, and the readmission cohort included 4468 hospitals. The majority of hospitals was small, nonteaching, and did not have advanced cardiac capabilities such as cardiac surgery or cardiac catheterization (Table 1).

For mortality measures, the smallest median number of cases per hospital was for AMI (48; interquartile range [IQR], 13,171), and the greatest number was for pneumonia (178; IQR, 87, 336). The same pattern held for readmission measures (AMI median 33; IQR; 9, 150; pneumonia median 191; IQR, 95, 352.5). With respect to mortality measures, AMI had the highest rate and HF the lowest rate; however, for readmission measures, HF had the highest rate and pneumonia the lowest rate (Table 2).

Every mortality measure was significantly correlated with every other mortality measure (range of correlation coefficients, 0.270.41, P < 0.0001 for all 3 correlations). For example, the correlation between risk‐standardized mortality rates (RSMR) for HF and pneumonia was 0.41. Similarly, every readmission measure was significantly correlated with every other readmission measure (range of correlation coefficients, 0.320.47; P < 0.0001 for all 3 correlations). Overall, the lowest correlation was between risk‐standardized mortality rates for AMI and pneumonia (r = 0.27), and the highest correlation was between risk‐standardized readmission rates (RSRR) for HF and pneumonia (r = 0.47) (Table 3).

Both the factor analysis for the mortality measures and the factor analysis for the readmission measures yielded only one factor with an eigenvalue >1. In each factor analysis, this single common factor kept more than half of the data based on the cumulative eigenvalue (55% for mortality measures and 60% for readmission measures). For the mortality measures, the pattern of RSMR for myocardial infarction (MI), heart failure (HF), and pneumonia (PN) in the factor was high (0.68 for MI, 0.78 for HF, and 0.76 for PN); the same was true of the RSRR in the readmission measures (0.72 for MI, 0.81 for HF, and 0.78 for PN).

For all condition pairs and both outcomes, a third or more of hospitals were in the same quartile of performance for both conditions of the pair (Table 4). Hospitals were more likely to be in the same quartile of performance if they were in the top or bottom quartile than if they were in the middle. Less than 10% of hospitals were in the top quartile for one condition in the mortality or readmission pair and in the bottom quartile for the other condition in the pair. Kappa scores for same quartile of performance between pairs of outcomes ranged from 0.16 to 0.27, and were highest for HF and pneumonia for both mortality and readmission rates.

In subgroup analyses, the highest mortality correlation was between HF and pneumonia in hospitals with more than 600 beds (r = 0.51, P = 0.0009), and the highest readmission correlation was between AMI and HF in hospitals with more than 600 beds (r = 0.67, P < 0.0001). Across both measures and all 3 condition pairs, correlations between conditions increased with increasing hospital bed size, presence of cardiac surgery capability, and increasing population of the hospital's Census Bureau statistical area. Furthermore, for most measures and condition pairs, correlations between conditions were highest in not‐for‐profit hospitals, hospitals belonging to the Council of Teaching Hospitals, and non‐safety net hospitals (Table 3).

For all condition pairs, the correlation between readmission rates was significantly higher than the correlation between mortality rates (P < 0.01). In subgroup analyses, readmission correlations were also significantly higher than mortality correlations for all pairs of conditions among moderate‐sized hospitals, among nonprofit hospitals, among teaching hospitals that did not belong to the Council of Teaching Hospitals, and among non‐safety net hospitals (Table 5).

DISCUSSION

In this study, we found that risk‐standardized mortality rates for 3 common medical conditions were moderately correlated within institutions, as were risk‐standardized readmission rates. Readmission rates were more strongly correlated than mortality rates, and all rates tracked closest together in large, urban, and/or teaching hospitals. Very few hospitals were in the top quartile of performance for one condition and in the bottom quartile for a different condition.

