Leslie A. Curry, 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.

Interest in healthcare teams has surged in recent years. A majority of the interest has been devoted to teamwork in the interdisciplinary clinical teams that staff operating rooms,1 emergency departments,2 and other inpatient settings.3 Interventions that enhance elements of teamwork like communication, mutual support among team members, and leadership have demonstrated effectiveness.4

Less attention has been paid to improving the success of hospital quality improvement (QI) teams, which gather individuals from different disciplines to improve a defined aspect of care. Studies suggest that QI teams can enable transformational change in healthcare systems,57 and that interdisciplinary representation,8, 9 physician involvement,10, 11 and clear goals12, 13 are associated with successful QI efforts. However, few studies have examined the behaviors of the QI teams that planned and implemented these efforts. Understanding how QI teams work to achieve their goals will allow hospitals to encourage these behaviors, and allow researchers to design interventions to augment these behaviors.

Accordingly, we sought to characterize the behaviors of successful interdisciplinary hospital QI teams. We previously reported on the strategies used by hospitals to reduce door‐to‐balloon times for patients with ST‐elevation myocardial infarction (STEMI)14, 15 to the evidence‐based guideline of 90 minutes.16 Our objective is to examine how QI teams designed and implemented these strategies. We believe that studying high‐performing QI teams is a first step to developing testable hypotheses about the effectiveness of QI team behaviors and mechanisms by which these behaviors might produce positive team outcomes.

METHODS

We designed a qualitative study using in‐depth interviews. We selected a qualitative methodology, since behaviors, social norms, and interpersonal interactions can be most appropriately examined using qualitative methods.17, 18 In addition, we used a positive deviance approach,19 where we focused on hospitals with top performance and the most improvement in door‐to‐balloon times. We sampled from hospitals in the National Registry of Myocardial Infarction (NRMI) who perform percutaneous coronary intervention (PCI, n = 151). We selected hospitals whose median door‐to‐balloon times were 90 minutes (n = 35). Then, we ranked hospitals in descending order according to their improvement during the previous 3 years (19992002). We sampled hospitals in descending order until we reached theoretical saturation where, as recommended for qualitative inquiry,2022 additional site visits did not uncover new concepts or patterns regarding our study questions. All sampled hospitals agreed to participate.

The first contact at each hospital was typically the director of QI. We asked to interview anyone with substantial involvement in the effort to reduce door‐to‐balloon times, and suggested that a wide variety of disciplines and roles be represented. We also used the snowball technique,22 where we asked participants to provide the names of individuals with substantial involvement in the reducing door‐to‐balloon times. Participants had varied levels of participation in QI teams. We purposely asked for minority and dissenting views from all participants.

At least 2 members of the research team conducted in‐depth interviews during hospital site visits. Interviews were conducted individually or in small groups, and lasted 1 to 1.5 hours. All data were audiotaped after verbal consent. Our interviews began with the grand tour question: What, if anything, has this hospital done to reduce its door‐to‐balloon times for patients with STEMI? The research team used standardized probes20, 23 to guide the discussion and achieve a complete understanding of the phenomena under study, including leadership and activities of the QI teams, and recommendations to other hospitals that wished to reduce door‐to‐balloon times. As recommended by experts,23 our interview guide was purposefully open‐ended to capture the range of experiences with QI teams. We did not specifically probe for facilitating or challenging behaviors. Audiotapes were transcribed by an independent, professional transcriptionist.

For this analysis, we defined QI teams as groups of administrators, providers, and staff who designed, implemented, and monitored processes to reduce door‐to‐balloon times. Each analysis team member independently cataloged quotes about team behaviors using a list of concepts (or codes). We then analyzed the quotes to identify recurrent themes relevant to the behaviors of interdisciplinary QI teams. We used the constant comparative method of analysis,20, 24, 25 which stipulates that the initial list of codes is refined as new transcripts are analyzed, and the final list is applied to all the transcripts. The analysis team included experts in QI, medicine, qualitative and health services research, as well as organizational psychology, and one of the interviewers. The presence of diverse perspectives in the analysis team,21 and a detailed audit trail20 to document the emergence of codes and themes, helped enhance researcher neutrality, data accuracy, and validity. We used Atlas.ti version 5.2 (Scientific Software Development GMbH, Berlin, Germany) to assist in the analysis.

