Mayur M. Desai, PhD, MPH

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).

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.

If you wish to receive credit for this activity, which beginson the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to:

• Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.

• Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

Instructions on Receiving Credit

For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

If you wish to receive credit for this activity, which beginson the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to:

• Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.

• Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

Instructions on Receiving Credit

For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

If you wish to receive credit for this activity, which beginson the next page, please refer to the website: www.blackwellpublishing.com/cme.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this educational activity for a 1 AMA PRA Category 1 Credit. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

Upon completion of this educational activity, participants will be better able to:

• Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.

• Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.

Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:

• Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.

• Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.

• Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.

• Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.

• Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.

• Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.

Instructions on Receiving Credit

For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within the time designated on the title page; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period that is noted on the title page.

Follow these steps to earn credit:

• Log on to www.blackwellpublishing.com/cme.

• Read the target audience, learning objectives, and author disclosures.

• Read the article in print or online format.

• Reflect on the article.

• Access the CME Exam, and choose the best answer to each question.

• Complete the required evaluation component of the activity.

Hospital readmissions are emblematic of the numerous challenges facing the US health care system. Despite high levels of spending, nearly 20% of Medicare beneficiaries are readmitted within 30 days of hospital discharge, many readmissions are considered preventable, and rates vary widely by hospital and region.1 Further, while readmissions have been estimated to cost taxpayers as much as $17 billion annually, the current fee‐for‐service method of paying for the acute care needs of seniors rewards hospitals financially for readmission, not their prevention.2

Pneumonia is the second most common reason for hospitalization among Medicare beneficiaries, accounting for approximately 650,000 admissions annually,3 and has been a focus of national quality‐improvement efforts for more than a decade.4, 5 Despite improvements in key processes of care, rates of readmission within 30 days of discharge following a hospitalization for pneumonia have been reported to vary from 10% to 24%.68 Among several factors, readmissions are believed to be influenced by the quality of both inpatient and outpatient care, and by care‐coordination activities occurring in the transition from inpatient to outpatient status.912

Public reporting of hospital performance is considered a key strategy for improving quality, reducing costs, and increasing the value of hospital care, both in the US and worldwide.13 In 2009, the Centers for Medicare & Medicaid Services (CMS) expanded its reporting initiatives by adding risk‐adjusted hospital readmission rates for acute myocardial infarction, heart failure, and pneumonia to the Hospital Compare website.14, 15 Readmission rates are an attractive focus for public reporting for several reasons. First, in contrast to most process‐based measures of quality (eg, whether a patient with pneumonia received a particular antibiotic), a readmission is an adverse outcome that matters to patients and families.16 Second, unlike process measures whose assessment requires detailed review of medical records, readmissions can be easily determined from standard hospital claims. Finally, readmissions are costly, and their prevention could yield substantial savings to society.

A necessary prerequisite for public reporting of readmission is a validated, risk‐adjusted measure that can be used to track performance over time and can facilitate comparisons across institutions. Toward this end, we describe the development, validation, and results of a National Quality Forum‐approved and CMS‐adopted model to estimate hospital‐specific, risk‐standardized, 30‐day readmission rates for Medicare patients hospitalized with pneumonia.17

METHODS

RESULTS

Model Derivation and Performance

After exclusions were applied, the 2006 sample included 453,251 pneumonia hospitalizations (Figure 1). The development sample consisted of 226,545 hospitalizations at 4675 hospitals, with an overall unadjusted 30‐day readmission rate of 17.4%. In 11,694 index cases (5.2%), the patient died within 30 days without being readmitted. Median readmission rate was 16.3%, 25th and 75th percentile rates were 11.1% and 21.3%, and at the 10th and 90th percentile, hospital readmission rates ranged from 4.6% to 26.7% (Figure 2).

Figure 1

Figure 2

The claims model included 39 variables (age, sex, and 37 clinical variables) (Table 1). The mean age of the cohort was 80.0 years, with 55.5% women and 11.1% nonwhite patients. Mean observed readmission rate in the development sample ranged from 9% in the lowest decile of predicted pneumonia readmission rates to 32% in the highest predicted decile, a range of 23%. The AUC was 0.63. For comparison, a model with only age and sex had an AUC of 0.51, and a model with all candidate variables had an AUC equal to 0.63 (Table 2).