Our findings are consistent with the hypothesis that 30‐day risk‐standardized mortality and 30‐day risk‐standardized readmission rates, in part, capture broad aspects of hospital quality that transcend condition‐specific activities. In this study, readmission rates tracked better together than mortality rates for every pair of conditions, suggesting that there may be a greater contribution of hospital‐wide environment, structure, and processes to readmission rates than to mortality rates. This difference is plausible because services specific to readmission, such as discharge planning, care coordination, medication reconciliation, and discharge communication with patients and outpatient clinicians, are typically hospital‐wide processes.

Our study differs from earlier studies of medical conditions in that the correlations we found were higher.18, 19 There are several possible explanations for this difference. First, during the intervening 1525 years since those studies were performed, care for these conditions has evolved substantially, such that there are now more standardized protocols available for all 3 of these diseases. Hospitals that are sufficiently organized or acculturated to systematically implement care protocols may have the infrastructure or culture to do so for all conditions, increasing correlation of performance among conditions. In addition, there are now more technologies and systems available that span care for multiple conditions, such as electronic medical records and quality committees, than were available in previous generations. Second, one of these studies utilized less robust risk‐adjustment,18 and neither used the same methodology of risk standardization. Nonetheless, it is interesting to note that Rosenthal and colleagues identified the same increase in correlation with higher volumes than we did.19 Studies investigating mortality correlations among surgical procedures, on the other hand, have generally found higher correlations than we found in these medical conditions.16, 17

Accountable care organizations will be assessed using an all‐condition readmission measure,31 several states track all‐condition readmission rates,3234 and several countries measure all‐condition mortality.35 An all‐condition measure for quality assessment first requires that there be a hospital‐wide quality signal above and beyond disease‐specific care. This study suggests that a moderate signal exists for readmission and, to a slightly lesser extent, for mortality, across 3 common conditions. There are other considerations, however, in developing all‐condition measures. There must be adequate risk adjustment for the wide variety of conditions that are included, and there must be a means of accounting for the variation in types of conditions and procedures cared for by different hospitals. Our study does not address these challenges, which have been described to be substantial for mortality measures.35

We were surprised by the finding that risk‐standardized rates correlated more strongly within larger institutions than smaller ones, because one might assume that care within smaller hospitals might be more homogenous. It may be easier, however, to detect a quality signal in hospitals with higher volumes of patients for all 3 conditions, because estimates for these hospitals are more precise. Consequently, we have greater confidence in results for larger volumes, and suspect a similar quality signal may be present but more difficult to detect statistically in smaller hospitals. Overall correlations were higher when we restricted the sample to hospitals with at least 25 cases, as is used for public reporting. It is also possible that the finding is real given that large‐volume hospitals have been demonstrated to provide better care for these conditions and are more likely to adopt systems of care that affect multiple conditions, such as electronic medical records.14, 36

The kappa scores comparing quartile of national performance for pairs of conditions were only in the fair range. There are several possible explanations for this fact: 1) outcomes for these 3 conditions are not measuring the same constructs; 2) they are all measuring the same construct, but they are unreliable in doing so; and/or 3) hospitals have similar latent quality for all 3 conditions, but the national quality of performance differs by condition, yielding variable relative performance per hospital for each condition. Based solely on our findings, we cannot distinguish which, if any, of these explanations may be true.31

Our study has several limitations. First, all 3 conditions currently publicly reported by CMS are medical diagnoses, although AMI patients may be cared for in distinct cardiology units and often undergo procedures; therefore, we cannot determine the degree to which correlations reflect hospital‐wide quality versus medicine‐wide quality. An institution may have a weak medicine department but a strong surgical department or vice versa. Second, it is possible that the correlations among conditions for readmission and among conditions for mortality are attributable to patient characteristics that are not adequately adjusted for in the risk‐adjustment model, such as socioeconomic factors, or to hospital characteristics not related to quality, such as coding practices or inter‐hospital transfer rates. For this to be true, these unmeasured characteristics would have to be consistent across different conditions within each hospital and have a consistent influence on outcomes. Third, it is possible that public reporting may have prompted disease‐specific focus on these conditions. We do not have data from non‐publicly reported conditions to test this hypothesis. Fourth, there are many small‐volume hospitals in this study; their estimates for readmission and mortality are less reliable than for large‐volume hospitals, potentially limiting our ability to detect correlations in this group of hospitals.