RESULTS

Our sample (n = 11) included hospitals that varied on several characteristics (eg, geographic location), and median door‐to‐balloon times ranged from 55.5 to 89.5 minutes (Table 1). Hospitals in our sample had higher mean improvements in door‐to‐balloon times compared with non‐sampled NRMI hospitals (n = 140, 24 minutes vs 3 minutes over 3 years). Our interview participants (n = 122) included physicians, nurses, QI personnel, and administrative staff (Table 2). Five behaviors emerged from the data analysis. We found that interdisciplinary QI teams in successful hospitals focused on: (1) motivating involved hospital staff towards a shared goal, (2) creating opportunities for learning and problem‐solving, (3) addressing the impact of changes in care processes on staff, (4) protecting the integrity of the newly developed care processes, and (5) representing each involved clinical discipline effectively. These behaviors were recurrent across our diverse set of hospitals.

Discussion

We identified 5 behaviors of successful interdisciplinary QI teams based on our analysis of hospitals that reduced door‐to‐balloon times for patients with STEMI. These QI teams: (1) motivated involved hospital staff to consider lowering door‐to‐balloon times, a shared goal, (2) created opportunities for learning and problem‐solving, (3) addressed the impact of changes to care processes for patients with STEMI on staff, (4) protected the integrity of new care processes, and (5) represented each clinical discipline effectively by having members with in‐depth knowledge and authority.

Experts suggest that the key elements of effective teamwork in healthcare include prioritizing team over individual goals, mutual understanding, leadership, adaptability, and anticipation of the needs of others.26 These elements are supported by mutual trust and closed‐loop communication. The behaviors of QI teams in our study represent adaptive responses to the unique demands of QI in a complex organization. These teams went beyond an improvement model of identifying and analyzing a problem, and then developing and testing solutions by: (1) motivating and gathering information from each discipline, regardless of interdisciplinary conflicts; (2) responding to the concerns of front‐line staff, while maintaining control over the improvement process; and (3) sharing information across the hospital hierarchy. Table 3 illustrates potential relationships between the team behaviors in our data, the demands on hospital QI teams, and known elements of effective teamwork.

The behaviors in our study suggest effective teamwork strategies for QI. For example, our data suggest that successful interdisciplinary QI teams need effective representation from each involved discipline. This representation is necessary for motivation of front‐line staff, gathering of detailed information about processes, and the effective implementation of changes. Although this level of representation might challenge the cohesiveness of some teams,27 the teams in our sample managed conflict among disciplines without sacrificing the shared goal. By allocating attention and resources to the concerns of each discipline, the teams we studied prioritized team over individual goals and promoted mutual understanding.

Similarly, deciding when to modify the new protocols required leadership, adaptability, and anticipation of the needs of others. Successful QI teams in our sample modified protocols based on data and feedback, and created the mutual trust environment that is known to facilitate learning among disciplines.2830 Their willingness to learn, however, did not deter teams from protecting the integrity of new protocols. Lastly, participants stressed the importance of managing information across hierarchical boundaries. Managing reliable, timely, and accurate information across all levels is crucial to teamwork, and to the power and influence of a team.31

Our conclusions should be interpreted in light of several limitations. First, our study did not include a comparison group of low‐performing hospitals. We followed the recommendations of qualitative research experts23 who recommend sampling those with the most information on, and experience with, the phenomena under study (QI teams in high‐performing hospitals). The hypotheses we present here require further testing in quantitative studies of hospitals with diversity in QI team outcomes. Second, it is possible that sampled participants favored responses that they considered more desirable. To minimize this bias, we interviewed multiple participants per hospital, assured their confidentiality, and asked them to elaborate their responses. We sampled participants with a wide range of clinical and operational roles in each hospital, and also used the snowball sampling method to augment our sample. The range of responses collected, including frank discussions about setbacks, argues against the existence of contrasting behaviors to those captured. Third, although our sample included hospitals of various size and location, our findings might not reflect those of a larger sample of US hospitals. Last, the behaviors of QI teams may differ for other clinical processes.

