Leora I. Horwitz, MD, MHS

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.

Hospital readmission is a common, costly, and often preventable occurrence in the United States. Among Medicare beneficiaries, 1 out of 5 patients is readmitted within 30 days, and the cost of unplanned readmissions exceeded $17 billion in 2007.1 As a result, the Centers for Medicare and Medicaid Services (CMS) and others have called for focused efforts to reduce hospital readmission rates.24 The quality of hospital discharge care is a key determinant of readmission rates,57 and many recent interventions to reduce readmission have focused on improving various aspects of the discharge process.810 Although these approaches have shown promise, the role of physicians in improving the quality of discharge care has not been extensively studied. Existing studies have focused on communication barriers between physicians in the hospital and outpatient settings,1113 but these have not examined the hospital discharge process itself and the experience of physicians in that process. Physician perspectives on this process are critical to inform strategies to leverage their roles in improving the performance of discharge teams.

Accordingly, we sought to understand physician experiences with the hospital discharge process, focusing on factors that physicians perceived to limit the quality of the discharge process at teaching hospitals. Teaching hospitals provided an ideal setting for this study given their high readmission rates,14 despite efforts to improve discharge quality of care through multidisciplinary team approaches. We focused on housestaff physicians because of their in‐depth involvement in the discharge process at teaching hospitals, which collectively provide 20% of all hospital care in the US.15 Housestaff perspectives on quality‐limiting factors of the discharge process may help identify targets for interventions to improve the quality of inpatient discharge care and to ultimately reduce hospital readmissions.

METHODS

RESULTS

Based on interview transcripts from 29 internal medicine housestaff physicians (Table 3), we identified 5 recurrent and unifying themes describing factors perceived to limit the quality of inpatient discharge care: (1) competing priorities in the discharge process; (2) inadequate coordination within multidisciplinary discharge teams; (3) lack of standardization in discharge procedures; (4) poor patient and family communication; and (5) lack of postdischarge feedback and clinical responsibility.

DISCUSSION

Housestaff physicians experienced 5 quality‐limiting factors that collectively created and reinforced a practice environment in which patients and patient outcomes after discharge remain largely out of sight, out of mind. In this environment, discharge was often viewed as a summative event that signaled the conclusion of care in one setting rather than a transition in care from one setting to another. Paradoxically, this environment was apparent despite the values and goals participants described for providing high‐quality discharge care, working within multidisciplinary discharge teams, and reducing readmissions.

The degree to which housestaff were focused on the hospital portion of patients' care, and viewed postdischarge care as beyond their scope or responsibility, was striking. The tight boundary they drew between hospital and post‐hospital care reflected the demanding workload in the hospital, the lack of data feedback about patients post‐hospitalization, and professional norms and expectations about housestaff responsibilities. Downstream effects of this tight boundary may result in confusion for patients and family about who to contact in case of postdischarge complications, and may ultimately catalyze higher emergency department use and readmissions.26 Efforts to redefine inpatient physician responsibilities, as providing patient care until management has been successfully transferred to a community‐based provider, may be necessary to ensure adequate postdischarge continuity of care.27

We also found that housestaff physicians reported marked variation in discharge practices across different hospitals and training settings, across different teams within hospitals, and across individual attending physicians. Although guidelines for discharge care currently endorsed by the National Quality Forum28 and others4, 27, 29 provide excellent templates, our findings suggest that the implementation of these standards at the hospital and physician level is limited. Furthermore, while existing single‐site interventions to standardize various discharge practices provide a foundational evidence base for high‐quality discharge care,2932 our study adds insight into the individual and institutional barriers that prevent diffusion of these practices to other hospitals.

Finally, the lack of coordination within discharge teams, described by housestaff physicians in our study, also suggests a need for improved leadership in the hospital overall and at the level of the discharge team. Studies of high‐performing hospitals have shown that top‐level institutional support is a necessary substrate for the creation and maintenance of high‐performance teamwork.33 At the level of the discharge team, creating a culture of high‐quality discharge care will require greater focus on defining team‐member roles and responsibilities. At the individual level, changes in physician training to provide discharge care are critical, especially since practice patterns learned in residency may predict quality of care over physicians' careers.34 Recent examples of curricular innovations for discharge care are encouraging,35 but more research on how physicians learn about discharge care and related systems‐based practice, learning, and improvement is needed to enable changes on a national scale.

