Hospital Readmission Performance and Patterns of Readmission
Hospital Readmission Performance and Patterns of Readmission
In contemporary practice, hospitals with different 30 day readmission rates after index admissions for heart failure, acute myocardial infarction, or pneumonia have a similar distribution of readmissions with regard to their diagnoses and timing. Our national study examined readmission diagnoses and timing patterns across hospitals with different performance profiles. The findings extend previous work on the predictors of hospital performance by showing that high performing hospitals with low 30 day risk standardized readmission rates maintain a similar pattern of readmission diagnoses and timing as lower performing institutions. High performers have not empirically achieved low overall rates of readmission by reducing readmissions from specific diagnoses or time periods after discharge. As readmissions result from a diverse spectrum of conditions and occur throughout the month after admission, our results suggest that hospitals might best achieve low 30 day readmission rates by using general strategies or capacities that apply broadly across potential readmission diagnoses and time periods after discharge.
We have extended previous research describing the diverse spectrum of readmission diagnoses by showing that medical conditions that cause readmission remain similar across hospitals with different characteristics. For example, in multivariate analysis we found that the percentage of readmissions for each of the 10 most common readmission diagnoses in heart failure, acute myocardial infarction, and pneumonia cohorts varied by less than 1% at safety net hospitals compared with non-safety net hospitals. We additionally found that every standard deviation increase in hospitals' percentage of patients from ethnic minority groups and patients with Medicaid health insurance resulted in similarly small changes in the percentage of readmissions for common readmission diagnoses. These results show that hospitals serving minority groups have similar readmission patterns, despite having higher overall rates of readmission. Strategies to reduce readmission could therefore account for a similar underlying spectrum of readmission diagnoses across many different care settings.
Our findings could explain why the combination of broad based and longitudinal strategies applicable to a range of potential readmission diagnoses throughout the period after discharge have, in intervention studies, shown efficacy in lowering hospital readmissions while disease specific or time limited interventions have been often unsuccessful. Rich and colleagues showed that complementary interventions delivered by a nurse, dietician, social worker, and geriatrician in the hospital as well as longitudinal follow-up with home care and study teams reduced readmissions after hospital admission for heart failure. Similarly, Coleman and colleagues found that the combination of inpatient visits and visits after discharge by a nurse transition coach, assistance with self management of drug treatment, education about potential "red flag" conditions that require expedited follow-up, and the creation of a patient owned medical record designed to bridge care settings lowered readmission in patients with 11 common conditions.
Our findings could also explain why disease specific or time limited interventions have generally been found to be less efficacious, as the highest performing hospitals were not especially good at reducing readmissions for particular medical conditions or time periods after discharge. The wide breadth of diagnoses and time periods associated with readmission implies that overall rates of readmission cannot be reduced by lowering only a small subset of readmissions. It is therefore not surprising that major trials of disease specific or singular interventions after discharge have had disappointing results for all cause readmission. In particular, well designed trials of telemonitoring to identify early evidence of heart failure decompensation have not shown reduced rates of all cause readmission, despite involving highly engaged patients and healthcare providers.
Our results are also consistent with those of previous studies linking high hospital performance with organizational and cultural characteristics that would be expected to apply broadly to a range of conditions and times after hospital admission. Curry and colleagues conducted a qualitative analysis to identify potential drivers of low risk standardized mortality rates for acute myocardial infarction and found that high and low performing hospitals were not differentiated by disease specific protocols or processes of care but rather by shared organizational values of providing high quality care, senior managerial provision of financial and non-financial resources, multidisciplinary teams with empowered non-physician providers, strong internal communication and coordination, and commitment to problem solving. The presence of organizational characteristics with the potential to exert broad influence across readmission diagnoses and time periods after discharge among high performing hospitals was confirmed in a cross sectional survey of more than 500 hospitals.