Translating these findings into practice will require future studies of the impact of QI team behaviors on sustainability of quality gains. Since QI teams are not typically permanent, additional research is needed to identify behaviors associated with sustainable improvements. In addition, we must test whether the relationship between behaviors and team outcomes depends on whether the QI team strives to reach an evidence‐based goal or to improve a process as much as possible. Our sample demonstrated a combined approach, where the evidence‐based goal was followed by a desire to continue to further reduce door‐to‐balloon times. Similarly, the relationship between behaviors and team outcomes might depend on the catalyst for improvement (eg, regulatory pressure, an adverse event). The confluence of strong evidence and regulatory pressure that fueled these teams might not be true for other measures. Lastly, studies of teamwork in QI teams will require objective measures of team behaviors. A combination of surveys and direct team observation will likely be required to measure these behaviors, especially effective representation.

Our study highlights behaviors common to successful interdisciplinary QI teams in high‐performing hospitals. Previous studies have identified elements of teamwork and the importance of teams to QI, but have not examined team behaviors. In the era of an ever‐growing list of quality measures and of movement toward performance‐based reimbursement models,3234 hospitals have embraced the use of interdisciplinary teams as a key component of QI efforts. Our findings suggest that hospitals could enhance QI team effectiveness by promoting behaviors associated with successful interdisciplinary teams. When applied to QI teams, teamwork training could be supplemented with knowledge, attitudes, and skills regarding information‐gathering, problem‐solving, and communication across disciplines and levels of the hospital hierarchy.

Interest in healthcare teams has surged in recent years. A majority of the interest has been devoted to teamwork in the interdisciplinary clinical teams that staff operating rooms,1 emergency departments,2 and other inpatient settings.3 Interventions that enhance elements of teamwork like communication, mutual support among team members, and leadership have demonstrated effectiveness.4

Less attention has been paid to improving the success of hospital quality improvement (QI) teams, which gather individuals from different disciplines to improve a defined aspect of care. Studies suggest that QI teams can enable transformational change in healthcare systems,57 and that interdisciplinary representation,8, 9 physician involvement,10, 11 and clear goals12, 13 are associated with successful QI efforts. However, few studies have examined the behaviors of the QI teams that planned and implemented these efforts. Understanding how QI teams work to achieve their goals will allow hospitals to encourage these behaviors, and allow researchers to design interventions to augment these behaviors.

Accordingly, we sought to characterize the behaviors of successful interdisciplinary hospital QI teams. We previously reported on the strategies used by hospitals to reduce door‐to‐balloon times for patients with ST‐elevation myocardial infarction (STEMI)14, 15 to the evidence‐based guideline of 90 minutes.16 Our objective is to examine how QI teams designed and implemented these strategies. We believe that studying high‐performing QI teams is a first step to developing testable hypotheses about the effectiveness of QI team behaviors and mechanisms by which these behaviors might produce positive team outcomes.

METHODS

We designed a qualitative study using in‐depth interviews. We selected a qualitative methodology, since behaviors, social norms, and interpersonal interactions can be most appropriately examined using qualitative methods.17, 18 In addition, we used a positive deviance approach,19 where we focused on hospitals with top performance and the most improvement in door‐to‐balloon times. We sampled from hospitals in the National Registry of Myocardial Infarction (NRMI) who perform percutaneous coronary intervention (PCI, n = 151). We selected hospitals whose median door‐to‐balloon times were 90 minutes (n = 35). Then, we ranked hospitals in descending order according to their improvement during the previous 3 years (19992002). We sampled hospitals in descending order until we reached theoretical saturation where, as recommended for qualitative inquiry,2022 additional site visits did not uncover new concepts or patterns regarding our study questions. All sampled hospitals agreed to participate.

The first contact at each hospital was typically the director of QI. We asked to interview anyone with substantial involvement in the effort to reduce door‐to‐balloon times, and suggested that a wide variety of disciplines and roles be represented. We also used the snowball technique,22 where we asked participants to provide the names of individuals with substantial involvement in the reducing door‐to‐balloon times. Participants had varied levels of participation in QI teams. We purposely asked for minority and dissenting views from all participants.