Table 2.Readmission Model Performance of Administrative Claims Models

		Calibration (0, 1)*	Discrimination		Residuals Lack of Fit (Pearson Residual Fall %)				Model 2 (No. of Covariates)
			Predictive Ability (Lowest Decile, Highest Decile)	AUC	(<2)	(2, 0)	(0, 2)	(2+)
NOTE: Over‐fitting indices (0, 1) provide evidence of over‐fitting and require several steps to calculate. Let b denote the estimated vector of regression coefficients. Predicted Probabilities (p) = 1/(1+exp{Xb}), and Z = Xb (eg, the linear predictor that is a scalar value for everyone). A new logistic regression model that includes only an intercept and a slope by regressing the logits on Z is fitted in the validation sample; eg, Logit(P(Y = 1|Z)) = 0 + 1Z. Estimated values of 0 far from 0 and estimated values of 1 far from 1 provide evidence of over‐fitting. Abbreviations: AUC, area under the receiver operating curve. * Max‐rescaled R‐square. Observed rates. Wald chi‐square.
Development sample
2006	(1st half) N = 226,545	(0, 1)	(0.09, 0.32)	0.63	0	82.62	7.39	9.99	6,843 (40)
Validation sample
2006	(2nd half) N = 226,706	(0.002, 0.997)	(0.09, 0.31)	0.63	0	82.55	7.45	9.99	6,870 (40)
2005	N = 536,015	(0.035, 1.008)	(0.08, 0.31)	0.63	0	82.67	7.31	10.03	16,241 (40)

Hospital Risk‐Standardized Readmission Rates

Risk‐standardized readmission rates varied across hospitals (Figure 3). Median risk‐standardized readmission rate was 17.3%, and the 25th and 75th percentiles were 16.9% and 17.9%, respectively. The 5th percentile was 16.0% and the 95th percentile was 19.1%. Odds of readmission for a hospital one standard deviation above average was 1.4 times that of a hospital one standard deviation below average.

Figure 3

Medical Record Validation

After exclusions, the medical record sample taken from the National Pneumonia Project included 47,429 cases, with an unadjusted 30‐day readmission rate of 17.0%. The final medical record risk‐adjustment model included a total of 35 variables, whose prevalence and association with readmission risk varied modestly (Table 3). Performance of the medical record and administrative models was similar (areas under the ROC curve 0.59 and 0.63, respectively) (Table 4). Additionally, in the administrative model, predicted readmission rates ranged from 8% in the lowest predicted decile to 30% in the highest predicted decile, while in the medical record model, the corresponding rates varied from 10% to 26%.