The reasons underlying the greater percentage of readmissions for recurrent acute myocardial infarction and recurrent pneumonia at rural and critical access hospitals are unknown. As these hospitals are less likely to have catheterization facilities, patients treated there for acute myocardial infarction might be less likely to undergo revascularization and more likely to experience reinfarction. Rural hospitals might also less often follow acute myocardial infarction process measures. The reasons for greater recurrence of pneumonia at rural hospitals are less clear but could relate to worse performance on evidence based care processes, differences in use of resources after acute care, or greater difficulties accessing care. Despite these uncertainties, the different composition of readmission diagnoses at these facilities could be useful in guiding disease surveillance after hospital discharge.
Our finding of similar patterns of readmission regardless of hospital readmission performance could imply deficiencies in the Centers for Medicare and Medicaid Services algorithms we used to calculate risk standardized readmission rates. Differences in hospital performance could fundamentally reflect differences in social and environmental factors experienced by patients at high and low performing hospitals rather than intrinsic signals of hospital quality. There is, however, controversy about the importance of social and environmental factors at the hospital level. The predictive power of these factors at the patient level has been inconsistent. Even clinical factors that describe illness severity at the time of admission, such as vital signs and results of laboratory testing, have not been shown to provide substantial incremental value in predicting rates of hospital readmission compared with administrative claims data.
In contrast, there is evidence that hospitals and hospital organizational characteristics can impact rates of readmission. For example, between 1997 and 2010 Veterans Affairs hospitals significantly lowered 30 day readmission rates for heart failure, acute myocardial infarction, pneumonia, and other common conditions. Moreover, hospitals that care for older adults lowered 30 day readmission rates in the last quarter of 2012 in the face of new financial penalties designed to reduce readmission. In addition, many hospitals with a high percentage of medically underserved patients from either racial and ethnic minority groups or low socioeconomic strata nonetheless have low risk standardized readmission rates. High performing hospitals might possess particular organizational characteristics and capacities that are responsible for their low rates of adverse outcomes and are consistent with organizational performance goals for the highest performing healthcare organizations.
There are potential limitations to this analysis. We restricted our study to beneficiaries of Medicare fee for service health insurance at hospitals with more than 25 index admissions during the study period, so conclusions drawn from this population might not apply to others such as younger patients, patients admitted with different conditions, or patients at the smallest hospitals. We also did not include information from hospitals with no readmissions during the study period, which included 708 hospitals (15.9% of all hospitals) treating acute myocardial infarction and 313 hospitals (6.5% of all hospitals) treating pneumonia. Most of these hospitals had small case volumes as the median number of admissions for acute myocardial infarction and pneumonia at these institutions was three and 30, respectively. Only 79 hospitals (1.8% of all hospitals) caring for patients with acute myocardial infarction and 162 hospitals (3.4% of hospitals) caring for patients with pneumonia had more than 25 index admissions and no readmissions during the study period. More than 93% of these "super performing" hospitals for acute myocardial infarction and 96% of these "super performing" hospitals for pneumonia did not submit survey data to the American Hospital Association and are therefore difficult to characterize. Study conclusions might therefore not apply to hospitals not participating in the annual survey of the American Hospital Association. This study of more than 600,000 hospital readmissions from more than 4,000 hospitals, however, is the largest of its kind and shows consistent findings across the most common cardiopulmonary conditions. Though we relied on claims data to assign diagnoses to admissions, administrative codes have been validated for cardiovascular and pulmonary diagnoses. We did not exclude all potential planned readmissions such as those for treatment with chemotherapy or radiotherapy. However, we did not expect these elective admissions to be common soon after incident heart failure, myocardial infarction, or major infection such as pneumonia. We did exclude readmissions for revascularization after admission for acute myocardial infarction. Lastly, there might be concerns that the hierarchical modeling approach used by the Centers for Medicare and Medicaid Services obscures relations because findings are weighted by hospital volume, thereby making it less likely that small hospitals are considered high or low performers. This approach, however, was approved by the National Quality Forum and an independent committee of statisticians to reduce the likelihood of finding spurious results based on small sample volumes at certain hospitals. Moreover, we observed a substantial range of readmissions with this method. Our goal was not to further validate the readmission performance algorithms created by the Centers for Medicare and Medicaid Services but to understand how differences in hospitals’ risk standardized readmission rates relate to readmission patterns.