At least 2 members of the research team conducted in‐depth interviews during hospital site visits. Interviews were conducted individually or in small groups, and lasted 1 to 1.5 hours. All data were audiotaped after verbal consent. Our interviews began with the grand tour question: What, if anything, has this hospital done to reduce its door‐to‐balloon times for patients with STEMI? The research team used standardized probes20, 23 to guide the discussion and achieve a complete understanding of the phenomena under study, including leadership and activities of the QI teams, and recommendations to other hospitals that wished to reduce door‐to‐balloon times. As recommended by experts,23 our interview guide was purposefully open‐ended to capture the range of experiences with QI teams. We did not specifically probe for facilitating or challenging behaviors. Audiotapes were transcribed by an independent, professional transcriptionist.

For this analysis, we defined QI teams as groups of administrators, providers, and staff who designed, implemented, and monitored processes to reduce door‐to‐balloon times. Each analysis team member independently cataloged quotes about team behaviors using a list of concepts (or codes). We then analyzed the quotes to identify recurrent themes relevant to the behaviors of interdisciplinary QI teams. We used the constant comparative method of analysis,20, 24, 25 which stipulates that the initial list of codes is refined as new transcripts are analyzed, and the final list is applied to all the transcripts. The analysis team included experts in QI, medicine, qualitative and health services research, as well as organizational psychology, and one of the interviewers. The presence of diverse perspectives in the analysis team,21 and a detailed audit trail20 to document the emergence of codes and themes, helped enhance researcher neutrality, data accuracy, and validity. We used Atlas.ti version 5.2 (Scientific Software Development GMbH, Berlin, Germany) to assist in the analysis.

RESULTS

Our sample (n = 11) included hospitals that varied on several characteristics (eg, geographic location), and median door‐to‐balloon times ranged from 55.5 to 89.5 minutes (Table 1). Hospitals in our sample had higher mean improvements in door‐to‐balloon times compared with non‐sampled NRMI hospitals (n = 140, 24 minutes vs 3 minutes over 3 years). Our interview participants (n = 122) included physicians, nurses, QI personnel, and administrative staff (Table 2). Five behaviors emerged from the data analysis. We found that interdisciplinary QI teams in successful hospitals focused on: (1) motivating involved hospital staff towards a shared goal, (2) creating opportunities for learning and problem‐solving, (3) addressing the impact of changes in care processes on staff, (4) protecting the integrity of the newly developed care processes, and (5) representing each involved clinical discipline effectively. These behaviors were recurrent across our diverse set of hospitals.

Discussion

We identified 5 behaviors of successful interdisciplinary QI teams based on our analysis of hospitals that reduced door‐to‐balloon times for patients with STEMI. These QI teams: (1) motivated involved hospital staff to consider lowering door‐to‐balloon times, a shared goal, (2) created opportunities for learning and problem‐solving, (3) addressed the impact of changes to care processes for patients with STEMI on staff, (4) protected the integrity of new care processes, and (5) represented each clinical discipline effectively by having members with in‐depth knowledge and authority.

Experts suggest that the key elements of effective teamwork in healthcare include prioritizing team over individual goals, mutual understanding, leadership, adaptability, and anticipation of the needs of others.26 These elements are supported by mutual trust and closed‐loop communication. The behaviors of QI teams in our study represent adaptive responses to the unique demands of QI in a complex organization. These teams went beyond an improvement model of identifying and analyzing a problem, and then developing and testing solutions by: (1) motivating and gathering information from each discipline, regardless of interdisciplinary conflicts; (2) responding to the concerns of front‐line staff, while maintaining control over the improvement process; and (3) sharing information across the hospital hierarchy. Table 3 illustrates potential relationships between the team behaviors in our data, the demands on hospital QI teams, and known elements of effective teamwork.

The behaviors in our study suggest effective teamwork strategies for QI. For example, our data suggest that successful interdisciplinary QI teams need effective representation from each involved discipline. This representation is necessary for motivation of front‐line staff, gathering of detailed information about processes, and the effective implementation of changes. Although this level of representation might challenge the cohesiveness of some teams,27 the teams in our sample managed conflict among disciplines without sacrificing the shared goal. By allocating attention and resources to the concerns of each discipline, the teams we studied prioritized team over individual goals and promoted mutual understanding.