Table 3.Regression Model Results from Medical Record Sample

Variable	Percent	Estimate	Standard Error	Odds Ratio	95% CI
NOTE: Between‐state variance = 0.024; standard error = 0.00. Abbreviations: BP, blood pressure; BUN, blood urea nitrogen; CI, confidence interval; SD, standard deviation; WBC, white blood cell count.
Age 65, mean (SD)	15.24 (7.87)	0.003	0.002	0.997	0.993	1.000
Male	46.18	0.122	0.025	1.130	1.075	1.188
Nursing home resident	17.71	0.035	0.037	1.036	0.963	1.114
Neoplastic disease	6.80	0.130	0.049	1.139	1.034	1.254
Liver disease	1.04	0.089	0.123	0.915	0.719	1.164
History of heart failure	28.98	0.234	0.029	1.264	1.194	1.339
History of renal disease	8.51	0.188	0.047	1.206	1.100	1.323
Altered mental status	17.95	0.009	0.034	1.009	0.944	1.080
Pleural effusion	21.20	0.165	0.030	1.179	1.111	1.251
BUN 30 mg/dl	23.28	0.160	0.033	1.174	1.100	1.252
BUN missing	14.56	0.101	0.185	0.904	0.630	1.298
Systolic BP <90 mmHg	2.95	0.068	0.070	1.070	0.932	1.228
Systolic BP missing	11.21	0.149	0.425	1.160	0.504	2.669
Pulse 125/min	7.73	0.036	0.047	1.036	0.945	1.137
Pulse missing	11.22	0.210	0.405	1.234	0.558	2.729
Respiratory rate 30/min	16.38	0.079	0.034	1.082	1.012	1.157
Respiratory rate missing	11.39	0.204	0.240	1.226	0.765	1.964
Sodium <130 mmol/L	4.82	0.136	0.057	1.145	1.025	1.280
Sodium missing	14.39	0.049	0.143	1.050	0.793	1.391
Glucose 250 mg/dl	5.19	0.005	0.057	0.995	0.889	1.114
Glucose missing	15.44	0.156	0.105	0.855	0.696	1.051
Hematocrit <30%	7.77	0.270	0.044	1.310	1.202	1.428
Hematocrit missing	13.62	0.071	0.135	0.932	0.715	1.215
Creatinine 2.5 mg/dL	4.68	0.109	0.062	1.115	0.989	1.258
Creatinine missing	14.63	0.200	0.167	1.221	0.880	1.695
WBC 6‐12 b/L	38.04	0.021	0.049	0.979	0.889	1.079
WBC >12 b/L	41.45	0.068	0.049	0.934	0.848	1.029
WBC missing	12.85	0.167	0.162	1.181	0.860	1.623
Immunosuppressive therapy	15.01	0.347	0.035	1.415	1.321	1.516
Chronic lung disease	42.16	0.137	0.028	1.147	1.086	1.211
Coronary artery disease	39.57	0.150	0.028	1.162	1.100	1.227
Diabetes mellitus	20.90	0.137	0.033	1.147	1.076	1.223
Alcohol/drug abuse	3.40	0.099	0.071	0.906	0.788	1.041
Dementia/Alzheimer's disease	16.38	0.125	0.038	1.133	1.052	1.222
Splenectomy	0.44	0.016	0.186	1.016	0.706	1.463

Table 4.Model Performance of Medical Record Model

Model	Calibration (0, 1)*	Discrimination		Residuals Lack of Fit (Pearson Residual Fall %)				Model 2 (No. of Covariates)
		Predictive Ability (Lowest Decile, Highest Decile)	AUC	(<2)	(2, 0)	(0, 2)	(2+)
Abbreviations: AUC, area under the receiver operating curve. * Max‐rescaled R‐square. Observed rates. Wald chi‐square.
Medical Record Model Development Sample (NP)
N = 47,429 No. of 30‐day readmissions = 8,042	(1, 0)	(0.10, 0.26)	0.59	0	83.04	5.28	11.68	710 (35)
Linked Administrative Model Validation Sample
N = 47,429 No. of 30‐day readmissions = 8,042	(1, 0)	(0.08, 0.30)	0.63	0	83.04	6.94	10.01	1,414 (40)

The correlation coefficient of the estimated state‐specific standardized readmission rates from the administrative and medical record models was 0.96, and the proportion of the variance explained by the model was 0.92 (Figure 4).

Figure 4

DISCUSSION

We have described the development, validation, and results of a hospital, 30‐day, risk‐standardized readmission model for pneumonia that was created to support current federal transparency initiatives. The model uses administrative claims data from Medicare fee‐for‐service patients and produces results that are comparable to a model based on information obtained through manual abstraction of medical records. We observed an overall 30‐day readmission rate of 17%, and our analyses revealed substantial variation across US hospitals, suggesting that improvement by lower performing institutions is an achievable goal.

Because more than one in six pneumonia patients are rehospitalized shortly after discharge, and because pneumonia hospitalizations represent an enormous expense to the Medicare program, prevention of readmissions is now widely recognized to offer a substantial opportunity to improve patient outcomes while simultaneously lowering health care costs. Accordingly, promotion of strategies to reduce readmission rates has become a key priority for payers and quality‐improvement organizations. These range from policy‐level attempts to stimulate change, such as publicly reporting hospital readmission rates on government websites, to establishing accreditation standardssuch as the Joint Commission's requirement to accurately reconcile medications, to the creation of quality improvement collaboratives focused on sharing best practices across institutions. Regardless of the approach taken, a valid, risk‐adjusted measure of performance is required to evaluate and track performance over time. The measure we have described meets the National Quality Forum's measure evaluation criteria in that it addresses an important clinical topic for which there appears to be significant opportunities for improvement, the measure is precisely defined and has been subjected to validity and reliability testing, it is risk‐adjusted based on patient clinical factors present at the start of care, is feasible to produce, and is understandable by a broad range of potential users.21 Because hospitalists are the physicians primarily responsible for the care of patients with pneumonia at US hospitals, and because they frequently serve as the physician champions for quality improvement activities related to pneumonia, it is especially important that they maintain a thorough understanding of the measures and methodologies underlying current efforts to measure hospital performance.