Our findings should be interpreted in light of several limitations. First, we recruited housestaff from 1 specialty at 2 large training programs; experiences of housestaff in other specialties and other training programs may differ. Second, we cannot quantify the frequency of specific discharge procedures or outcomes described by our participants, as this was beyond the scope of our qualitative approach. Nevertheless, our aim was to explore the range of quality‐limiting factors, rather than their prevalence, and this in‐depth analysis has extended previous work by identifying factors that may influence the quality of discharge care. Third, social desirability bias36 could have led participants to exaggerate or minimize aspects of quality‐limiting factors identified in this study. To minimize this potential bias, we included specific prompts for both negative and positive aspects of providing discharge care in our interview guide. Finally, our analytic decisions to over‐sample for interns, and to not include physicians who have completed training (eg, hospitalists), may introduce bias towards inexperience; however, our objective was to study the culture of discharge care at teaching hospitals, and our sample reflects the distribution of labor for tasks of discharge care at such institutions. Future research should address important questions raised by this study about the role of attending physicians in discharge care at teaching and non‐teaching hospitals.

Improving the quality of discharge care is an important step to improving overall outcomes of hospitalization, including reduced adverse events and unnecessary admissions. Our study suggests important quality‐limiting factors embedded in the norms for discharge care at teaching hospitals. These factors are unlikely to change without interventions at multiple levels of hospitals, discharge teams, and individual providers. Targeted interventions to change these practices will be necessary to achieve higher overall quality of care for hospitalized patients at teaching hospitals.

Hospital readmission is a common, costly, and often preventable occurrence in the United States. Among Medicare beneficiaries, 1 out of 5 patients is readmitted within 30 days, and the cost of unplanned readmissions exceeded $17 billion in 2007.1 As a result, the Centers for Medicare and Medicaid Services (CMS) and others have called for focused efforts to reduce hospital readmission rates.24 The quality of hospital discharge care is a key determinant of readmission rates,57 and many recent interventions to reduce readmission have focused on improving various aspects of the discharge process.810 Although these approaches have shown promise, the role of physicians in improving the quality of discharge care has not been extensively studied. Existing studies have focused on communication barriers between physicians in the hospital and outpatient settings,1113 but these have not examined the hospital discharge process itself and the experience of physicians in that process. Physician perspectives on this process are critical to inform strategies to leverage their roles in improving the performance of discharge teams.

Accordingly, we sought to understand physician experiences with the hospital discharge process, focusing on factors that physicians perceived to limit the quality of the discharge process at teaching hospitals. Teaching hospitals provided an ideal setting for this study given their high readmission rates,14 despite efforts to improve discharge quality of care through multidisciplinary team approaches. We focused on housestaff physicians because of their in‐depth involvement in the discharge process at teaching hospitals, which collectively provide 20% of all hospital care in the US.15 Housestaff perspectives on quality‐limiting factors of the discharge process may help identify targets for interventions to improve the quality of inpatient discharge care and to ultimately reduce hospital readmissions.

METHODS

RESULTS

Based on interview transcripts from 29 internal medicine housestaff physicians (Table 3), we identified 5 recurrent and unifying themes describing factors perceived to limit the quality of inpatient discharge care: (1) competing priorities in the discharge process; (2) inadequate coordination within multidisciplinary discharge teams; (3) lack of standardization in discharge procedures; (4) poor patient and family communication; and (5) lack of postdischarge feedback and clinical responsibility.

DISCUSSION

Housestaff physicians experienced 5 quality‐limiting factors that collectively created and reinforced a practice environment in which patients and patient outcomes after discharge remain largely out of sight, out of mind. In this environment, discharge was often viewed as a summative event that signaled the conclusion of care in one setting rather than a transition in care from one setting to another. Paradoxically, this environment was apparent despite the values and goals participants described for providing high‐quality discharge care, working within multidisciplinary discharge teams, and reducing readmissions.