Similarly, deciding when to modify the new protocols required leadership, adaptability, and anticipation of the needs of others. Successful QI teams in our sample modified protocols based on data and feedback, and created the mutual trust environment that is known to facilitate learning among disciplines.2830 Their willingness to learn, however, did not deter teams from protecting the integrity of new protocols. Lastly, participants stressed the importance of managing information across hierarchical boundaries. Managing reliable, timely, and accurate information across all levels is crucial to teamwork, and to the power and influence of a team.31

Our conclusions should be interpreted in light of several limitations. First, our study did not include a comparison group of low‐performing hospitals. We followed the recommendations of qualitative research experts23 who recommend sampling those with the most information on, and experience with, the phenomena under study (QI teams in high‐performing hospitals). The hypotheses we present here require further testing in quantitative studies of hospitals with diversity in QI team outcomes. Second, it is possible that sampled participants favored responses that they considered more desirable. To minimize this bias, we interviewed multiple participants per hospital, assured their confidentiality, and asked them to elaborate their responses. We sampled participants with a wide range of clinical and operational roles in each hospital, and also used the snowball sampling method to augment our sample. The range of responses collected, including frank discussions about setbacks, argues against the existence of contrasting behaviors to those captured. Third, although our sample included hospitals of various size and location, our findings might not reflect those of a larger sample of US hospitals. Last, the behaviors of QI teams may differ for other clinical processes.

Translating these findings into practice will require future studies of the impact of QI team behaviors on sustainability of quality gains. Since QI teams are not typically permanent, additional research is needed to identify behaviors associated with sustainable improvements. In addition, we must test whether the relationship between behaviors and team outcomes depends on whether the QI team strives to reach an evidence‐based goal or to improve a process as much as possible. Our sample demonstrated a combined approach, where the evidence‐based goal was followed by a desire to continue to further reduce door‐to‐balloon times. Similarly, the relationship between behaviors and team outcomes might depend on the catalyst for improvement (eg, regulatory pressure, an adverse event). The confluence of strong evidence and regulatory pressure that fueled these teams might not be true for other measures. Lastly, studies of teamwork in QI teams will require objective measures of team behaviors. A combination of surveys and direct team observation will likely be required to measure these behaviors, especially effective representation.

Our study highlights behaviors common to successful interdisciplinary QI teams in high‐performing hospitals. Previous studies have identified elements of teamwork and the importance of teams to QI, but have not examined team behaviors. In the era of an ever‐growing list of quality measures and of movement toward performance‐based reimbursement models,3234 hospitals have embraced the use of interdisciplinary teams as a key component of QI efforts. Our findings suggest that hospitals could enhance QI team effectiveness by promoting behaviors associated with successful interdisciplinary teams. When applied to QI teams, teamwork training could be supplemented with knowledge, attitudes, and skills regarding information‐gathering, problem‐solving, and communication across disciplines and levels of the hospital hierarchy.

Interest in healthcare teams has surged in recent years. A majority of the interest has been devoted to teamwork in the interdisciplinary clinical teams that staff operating rooms,1 emergency departments,2 and other inpatient settings.3 Interventions that enhance elements of teamwork like communication, mutual support among team members, and leadership have demonstrated effectiveness.4

Less attention has been paid to improving the success of hospital quality improvement (QI) teams, which gather individuals from different disciplines to improve a defined aspect of care. Studies suggest that QI teams can enable transformational change in healthcare systems,57 and that interdisciplinary representation,8, 9 physician involvement,10, 11 and clear goals12, 13 are associated with successful QI efforts. However, few studies have examined the behaviors of the QI teams that planned and implemented these efforts. Understanding how QI teams work to achieve their goals will allow hospitals to encourage these behaviors, and allow researchers to design interventions to augment these behaviors.

Accordingly, we sought to characterize the behaviors of successful interdisciplinary hospital QI teams. We previously reported on the strategies used by hospitals to reduce door‐to‐balloon times for patients with ST‐elevation myocardial infarction (STEMI)14, 15 to the evidence‐based guideline of 90 minutes.16 Our objective is to examine how QI teams designed and implemented these strategies. We believe that studying high‐performing QI teams is a first step to developing testable hypotheses about the effectiveness of QI team behaviors and mechanisms by which these behaviors might produce positive team outcomes.