This study lends credence to the hypothesis that 30‐day risk‐standardized mortality and readmission rates for individual conditions may reflect aspects of hospital‐wide quality or at least medicine‐wide quality, although the correlations are not large enough to conclude that hospital‐wide factors play a dominant role, and there are other possible explanations for the correlations. Further work is warranted to better understand the causes of the correlations, and to better specify the nature of hospital factors that contribute to correlations among outcomes.

The Centers for Medicare & Medicaid Services (CMS) publicly reports hospital‐specific, 30‐day risk‐standardized mortality and readmission rates for Medicare fee‐for‐service patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.1 These measures are intended to reflect hospital performance on quality of care provided to patients during and after hospitalization.2, 3

Quality‐of‐care measures for a given disease are often assumed to reflect the quality of care for that particular condition. However, studies have found limited association between condition‐specific process measures and either mortality or readmission rates for those conditions.46 Mortality and readmission rates may instead reflect broader hospital‐wide or specialty‐wide structure, culture, and practice. For example, studies have previously found that hospitals differ in mortality or readmission rates according to organizational structure,7 financial structure,8 culture,9, 10 information technology,11 patient volume,1214 academic status,12 and other institution‐wide factors.12 There is now a strong policy push towards developing hospital‐wide (all‐condition) measures, beginning with readmission.15

It is not clear how much of the quality of care for a given condition is attributable to hospital‐wide influences that affect all conditions rather than disease‐specific factors. If readmission or mortality performance for a particular condition reflects, in large part, broader institutional characteristics, then improvement efforts might better be focused on hospital‐wide activities, such as team training or implementing electronic medical records. On the other hand, if the disease‐specific measures reflect quality strictly for those conditions, then improvement efforts would be better focused on disease‐specific care, such as early identification of the relevant patient population or standardizing disease‐specific care. As hospitals work to improve performance across an increasingly wide variety of conditions, it is becoming more important for hospitals to prioritize and focus their activities effectively and efficiently.

One means of determining the relative contribution of hospital versus disease factors is to explore whether outcome rates are consistent among different conditions cared for in the same hospital. If mortality (or readmission) rates across different conditions are highly correlated, it would suggest that hospital‐wide factors may play a substantive role in outcomes. Some studies have found that mortality for a particular surgical condition is a useful proxy for mortality for other surgical conditions,16, 17 while other studies have found little correlation among mortality rates for various medical conditions.18, 19 It is also possible that correlation varies according to hospital characteristics; for example, smaller or nonteaching hospitals might be more homogenous in their care than larger, less homogeneous institutions. No studies have been performed using publicly reported estimates of risk‐standardized mortality or readmission rates. In this study we use the publicly reported measures of 30‐day mortality and 30‐day readmission for AMI, HF, and pneumonia to examine whether, and to what degree, mortality rates track together within US hospitals, and separately, to what degree readmission rates track together within US hospitals.

METHODS

RESULTS

The mortality cohort included 4559 hospitals, and the readmission cohort included 4468 hospitals. The majority of hospitals was small, nonteaching, and did not have advanced cardiac capabilities such as cardiac surgery or cardiac catheterization (Table 1).

For mortality measures, the smallest median number of cases per hospital was for AMI (48; interquartile range [IQR], 13,171), and the greatest number was for pneumonia (178; IQR, 87, 336). The same pattern held for readmission measures (AMI median 33; IQR; 9, 150; pneumonia median 191; IQR, 95, 352.5). With respect to mortality measures, AMI had the highest rate and HF the lowest rate; however, for readmission measures, HF had the highest rate and pneumonia the lowest rate (Table 2).