The degree to which housestaff were focused on the hospital portion of patients' care, and viewed postdischarge care as beyond their scope or responsibility, was striking. The tight boundary they drew between hospital and post‐hospital care reflected the demanding workload in the hospital, the lack of data feedback about patients post‐hospitalization, and professional norms and expectations about housestaff responsibilities. Downstream effects of this tight boundary may result in confusion for patients and family about who to contact in case of postdischarge complications, and may ultimately catalyze higher emergency department use and readmissions.26 Efforts to redefine inpatient physician responsibilities, as providing patient care until management has been successfully transferred to a community‐based provider, may be necessary to ensure adequate postdischarge continuity of care.27

We also found that housestaff physicians reported marked variation in discharge practices across different hospitals and training settings, across different teams within hospitals, and across individual attending physicians. Although guidelines for discharge care currently endorsed by the National Quality Forum28 and others4, 27, 29 provide excellent templates, our findings suggest that the implementation of these standards at the hospital and physician level is limited. Furthermore, while existing single‐site interventions to standardize various discharge practices provide a foundational evidence base for high‐quality discharge care,2932 our study adds insight into the individual and institutional barriers that prevent diffusion of these practices to other hospitals.

Finally, the lack of coordination within discharge teams, described by housestaff physicians in our study, also suggests a need for improved leadership in the hospital overall and at the level of the discharge team. Studies of high‐performing hospitals have shown that top‐level institutional support is a necessary substrate for the creation and maintenance of high‐performance teamwork.33 At the level of the discharge team, creating a culture of high‐quality discharge care will require greater focus on defining team‐member roles and responsibilities. At the individual level, changes in physician training to provide discharge care are critical, especially since practice patterns learned in residency may predict quality of care over physicians' careers.34 Recent examples of curricular innovations for discharge care are encouraging,35 but more research on how physicians learn about discharge care and related systems‐based practice, learning, and improvement is needed to enable changes on a national scale.

Our findings should be interpreted in light of several limitations. First, we recruited housestaff from 1 specialty at 2 large training programs; experiences of housestaff in other specialties and other training programs may differ. Second, we cannot quantify the frequency of specific discharge procedures or outcomes described by our participants, as this was beyond the scope of our qualitative approach. Nevertheless, our aim was to explore the range of quality‐limiting factors, rather than their prevalence, and this in‐depth analysis has extended previous work by identifying factors that may influence the quality of discharge care. Third, social desirability bias36 could have led participants to exaggerate or minimize aspects of quality‐limiting factors identified in this study. To minimize this potential bias, we included specific prompts for both negative and positive aspects of providing discharge care in our interview guide. Finally, our analytic decisions to over‐sample for interns, and to not include physicians who have completed training (eg, hospitalists), may introduce bias towards inexperience; however, our objective was to study the culture of discharge care at teaching hospitals, and our sample reflects the distribution of labor for tasks of discharge care at such institutions. Future research should address important questions raised by this study about the role of attending physicians in discharge care at teaching and non‐teaching hospitals.

Improving the quality of discharge care is an important step to improving overall outcomes of hospitalization, including reduced adverse events and unnecessary admissions. Our study suggests important quality‐limiting factors embedded in the norms for discharge care at teaching hospitals. These factors are unlikely to change without interventions at multiple levels of hospitals, discharge teams, and individual providers. Targeted interventions to change these practices will be necessary to achieve higher overall quality of care for hospitalized patients at teaching hospitals.

Hospital readmission is a common, costly, and often preventable occurrence in the United States. Among Medicare beneficiaries, 1 out of 5 patients is readmitted within 30 days, and the cost of unplanned readmissions exceeded $17 billion in 2007.1 As a result, the Centers for Medicare and Medicaid Services (CMS) and others have called for focused efforts to reduce hospital readmission rates.24 The quality of hospital discharge care is a key determinant of readmission rates,57 and many recent interventions to reduce readmission have focused on improving various aspects of the discharge process.810 Although these approaches have shown promise, the role of physicians in improving the quality of discharge care has not been extensively studied. Existing studies have focused on communication barriers between physicians in the hospital and outpatient settings,1113 but these have not examined the hospital discharge process itself and the experience of physicians in that process. Physician perspectives on this process are critical to inform strategies to leverage their roles in improving the performance of discharge teams.