METHODS

We designed a qualitative study using in‐depth interviews. We selected a qualitative methodology, since behaviors, social norms, and interpersonal interactions can be most appropriately examined using qualitative methods.17, 18 In addition, we used a positive deviance approach,19 where we focused on hospitals with top performance and the most improvement in door‐to‐balloon times. We sampled from hospitals in the National Registry of Myocardial Infarction (NRMI) who perform percutaneous coronary intervention (PCI, n = 151). We selected hospitals whose median door‐to‐balloon times were 90 minutes (n = 35). Then, we ranked hospitals in descending order according to their improvement during the previous 3 years (19992002). We sampled hospitals in descending order until we reached theoretical saturation where, as recommended for qualitative inquiry,2022 additional site visits did not uncover new concepts or patterns regarding our study questions. All sampled hospitals agreed to participate.

The first contact at each hospital was typically the director of QI. We asked to interview anyone with substantial involvement in the effort to reduce door‐to‐balloon times, and suggested that a wide variety of disciplines and roles be represented. We also used the snowball technique,22 where we asked participants to provide the names of individuals with substantial involvement in the reducing door‐to‐balloon times. Participants had varied levels of participation in QI teams. We purposely asked for minority and dissenting views from all participants.

At least 2 members of the research team conducted in‐depth interviews during hospital site visits. Interviews were conducted individually or in small groups, and lasted 1 to 1.5 hours. All data were audiotaped after verbal consent. Our interviews began with the grand tour question: What, if anything, has this hospital done to reduce its door‐to‐balloon times for patients with STEMI? The research team used standardized probes20, 23 to guide the discussion and achieve a complete understanding of the phenomena under study, including leadership and activities of the QI teams, and recommendations to other hospitals that wished to reduce door‐to‐balloon times. As recommended by experts,23 our interview guide was purposefully open‐ended to capture the range of experiences with QI teams. We did not specifically probe for facilitating or challenging behaviors. Audiotapes were transcribed by an independent, professional transcriptionist.

For this analysis, we defined QI teams as groups of administrators, providers, and staff who designed, implemented, and monitored processes to reduce door‐to‐balloon times. Each analysis team member independently cataloged quotes about team behaviors using a list of concepts (or codes). We then analyzed the quotes to identify recurrent themes relevant to the behaviors of interdisciplinary QI teams. We used the constant comparative method of analysis,20, 24, 25 which stipulates that the initial list of codes is refined as new transcripts are analyzed, and the final list is applied to all the transcripts. The analysis team included experts in QI, medicine, qualitative and health services research, as well as organizational psychology, and one of the interviewers. The presence of diverse perspectives in the analysis team,21 and a detailed audit trail20 to document the emergence of codes and themes, helped enhance researcher neutrality, data accuracy, and validity. We used Atlas.ti version 5.2 (Scientific Software Development GMbH, Berlin, Germany) to assist in the analysis.

RESULTS

Our sample (n = 11) included hospitals that varied on several characteristics (eg, geographic location), and median door‐to‐balloon times ranged from 55.5 to 89.5 minutes (Table 1). Hospitals in our sample had higher mean improvements in door‐to‐balloon times compared with non‐sampled NRMI hospitals (n = 140, 24 minutes vs 3 minutes over 3 years). Our interview participants (n = 122) included physicians, nurses, QI personnel, and administrative staff (Table 2). Five behaviors emerged from the data analysis. We found that interdisciplinary QI teams in successful hospitals focused on: (1) motivating involved hospital staff towards a shared goal, (2) creating opportunities for learning and problem‐solving, (3) addressing the impact of changes in care processes on staff, (4) protecting the integrity of the newly developed care processes, and (5) representing each involved clinical discipline effectively. These behaviors were recurrent across our diverse set of hospitals.

Discussion

We identified 5 behaviors of successful interdisciplinary QI teams based on our analysis of hospitals that reduced door‐to‐balloon times for patients with STEMI. These QI teams: (1) motivated involved hospital staff to consider lowering door‐to‐balloon times, a shared goal, (2) created opportunities for learning and problem‐solving, (3) addressed the impact of changes to care processes for patients with STEMI on staff, (4) protected the integrity of new care processes, and (5) represented each clinical discipline effectively by having members with in‐depth knowledge and authority.