Every mortality measure was significantly correlated with every other mortality measure (range of correlation coefficients, 0.270.41, P < 0.0001 for all 3 correlations). For example, the correlation between risk‐standardized mortality rates (RSMR) for HF and pneumonia was 0.41. Similarly, every readmission measure was significantly correlated with every other readmission measure (range of correlation coefficients, 0.320.47; P < 0.0001 for all 3 correlations). Overall, the lowest correlation was between risk‐standardized mortality rates for AMI and pneumonia (r = 0.27), and the highest correlation was between risk‐standardized readmission rates (RSRR) for HF and pneumonia (r = 0.47) (Table 3).

Both the factor analysis for the mortality measures and the factor analysis for the readmission measures yielded only one factor with an eigenvalue >1. In each factor analysis, this single common factor kept more than half of the data based on the cumulative eigenvalue (55% for mortality measures and 60% for readmission measures). For the mortality measures, the pattern of RSMR for myocardial infarction (MI), heart failure (HF), and pneumonia (PN) in the factor was high (0.68 for MI, 0.78 for HF, and 0.76 for PN); the same was true of the RSRR in the readmission measures (0.72 for MI, 0.81 for HF, and 0.78 for PN).

For all condition pairs and both outcomes, a third or more of hospitals were in the same quartile of performance for both conditions of the pair (Table 4). Hospitals were more likely to be in the same quartile of performance if they were in the top or bottom quartile than if they were in the middle. Less than 10% of hospitals were in the top quartile for one condition in the mortality or readmission pair and in the bottom quartile for the other condition in the pair. Kappa scores for same quartile of performance between pairs of outcomes ranged from 0.16 to 0.27, and were highest for HF and pneumonia for both mortality and readmission rates.

In subgroup analyses, the highest mortality correlation was between HF and pneumonia in hospitals with more than 600 beds (r = 0.51, P = 0.0009), and the highest readmission correlation was between AMI and HF in hospitals with more than 600 beds (r = 0.67, P < 0.0001). Across both measures and all 3 condition pairs, correlations between conditions increased with increasing hospital bed size, presence of cardiac surgery capability, and increasing population of the hospital's Census Bureau statistical area. Furthermore, for most measures and condition pairs, correlations between conditions were highest in not‐for‐profit hospitals, hospitals belonging to the Council of Teaching Hospitals, and non‐safety net hospitals (Table 3).

For all condition pairs, the correlation between readmission rates was significantly higher than the correlation between mortality rates (P < 0.01). In subgroup analyses, readmission correlations were also significantly higher than mortality correlations for all pairs of conditions among moderate‐sized hospitals, among nonprofit hospitals, among teaching hospitals that did not belong to the Council of Teaching Hospitals, and among non‐safety net hospitals (Table 5).

DISCUSSION

In this study, we found that risk‐standardized mortality rates for 3 common medical conditions were moderately correlated within institutions, as were risk‐standardized readmission rates. Readmission rates were more strongly correlated than mortality rates, and all rates tracked closest together in large, urban, and/or teaching hospitals. Very few hospitals were in the top quartile of performance for one condition and in the bottom quartile for a different condition.

Our findings are consistent with the hypothesis that 30‐day risk‐standardized mortality and 30‐day risk‐standardized readmission rates, in part, capture broad aspects of hospital quality that transcend condition‐specific activities. In this study, readmission rates tracked better together than mortality rates for every pair of conditions, suggesting that there may be a greater contribution of hospital‐wide environment, structure, and processes to readmission rates than to mortality rates. This difference is plausible because services specific to readmission, such as discharge planning, care coordination, medication reconciliation, and discharge communication with patients and outpatient clinicians, are typically hospital‐wide processes.