Accordingly, we sought to understand physician experiences with the hospital discharge process, focusing on factors that physicians perceived to limit the quality of the discharge process at teaching hospitals. Teaching hospitals provided an ideal setting for this study given their high readmission rates,14 despite efforts to improve discharge quality of care through multidisciplinary team approaches. We focused on housestaff physicians because of their in‐depth involvement in the discharge process at teaching hospitals, which collectively provide 20% of all hospital care in the US.15 Housestaff perspectives on quality‐limiting factors of the discharge process may help identify targets for interventions to improve the quality of inpatient discharge care and to ultimately reduce hospital readmissions.

METHODS

RESULTS

Based on interview transcripts from 29 internal medicine housestaff physicians (Table 3), we identified 5 recurrent and unifying themes describing factors perceived to limit the quality of inpatient discharge care: (1) competing priorities in the discharge process; (2) inadequate coordination within multidisciplinary discharge teams; (3) lack of standardization in discharge procedures; (4) poor patient and family communication; and (5) lack of postdischarge feedback and clinical responsibility.

DISCUSSION

Housestaff physicians experienced 5 quality‐limiting factors that collectively created and reinforced a practice environment in which patients and patient outcomes after discharge remain largely out of sight, out of mind. In this environment, discharge was often viewed as a summative event that signaled the conclusion of care in one setting rather than a transition in care from one setting to another. Paradoxically, this environment was apparent despite the values and goals participants described for providing high‐quality discharge care, working within multidisciplinary discharge teams, and reducing readmissions.

The degree to which housestaff were focused on the hospital portion of patients' care, and viewed postdischarge care as beyond their scope or responsibility, was striking. The tight boundary they drew between hospital and post‐hospital care reflected the demanding workload in the hospital, the lack of data feedback about patients post‐hospitalization, and professional norms and expectations about housestaff responsibilities. Downstream effects of this tight boundary may result in confusion for patients and family about who to contact in case of postdischarge complications, and may ultimately catalyze higher emergency department use and readmissions.26 Efforts to redefine inpatient physician responsibilities, as providing patient care until management has been successfully transferred to a community‐based provider, may be necessary to ensure adequate postdischarge continuity of care.27

We also found that housestaff physicians reported marked variation in discharge practices across different hospitals and training settings, across different teams within hospitals, and across individual attending physicians. Although guidelines for discharge care currently endorsed by the National Quality Forum28 and others4, 27, 29 provide excellent templates, our findings suggest that the implementation of these standards at the hospital and physician level is limited. Furthermore, while existing single‐site interventions to standardize various discharge practices provide a foundational evidence base for high‐quality discharge care,2932 our study adds insight into the individual and institutional barriers that prevent diffusion of these practices to other hospitals.

Finally, the lack of coordination within discharge teams, described by housestaff physicians in our study, also suggests a need for improved leadership in the hospital overall and at the level of the discharge team. Studies of high‐performing hospitals have shown that top‐level institutional support is a necessary substrate for the creation and maintenance of high‐performance teamwork.33 At the level of the discharge team, creating a culture of high‐quality discharge care will require greater focus on defining team‐member roles and responsibilities. At the individual level, changes in physician training to provide discharge care are critical, especially since practice patterns learned in residency may predict quality of care over physicians' careers.34 Recent examples of curricular innovations for discharge care are encouraging,35 but more research on how physicians learn about discharge care and related systems‐based practice, learning, and improvement is needed to enable changes on a national scale.