Discussion
Principal Findings
In contemporary practice, hospitals with different 30 day readmission rates after index admissions for heart failure, acute myocardial infarction, or pneumonia have a similar distribution of readmissions with regard to their diagnoses and timing. Our national study examined readmission diagnoses and timing patterns across hospitals with different performance profiles. The findings extend previous work on the predictors of hospital performance by showing that high performing hospitals with low 30 day risk standardized readmission rates maintain a similar pattern of readmission diagnoses and timing as lower performing institutions. High performers have not empirically achieved low overall rates of readmission by reducing readmissions from specific diagnoses or time periods after discharge. As readmissions result from a diverse spectrum of conditions and occur throughout the month after admission, our results suggest that hospitals might best achieve low 30 day readmission rates by using general strategies or capacities that apply broadly across potential readmission diagnoses and time periods after discharge.
Extension of Previous Research
We have extended previous research describing the diverse spectrum of readmission diagnoses by showing that medical conditions that cause readmission remain similar across hospitals with different characteristics. For example, in multivariate analysis we found that the percentage of readmissions for each of the 10 most common readmission diagnoses in heart failure, acute myocardial infarction, and pneumonia cohorts varied by less than 1% at safety net hospitals compared with non-safety net hospitals. We additionally found that every standard deviation increase in hospitals' percentage of patients from ethnic minority groups and patients with Medicaid health insurance resulted in similarly small changes in the percentage of readmissions for common readmission diagnoses. These results show that hospitals serving minority groups have similar readmission patterns, despite having higher overall rates of readmission. Strategies to reduce readmission could therefore account for a similar underlying spectrum of readmission diagnoses across many different care settings.
Implications for Reducing Readmissions
Our findings could explain why the combination of broad based and longitudinal strategies applicable to a range of potential readmission diagnoses throughout the period after discharge have, in intervention studies, shown efficacy in lowering hospital readmissions while disease specific or time limited interventions have been often unsuccessful. Rich and colleagues showed that complementary interventions delivered by a nurse, dietician, social worker, and geriatrician in the hospital as well as longitudinal follow-up with home care and study teams reduced readmissions after hospital admission for heart failure. Similarly, Coleman and colleagues found that the combination of inpatient visits and visits after discharge by a nurse transition coach, assistance with self management of drug treatment, education about potential "red flag" conditions that require expedited follow-up, and the creation of a patient owned medical record designed to bridge care settings lowered readmission in patients with 11 common conditions.
Our findings could also explain why disease specific or time limited interventions have generally been found to be less efficacious, as the highest performing hospitals were not especially good at reducing readmissions for particular medical conditions or time periods after discharge. The wide breadth of diagnoses and time periods associated with readmission implies that overall rates of readmission cannot be reduced by lowering only a small subset of readmissions. It is therefore not surprising that major trials of disease specific or singular interventions after discharge have had disappointing results for all cause readmission. In particular, well designed trials of telemonitoring to identify early evidence of heart failure decompensation have not shown reduced rates of all cause readmission, despite involving highly engaged patients and healthcare providers.
Our results are also consistent with those of previous studies linking high hospital performance with organizational and cultural characteristics that would be expected to apply broadly to a range of conditions and times after hospital admission. Curry and colleagues conducted a qualitative analysis to identify potential drivers of low risk standardized mortality rates for acute myocardial infarction and found that high and low performing hospitals were not differentiated by disease specific protocols or processes of care but rather by shared organizational values of providing high quality care, senior managerial provision of financial and non-financial resources, multidisciplinary teams with empowered non-physician providers, strong internal communication and coordination, and commitment to problem solving. The presence of organizational characteristics with the potential to exert broad influence across readmission diagnoses and time periods after discharge among high performing hospitals was confirmed in a cross sectional survey of more than 500 hospitals.