Experts suggest that the key elements of effective teamwork in healthcare include prioritizing team over individual goals, mutual understanding, leadership, adaptability, and anticipation of the needs of others.26 These elements are supported by mutual trust and closed‐loop communication. The behaviors of QI teams in our study represent adaptive responses to the unique demands of QI in a complex organization. These teams went beyond an improvement model of identifying and analyzing a problem, and then developing and testing solutions by: (1) motivating and gathering information from each discipline, regardless of interdisciplinary conflicts; (2) responding to the concerns of front‐line staff, while maintaining control over the improvement process; and (3) sharing information across the hospital hierarchy. Table 3 illustrates potential relationships between the team behaviors in our data, the demands on hospital QI teams, and known elements of effective teamwork.

The behaviors in our study suggest effective teamwork strategies for QI. For example, our data suggest that successful interdisciplinary QI teams need effective representation from each involved discipline. This representation is necessary for motivation of front‐line staff, gathering of detailed information about processes, and the effective implementation of changes. Although this level of representation might challenge the cohesiveness of some teams,27 the teams in our sample managed conflict among disciplines without sacrificing the shared goal. By allocating attention and resources to the concerns of each discipline, the teams we studied prioritized team over individual goals and promoted mutual understanding.

Similarly, deciding when to modify the new protocols required leadership, adaptability, and anticipation of the needs of others. Successful QI teams in our sample modified protocols based on data and feedback, and created the mutual trust environment that is known to facilitate learning among disciplines.2830 Their willingness to learn, however, did not deter teams from protecting the integrity of new protocols. Lastly, participants stressed the importance of managing information across hierarchical boundaries. Managing reliable, timely, and accurate information across all levels is crucial to teamwork, and to the power and influence of a team.31

Our conclusions should be interpreted in light of several limitations. First, our study did not include a comparison group of low‐performing hospitals. We followed the recommendations of qualitative research experts23 who recommend sampling those with the most information on, and experience with, the phenomena under study (QI teams in high‐performing hospitals). The hypotheses we present here require further testing in quantitative studies of hospitals with diversity in QI team outcomes. Second, it is possible that sampled participants favored responses that they considered more desirable. To minimize this bias, we interviewed multiple participants per hospital, assured their confidentiality, and asked them to elaborate their responses. We sampled participants with a wide range of clinical and operational roles in each hospital, and also used the snowball sampling method to augment our sample. The range of responses collected, including frank discussions about setbacks, argues against the existence of contrasting behaviors to those captured. Third, although our sample included hospitals of various size and location, our findings might not reflect those of a larger sample of US hospitals. Last, the behaviors of QI teams may differ for other clinical processes.

Translating these findings into practice will require future studies of the impact of QI team behaviors on sustainability of quality gains. Since QI teams are not typically permanent, additional research is needed to identify behaviors associated with sustainable improvements. In addition, we must test whether the relationship between behaviors and team outcomes depends on whether the QI team strives to reach an evidence‐based goal or to improve a process as much as possible. Our sample demonstrated a combined approach, where the evidence‐based goal was followed by a desire to continue to further reduce door‐to‐balloon times. Similarly, the relationship between behaviors and team outcomes might depend on the catalyst for improvement (eg, regulatory pressure, an adverse event). The confluence of strong evidence and regulatory pressure that fueled these teams might not be true for other measures. Lastly, studies of teamwork in QI teams will require objective measures of team behaviors. A combination of surveys and direct team observation will likely be required to measure these behaviors, especially effective representation.

Our study highlights behaviors common to successful interdisciplinary QI teams in high‐performing hospitals. Previous studies have identified elements of teamwork and the importance of teams to QI, but have not examined team behaviors. In the era of an ever‐growing list of quality measures and of movement toward performance‐based reimbursement models,3234 hospitals have embraced the use of interdisciplinary teams as a key component of QI efforts. Our findings suggest that hospitals could enhance QI team effectiveness by promoting behaviors associated with successful interdisciplinary teams. When applied to QI teams, teamwork training could be supplemented with knowledge, attitudes, and skills regarding information‐gathering, problem‐solving, and communication across disciplines and levels of the hospital hierarchy.