Our findings should be interpreted in light of several limitations. First, we recruited housestaff from 1 specialty at 2 large training programs; experiences of housestaff in other specialties and other training programs may differ. Second, we cannot quantify the frequency of specific discharge procedures or outcomes described by our participants, as this was beyond the scope of our qualitative approach. Nevertheless, our aim was to explore the range of quality‐limiting factors, rather than their prevalence, and this in‐depth analysis has extended previous work by identifying factors that may influence the quality of discharge care. Third, social desirability bias36 could have led participants to exaggerate or minimize aspects of quality‐limiting factors identified in this study. To minimize this potential bias, we included specific prompts for both negative and positive aspects of providing discharge care in our interview guide. Finally, our analytic decisions to over‐sample for interns, and to not include physicians who have completed training (eg, hospitalists), may introduce bias towards inexperience; however, our objective was to study the culture of discharge care at teaching hospitals, and our sample reflects the distribution of labor for tasks of discharge care at such institutions. Future research should address important questions raised by this study about the role of attending physicians in discharge care at teaching and non‐teaching hospitals.

Improving the quality of discharge care is an important step to improving overall outcomes of hospitalization, including reduced adverse events and unnecessary admissions. Our study suggests important quality‐limiting factors embedded in the norms for discharge care at teaching hospitals. These factors are unlikely to change without interventions at multiple levels of hospitals, discharge teams, and individual providers. Targeted interventions to change these practices will be necessary to achieve higher overall quality of care for hospitalized patients at teaching hospitals.

The United States spends more money per capita on healthcare than any other industrialized nation,1 yet patients are the least satisfied with their care.2 Patient satisfaction is associated in both cross‐sectional3 and longitudinal studies4 with improved physical and mental health outcomes. Conversely, dissatisfaction with care hampers future medical interactions, prevents sharing of information, and impairs the building of trust.5 The increasing recognition that a patient's experience of care affects patient outcomes has furthered efforts to evaluate satisfaction with care.6, 7

However, patient satisfaction is challenging to define and understand. Even the definition of satisfaction is ambiguous, for to satisfy can mean both to make happy and the lesser, to be adequate. To dissatisfy is to displease or disappoint, but dissatisfaction is not the opposite of satisfaction: qualitative studies give little if any indication that patients evaluate satisfaction on a continuum ranging from dissatisfied at one end to very satisfied at the other.8 Instead, it appears that satisfaction and dissatisfaction are different constructs, such that patients may simultaneously be both satisfied and dissatisfied.9, 10 Patients often express overall satisfaction with a service or encounter while also reporting specific criticisms about its shortcomings.11, 12 Alternatively, consumers may be generally satisfied unless something unpleasant or improper happens.13 Thus, dissatisfaction and satisfaction may require different methods of measurement.

The most common model for measuring patient satisfaction is a quantitative survey of patients' experiences in specific predetermined domains. Of 54 hospital satisfaction surveys in common use, only 11 included patient input in their development,14 casting doubt on the relevance of these attributes to patients' priorities of care. Since it is well recognized that patient expectations influence satisfaction,8, 13, 15 it is important to identify patient expectations and priorities up front. However, these have not been clearly established. Furthermore, focusing purely on satisfaction with particular domains of care may miss the separate but illuminating construct of patient dissatisfaction.

In this study we therefore aim to understand patient dissatisfaction with hospitalization more fully as a means of elucidating implicit expectations for hospital care. Using qualitative techniques, we analyzed a large volume of patient responses to a single open‐ended study question to identify determinants and patterns of patient dissatisfaction.

Methods

Results

A total of 976 surveys was randomly selected from 9,764 postdischarge phone interviews completed between July 1, 2007 and June 30, 2008. A total of 56.3% of patients was female. Nearly half the patients were discharged from medical units (Table 1). Of the 976 patients, 439 (45.0%) gave at least one suggestion for improvement, yielding a total of 579 suggestions. Patients also offered numerous positive comments about their care, but these comments were not included in the analysis.

Through qualitative analysis, we assigned suggestions for improvement to six major categories of dissatisfaction: 1) ineptitude, 2) disrespect, 3) prolonged waits, 4) ineffective communication, 5) lack of environmental control, and 6) substandard amenities. We considered the inverse of these problems to represent six implicit expectations of good hospital care: 1) safety, 2) treatment with respect and dignity, 3) prompt and efficient care, 4) successful exchange of information, 5) environmental autonomy and control, and 6) high‐quality amenities (Table 2). The number of patient suggestions related to each domain is detailed in Table 3.