The reasons underlying the greater percentage of readmissions for recurrent acute myocardial infarction and recurrent pneumonia at rural and critical access hospitals are unknown. As these hospitals are less likely to have catheterization facilities, patients treated there for acute myocardial infarction might be less likely to undergo revascularization and more likely to experience reinfarction. Rural hospitals might also less often follow acute myocardial infarction process measures. The reasons for greater recurrence of pneumonia at rural hospitals are less clear but could relate to worse performance on evidence based care processes, differences in use of resources after acute care, or greater difficulties accessing care. Despite these uncertainties, the different composition of readmission diagnoses at these facilities could be useful in guiding disease surveillance after hospital discharge.
Social and Hospital Factors in Readmission
Our finding of similar patterns of readmission regardless of hospital readmission performance could imply deficiencies in the Centers for Medicare and Medicaid Services algorithms we used to calculate risk standardized readmission rates. Differences in hospital performance could fundamentally reflect differences in social and environmental factors experienced by patients at high and low performing hospitals rather than intrinsic signals of hospital quality. There is, however, controversy about the importance of social and environmental factors at the hospital level. The predictive power of these factors at the patient level has been inconsistent. Even clinical factors that describe illness severity at the time of admission, such as vital signs and results of laboratory testing, have not been shown to provide substantial incremental value in predicting rates of hospital readmission compared with administrative claims data.
In contrast, there is evidence that hospitals and hospital organizational characteristics can impact rates of readmission. For example, between 1997 and 2010 Veterans Affairs hospitals significantly lowered 30 day readmission rates for heart failure, acute myocardial infarction, pneumonia, and other common conditions. Moreover, hospitals that care for older adults lowered 30 day readmission rates in the last quarter of 2012 in the face of new financial penalties designed to reduce readmission. In addition, many hospitals with a high percentage of medically underserved patients from either racial and ethnic minority groups or low socioeconomic strata nonetheless have low risk standardized readmission rates. High performing hospitals might possess particular organizational characteristics and capacities that are responsible for their low rates of adverse outcomes and are consistent with organizational performance goals for the highest performing healthcare organizations.
Study Limitations
There are potential limitations to this analysis. We restricted our study to beneficiaries of Medicare fee for service health insurance at hospitals with more than 25 index admissions during the study period, so conclusions drawn from this population might not apply to others such as younger patients, patients admitted with different conditions, or patients at the smallest hospitals. We also did not include information from hospitals with no readmissions during the study period, which included 708 hospitals (15.9% of all hospitals) treating acute myocardial infarction and 313 hospitals (6.5% of all hospitals) treating pneumonia. Most of these hospitals had small case volumes as the median number of admissions for acute myocardial infarction and pneumonia at these institutions was three and 30, respectively. Only 79 hospitals (1.8% of all hospitals) caring for patients with acute myocardial infarction and 162 hospitals (3.4% of hospitals) caring for patients with pneumonia had more than 25 index admissions and no readmissions during the study period. More than 93% of these "super performing" hospitals for acute myocardial infarction and 96% of these "super performing" hospitals for pneumonia did not submit survey data to the American Hospital Association and are therefore difficult to characterize. Study conclusions might therefore not apply to hospitals not participating in the annual survey of the American Hospital Association. This study of more than 600,000 hospital readmissions from more than 4,000 hospitals, however, is the largest of its kind and shows consistent findings across the most common cardiopulmonary conditions. Though we relied on claims data to assign diagnoses to admissions, administrative codes have been validated for cardiovascular and pulmonary diagnoses. We did not exclude all potential planned readmissions such as those for treatment with chemotherapy or radiotherapy. However, we did not expect these elective admissions to be common soon after incident heart failure, myocardial infarction, or major infection such as pneumonia. We did exclude readmissions for revascularization after admission for acute myocardial infarction. Lastly, there might be concerns that the hierarchical modeling approach used by the Centers for Medicare and Medicaid Services obscures relations because findings are weighted by hospital volume, thereby making it less likely that small hospitals are considered high or low performers. This approach, however, was approved by the National Quality Forum and an independent committee of statisticians to reduce the likelihood of finding spurious results based on small sample volumes at certain hospitals. Moreover, we observed a substantial range of readmissions with this method. Our goal was not to further validate the readmission performance algorithms created by the Centers for Medicare and Medicaid Services but to understand how differences in hospitals’ risk standardized readmission rates relate to readmission patterns.