Several features of our approach warrant additional comment. First, we deliberately chose to measure all readmission events rather than attempt to discriminate between potentially preventable and nonpreventable readmissions. From the patient perspective, readmission for any reason is a concern, and limiting the measure to pneumonia‐related readmissions could make it susceptible to gaming by hospitals. Moreover, determining whether a readmission is related to a potential quality problem is not straightforward. For example, a patient with pneumonia whose discharge medications were prescribed incorrectly may be readmitted with a hip fracture following an episode of syncope. It would be inappropriate to treat this readmission as unrelated to the care the patient received for pneumonia. Additionally, while our approach does not presume that every readmission is preventable, the goal is to reduce the risk of readmissions generally (not just in narrowly defined subpopulations), and successful interventions to reduce rehospitalization have typically demonstrated reductions in all‐cause readmission.9, 22 Second, deaths that occurred within 30 days of discharge, yet that were not accompanied by a hospital readmission, were not counted as a readmission outcome. While it may seem inappropriate to treat a postdischarge death as a nonevent (rather than censoring or excluding such cases), alternative analytic approaches, such as using a hierarchical survival model, are not currently computationally feasible with large national data sets. Fortunately, only a relatively small proportion of discharges fell into this category (5.2% of index cases in the 2006 development sample died within 30 days of discharge without being readmitted). An alternative approach to handling the competing outcome of death would have been to use a composite outcome of readmission or death. However, we believe that it is important to report the outcomes separately because factors that predict readmission and mortality may differ, and when making comparisons across hospitals it would not be possible to determine whether differences in rate were due to readmission or mortality. Third, while the patient‐level readmission model showed only modest discrimination, we intentionally excluded covariates such as race and socioeconomic status, as well as in‐hospital events and potential complications of care, and whether patients were discharged home or to a skilled nursing facility. While these variables could have improved predictive ability, they may be directly or indirectly related to quality or supply factors that should not be included in a model that seeks to control for patient clinical characteristics. For example, if hospitals with a large share of poor patients have higher readmission rates, then including income in the model will obscure differences that are important to identify. While we believe that the decision to exclude such factors in the model is in the best interest of patients, and supports efforts to reduce health inequality in society more generally, we also recognize that hospitals that care for a disproportionate share of poor patients are likely to require additional resources to overcome these social factors. Fourth, we limited the analysis to patients with a principal diagnosis of pneumonia, and chose not to also include those with a principal diagnosis of sepsis or respiratory failure coupled with a secondary diagnosis of pneumonia. While the broader definition is used by CMS in the National Pneumonia Project, that initiative relied on chart abstraction to differentiate pneumonia present at the time of admission from cases developing as a complication of hospitalization. Additionally, we did not attempt to differentiate between community‐acquired and healthcare‐associated pneumonia, however our approach is consistent with the National Pneumonia Project and Pneumonia Patient Outcomes Research Team.18 Fifth, while our model estimates readmission rates at the hospital level, we recognize that readmissions are influenced by a complex and extensive range of factors. In this context, greater cooperation between hospitals and other care providers will almost certainly be required in order to achieve dramatic improvement in readmission rates, which in turn will depend upon changes to the way serious illness is paid for. Some options that have recently been described include imposing financial penalties for early readmission, extending the boundaries of case‐based payment beyond hospital discharge, and bundling payments between hospitals and physicians.2325

Our measure has several limitations. First, our models were developed and validated using Medicare data, and the results may not apply to pneumonia patients less than 65 years of age. However, most patients hospitalized with pneumonia in the US are 65 or older. In addition, we were unable to test the model with a Medicare managed care population, because data are not currently available on such patients. Finally, the medical record‐based validation was conducted by state‐level analysis because the sample size was insufficient to carry this out at the hospital level.