Our study differs from earlier studies of medical conditions in that the correlations we found were higher.18, 19 There are several possible explanations for this difference. First, during the intervening 1525 years since those studies were performed, care for these conditions has evolved substantially, such that there are now more standardized protocols available for all 3 of these diseases. Hospitals that are sufficiently organized or acculturated to systematically implement care protocols may have the infrastructure or culture to do so for all conditions, increasing correlation of performance among conditions. In addition, there are now more technologies and systems available that span care for multiple conditions, such as electronic medical records and quality committees, than were available in previous generations. Second, one of these studies utilized less robust risk‐adjustment,18 and neither used the same methodology of risk standardization. Nonetheless, it is interesting to note that Rosenthal and colleagues identified the same increase in correlation with higher volumes than we did.19 Studies investigating mortality correlations among surgical procedures, on the other hand, have generally found higher correlations than we found in these medical conditions.16, 17

Accountable care organizations will be assessed using an all‐condition readmission measure,31 several states track all‐condition readmission rates,3234 and several countries measure all‐condition mortality.35 An all‐condition measure for quality assessment first requires that there be a hospital‐wide quality signal above and beyond disease‐specific care. This study suggests that a moderate signal exists for readmission and, to a slightly lesser extent, for mortality, across 3 common conditions. There are other considerations, however, in developing all‐condition measures. There must be adequate risk adjustment for the wide variety of conditions that are included, and there must be a means of accounting for the variation in types of conditions and procedures cared for by different hospitals. Our study does not address these challenges, which have been described to be substantial for mortality measures.35

We were surprised by the finding that risk‐standardized rates correlated more strongly within larger institutions than smaller ones, because one might assume that care within smaller hospitals might be more homogenous. It may be easier, however, to detect a quality signal in hospitals with higher volumes of patients for all 3 conditions, because estimates for these hospitals are more precise. Consequently, we have greater confidence in results for larger volumes, and suspect a similar quality signal may be present but more difficult to detect statistically in smaller hospitals. Overall correlations were higher when we restricted the sample to hospitals with at least 25 cases, as is used for public reporting. It is also possible that the finding is real given that large‐volume hospitals have been demonstrated to provide better care for these conditions and are more likely to adopt systems of care that affect multiple conditions, such as electronic medical records.14, 36

The kappa scores comparing quartile of national performance for pairs of conditions were only in the fair range. There are several possible explanations for this fact: 1) outcomes for these 3 conditions are not measuring the same constructs; 2) they are all measuring the same construct, but they are unreliable in doing so; and/or 3) hospitals have similar latent quality for all 3 conditions, but the national quality of performance differs by condition, yielding variable relative performance per hospital for each condition. Based solely on our findings, we cannot distinguish which, if any, of these explanations may be true.31

Our study has several limitations. First, all 3 conditions currently publicly reported by CMS are medical diagnoses, although AMI patients may be cared for in distinct cardiology units and often undergo procedures; therefore, we cannot determine the degree to which correlations reflect hospital‐wide quality versus medicine‐wide quality. An institution may have a weak medicine department but a strong surgical department or vice versa. Second, it is possible that the correlations among conditions for readmission and among conditions for mortality are attributable to patient characteristics that are not adequately adjusted for in the risk‐adjustment model, such as socioeconomic factors, or to hospital characteristics not related to quality, such as coding practices or inter‐hospital transfer rates. For this to be true, these unmeasured characteristics would have to be consistent across different conditions within each hospital and have a consistent influence on outcomes. Third, it is possible that public reporting may have prompted disease‐specific focus on these conditions. We do not have data from non‐publicly reported conditions to test this hypothesis. Fourth, there are many small‐volume hospitals in this study; their estimates for readmission and mortality are less reliable than for large‐volume hospitals, potentially limiting our ability to detect correlations in this group of hospitals.

This study lends credence to the hypothesis that 30‐day risk‐standardized mortality and readmission rates for individual conditions may reflect aspects of hospital‐wide quality or at least medicine‐wide quality, although the correlations are not large enough to conclude that hospital‐wide factors play a dominant role, and there are other possible explanations for the correlations. Further work is warranted to better understand the causes of the correlations, and to better specify the nature of hospital factors that contribute to correlations among outcomes.