In conclusion, more than 17% of Medicare beneficiaries are readmitted within 30 days following discharge after a hospitalization for pneumonia, and rates vary substantially across institutions. The development of a valid measure of hospital performance and public reporting are important first steps towards focusing attention on this problem. Actual improvement will now depend on whether hospitals and partner organizations are successful at identifying and implementing effective methods to prevent readmission.

Hospital readmissions are emblematic of the numerous challenges facing the US health care system. Despite high levels of spending, nearly 20% of Medicare beneficiaries are readmitted within 30 days of hospital discharge, many readmissions are considered preventable, and rates vary widely by hospital and region.1 Further, while readmissions have been estimated to cost taxpayers as much as $17 billion annually, the current fee‐for‐service method of paying for the acute care needs of seniors rewards hospitals financially for readmission, not their prevention.2

Pneumonia is the second most common reason for hospitalization among Medicare beneficiaries, accounting for approximately 650,000 admissions annually,3 and has been a focus of national quality‐improvement efforts for more than a decade.4, 5 Despite improvements in key processes of care, rates of readmission within 30 days of discharge following a hospitalization for pneumonia have been reported to vary from 10% to 24%.68 Among several factors, readmissions are believed to be influenced by the quality of both inpatient and outpatient care, and by care‐coordination activities occurring in the transition from inpatient to outpatient status.912

Public reporting of hospital performance is considered a key strategy for improving quality, reducing costs, and increasing the value of hospital care, both in the US and worldwide.13 In 2009, the Centers for Medicare & Medicaid Services (CMS) expanded its reporting initiatives by adding risk‐adjusted hospital readmission rates for acute myocardial infarction, heart failure, and pneumonia to the Hospital Compare website.14, 15 Readmission rates are an attractive focus for public reporting for several reasons. First, in contrast to most process‐based measures of quality (eg, whether a patient with pneumonia received a particular antibiotic), a readmission is an adverse outcome that matters to patients and families.16 Second, unlike process measures whose assessment requires detailed review of medical records, readmissions can be easily determined from standard hospital claims. Finally, readmissions are costly, and their prevention could yield substantial savings to society.

A necessary prerequisite for public reporting of readmission is a validated, risk‐adjusted measure that can be used to track performance over time and can facilitate comparisons across institutions. Toward this end, we describe the development, validation, and results of a National Quality Forum‐approved and CMS‐adopted model to estimate hospital‐specific, risk‐standardized, 30‐day readmission rates for Medicare patients hospitalized with pneumonia.17

METHODS

RESULTS

Model Derivation and Performance

After exclusions were applied, the 2006 sample included 453,251 pneumonia hospitalizations (Figure 1). The development sample consisted of 226,545 hospitalizations at 4675 hospitals, with an overall unadjusted 30‐day readmission rate of 17.4%. In 11,694 index cases (5.2%), the patient died within 30 days without being readmitted. Median readmission rate was 16.3%, 25th and 75th percentile rates were 11.1% and 21.3%, and at the 10th and 90th percentile, hospital readmission rates ranged from 4.6% to 26.7% (Figure 2).

Figure 1

Figure 2

The claims model included 39 variables (age, sex, and 37 clinical variables) (Table 1). The mean age of the cohort was 80.0 years, with 55.5% women and 11.1% nonwhite patients. Mean observed readmission rate in the development sample ranged from 9% in the lowest decile of predicted pneumonia readmission rates to 32% in the highest predicted decile, a range of 23%. The AUC was 0.63. For comparison, a model with only age and sex had an AUC of 0.51, and a model with all candidate variables had an AUC equal to 0.63 (Table 2).

Table 2.Readmission Model Performance of Administrative Claims Models

		Calibration (0, 1)*	Discrimination		Residuals Lack of Fit (Pearson Residual Fall %)				Model 2 (No. of Covariates)
			Predictive Ability (Lowest Decile, Highest Decile)	AUC	(<2)	(2, 0)	(0, 2)	(2+)
NOTE: Over‐fitting indices (0, 1) provide evidence of over‐fitting and require several steps to calculate. Let b denote the estimated vector of regression coefficients. Predicted Probabilities (p) = 1/(1+exp{Xb}), and Z = Xb (eg, the linear predictor that is a scalar value for everyone). A new logistic regression model that includes only an intercept and a slope by regressing the logits on Z is fitted in the validation sample; eg, Logit(P(Y = 1|Z)) = 0 + 1Z. Estimated values of 0 far from 0 and estimated values of 1 far from 1 provide evidence of over‐fitting. Abbreviations: AUC, area under the receiver operating curve. * Max‐rescaled R‐square. Observed rates. Wald chi‐square.
Development sample
2006	(1st half) N = 226,545	(0, 1)	(0.09, 0.32)	0.63	0	82.62	7.39	9.99	6,843 (40)
Validation sample
2006	(2nd half) N = 226,706	(0.002, 0.997)	(0.09, 0.31)	0.63	0	82.55	7.45	9.99	6,870 (40)
2005	N = 536,015	(0.035, 1.008)	(0.08, 0.31)	0.63	0	82.67	7.31	10.03	16,241 (40)

Hospital Risk‐Standardized Readmission Rates

Risk‐standardized readmission rates varied across hospitals (Figure 3). Median risk‐standardized readmission rate was 17.3%, and the 25th and 75th percentiles were 16.9% and 17.9%, respectively. The 5th percentile was 16.0% and the 95th percentile was 19.1%. Odds of readmission for a hospital one standard deviation above average was 1.4 times that of a hospital one standard deviation below average.

Figure 3

Medical Record Validation

After exclusions, the medical record sample taken from the National Pneumonia Project included 47,429 cases, with an unadjusted 30‐day readmission rate of 17.0%. The final medical record risk‐adjustment model included a total of 35 variables, whose prevalence and association with readmission risk varied modestly (Table 3). Performance of the medical record and administrative models was similar (areas under the ROC curve 0.59 and 0.63, respectively) (Table 4). Additionally, in the administrative model, predicted readmission rates ranged from 8% in the lowest predicted decile to 30% in the highest predicted decile, while in the medical record model, the corresponding rates varied from 10% to 26%.

Table 3.Regression Model Results from Medical Record Sample

Variable	Percent	Estimate	Standard Error	Odds Ratio	95% CI
NOTE: Between‐state variance = 0.024; standard error = 0.00. Abbreviations: BP, blood pressure; BUN, blood urea nitrogen; CI, confidence interval; SD, standard deviation; WBC, white blood cell count.
Age 65, mean (SD)	15.24 (7.87)	0.003	0.002	0.997	0.993	1.000
Male	46.18	0.122	0.025	1.130	1.075	1.188
Nursing home resident	17.71	0.035	0.037	1.036	0.963	1.114
Neoplastic disease	6.80	0.130	0.049	1.139	1.034	1.254
Liver disease	1.04	0.089	0.123	0.915	0.719	1.164
History of heart failure	28.98	0.234	0.029	1.264	1.194	1.339
History of renal disease	8.51	0.188	0.047	1.206	1.100	1.323
Altered mental status	17.95	0.009	0.034	1.009	0.944	1.080
Pleural effusion	21.20	0.165	0.030	1.179	1.111	1.251
BUN 30 mg/dl	23.28	0.160	0.033	1.174	1.100	1.252
BUN missing	14.56	0.101	0.185	0.904	0.630	1.298
Systolic BP <90 mmHg	2.95	0.068	0.070	1.070	0.932	1.228
Systolic BP missing	11.21	0.149	0.425	1.160	0.504	2.669
Pulse 125/min	7.73	0.036	0.047	1.036	0.945	1.137
Pulse missing	11.22	0.210	0.405	1.234	0.558	2.729
Respiratory rate 30/min	16.38	0.079	0.034	1.082	1.012	1.157
Respiratory rate missing	11.39	0.204	0.240	1.226	0.765	1.964
Sodium <130 mmol/L	4.82	0.136	0.057	1.145	1.025	1.280
Sodium missing	14.39	0.049	0.143	1.050	0.793	1.391
Glucose 250 mg/dl	5.19	0.005	0.057	0.995	0.889	1.114
Glucose missing	15.44	0.156	0.105	0.855	0.696	1.051
Hematocrit <30%	7.77	0.270	0.044	1.310	1.202	1.428
Hematocrit missing	13.62	0.071	0.135	0.932	0.715	1.215
Creatinine 2.5 mg/dL	4.68	0.109	0.062	1.115	0.989	1.258
Creatinine missing	14.63	0.200	0.167	1.221	0.880	1.695
WBC 6‐12 b/L	38.04	0.021	0.049	0.979	0.889	1.079
WBC >12 b/L	41.45	0.068	0.049	0.934	0.848	1.029
WBC missing	12.85	0.167	0.162	1.181	0.860	1.623
Immunosuppressive therapy	15.01	0.347	0.035	1.415	1.321	1.516
Chronic lung disease	42.16	0.137	0.028	1.147	1.086	1.211
Coronary artery disease	39.57	0.150	0.028	1.162	1.100	1.227
Diabetes mellitus	20.90	0.137	0.033	1.147	1.076	1.223
Alcohol/drug abuse	3.40	0.099	0.071	0.906	0.788	1.041
Dementia/Alzheimer's disease	16.38	0.125	0.038	1.133	1.052	1.222
Splenectomy	0.44	0.016	0.186	1.016	0.706	1.463