The Centers for Medicare & Medicaid Services (CMS) publicly reports hospital‐specific, 30‐day risk‐standardized mortality and readmission rates for Medicare fee‐for‐service patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.1 These measures are intended to reflect hospital performance on quality of care provided to patients during and after hospitalization.2, 3

Quality‐of‐care measures for a given disease are often assumed to reflect the quality of care for that particular condition. However, studies have found limited association between condition‐specific process measures and either mortality or readmission rates for those conditions.46 Mortality and readmission rates may instead reflect broader hospital‐wide or specialty‐wide structure, culture, and practice. For example, studies have previously found that hospitals differ in mortality or readmission rates according to organizational structure,7 financial structure,8 culture,9, 10 information technology,11 patient volume,1214 academic status,12 and other institution‐wide factors.12 There is now a strong policy push towards developing hospital‐wide (all‐condition) measures, beginning with readmission.15

It is not clear how much of the quality of care for a given condition is attributable to hospital‐wide influences that affect all conditions rather than disease‐specific factors. If readmission or mortality performance for a particular condition reflects, in large part, broader institutional characteristics, then improvement efforts might better be focused on hospital‐wide activities, such as team training or implementing electronic medical records. On the other hand, if the disease‐specific measures reflect quality strictly for those conditions, then improvement efforts would be better focused on disease‐specific care, such as early identification of the relevant patient population or standardizing disease‐specific care. As hospitals work to improve performance across an increasingly wide variety of conditions, it is becoming more important for hospitals to prioritize and focus their activities effectively and efficiently.

One means of determining the relative contribution of hospital versus disease factors is to explore whether outcome rates are consistent among different conditions cared for in the same hospital. If mortality (or readmission) rates across different conditions are highly correlated, it would suggest that hospital‐wide factors may play a substantive role in outcomes. Some studies have found that mortality for a particular surgical condition is a useful proxy for mortality for other surgical conditions,16, 17 while other studies have found little correlation among mortality rates for various medical conditions.18, 19 It is also possible that correlation varies according to hospital characteristics; for example, smaller or nonteaching hospitals might be more homogenous in their care than larger, less homogeneous institutions. No studies have been performed using publicly reported estimates of risk‐standardized mortality or readmission rates. In this study we use the publicly reported measures of 30‐day mortality and 30‐day readmission for AMI, HF, and pneumonia to examine whether, and to what degree, mortality rates track together within US hospitals, and separately, to what degree readmission rates track together within US hospitals.

METHODS

RESULTS

The mortality cohort included 4559 hospitals, and the readmission cohort included 4468 hospitals. The majority of hospitals was small, nonteaching, and did not have advanced cardiac capabilities such as cardiac surgery or cardiac catheterization (Table 1).

For mortality measures, the smallest median number of cases per hospital was for AMI (48; interquartile range [IQR], 13,171), and the greatest number was for pneumonia (178; IQR, 87, 336). The same pattern held for readmission measures (AMI median 33; IQR; 9, 150; pneumonia median 191; IQR, 95, 352.5). With respect to mortality measures, AMI had the highest rate and HF the lowest rate; however, for readmission measures, HF had the highest rate and pneumonia the lowest rate (Table 2).

Every mortality measure was significantly correlated with every other mortality measure (range of correlation coefficients, 0.270.41, P < 0.0001 for all 3 correlations). For example, the correlation between risk‐standardized mortality rates (RSMR) for HF and pneumonia was 0.41. Similarly, every readmission measure was significantly correlated with every other readmission measure (range of correlation coefficients, 0.320.47; P < 0.0001 for all 3 correlations). Overall, the lowest correlation was between risk‐standardized mortality rates for AMI and pneumonia (r = 0.27), and the highest correlation was between risk‐standardized readmission rates (RSRR) for HF and pneumonia (r = 0.47) (Table 3).