Table 4.Model Performance of Medical Record Model

Model	Calibration (0, 1)*	Discrimination		Residuals Lack of Fit (Pearson Residual Fall %)				Model 2 (No. of Covariates)
		Predictive Ability (Lowest Decile, Highest Decile)	AUC	(<2)	(2, 0)	(0, 2)	(2+)
Abbreviations: AUC, area under the receiver operating curve. * Max‐rescaled R‐square. Observed rates. Wald chi‐square.
Medical Record Model Development Sample (NP)
N = 47,429 No. of 30‐day readmissions = 8,042	(1, 0)	(0.10, 0.26)	0.59	0	83.04	5.28	11.68	710 (35)
Linked Administrative Model Validation Sample
N = 47,429 No. of 30‐day readmissions = 8,042	(1, 0)	(0.08, 0.30)	0.63	0	83.04	6.94	10.01	1,414 (40)

The correlation coefficient of the estimated state‐specific standardized readmission rates from the administrative and medical record models was 0.96, and the proportion of the variance explained by the model was 0.92 (Figure 4).

Figure 4

DISCUSSION

We have described the development, validation, and results of a hospital, 30‐day, risk‐standardized readmission model for pneumonia that was created to support current federal transparency initiatives. The model uses administrative claims data from Medicare fee‐for‐service patients and produces results that are comparable to a model based on information obtained through manual abstraction of medical records. We observed an overall 30‐day readmission rate of 17%, and our analyses revealed substantial variation across US hospitals, suggesting that improvement by lower performing institutions is an achievable goal.

Because more than one in six pneumonia patients are rehospitalized shortly after discharge, and because pneumonia hospitalizations represent an enormous expense to the Medicare program, prevention of readmissions is now widely recognized to offer a substantial opportunity to improve patient outcomes while simultaneously lowering health care costs. Accordingly, promotion of strategies to reduce readmission rates has become a key priority for payers and quality‐improvement organizations. These range from policy‐level attempts to stimulate change, such as publicly reporting hospital readmission rates on government websites, to establishing accreditation standardssuch as the Joint Commission's requirement to accurately reconcile medications, to the creation of quality improvement collaboratives focused on sharing best practices across institutions. Regardless of the approach taken, a valid, risk‐adjusted measure of performance is required to evaluate and track performance over time. The measure we have described meets the National Quality Forum's measure evaluation criteria in that it addresses an important clinical topic for which there appears to be significant opportunities for improvement, the measure is precisely defined and has been subjected to validity and reliability testing, it is risk‐adjusted based on patient clinical factors present at the start of care, is feasible to produce, and is understandable by a broad range of potential users.21 Because hospitalists are the physicians primarily responsible for the care of patients with pneumonia at US hospitals, and because they frequently serve as the physician champions for quality improvement activities related to pneumonia, it is especially important that they maintain a thorough understanding of the measures and methodologies underlying current efforts to measure hospital performance.