Readmission Rates After Passage of the Hospital Readmissions Reduction Program

Passage of the Patient Protection and Affordable Care Act (ACA) in March 2010 created the Medicare Hospital Readmissions Reduction Program (HRRP), which introduced the prospect of financially penalizing hospitals (1, 2) based on their previous performance. Performance was assessed on the basis of readmission rates within 30 days after hospitalizations for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia. These metrics were publicly reported on Medicare's Hospital Compare Web site starting on 9 July 2009 (3) and applied to reimbursements starting on 1 October 2012 (2). Performance during a period starting in 2009 and extending after passage of the law was the basis for the penalties administered in 2012 (Figure 1). Figure 1. Timeline of Medicare public reporting, passage of health reform, and financial penalties. CMS= Centers for Medicare & Medicaid Services; HRRP= Hospital Readmissions Reduction Program. This program represents one of several efforts to encourage provider organizations to enhance safety and value. Medicare has reported lower readmission rates since passage of the law (4, 5), but whether the lowest-performing hospitals might experience less rapid improvement than hospitals that are already performing well, due to being underresourced or serving vulnerable populations, has been questioned (6). In the context of this uncertainty, we analyzed time-series data on all-cause and condition-specific 30-day risk-standardized readmission rates (RSRRs) for Medicare fee-for-service enrollees from 2000 to 2013 to answer 2 questions. First, we sought to confirm that hospital RSRRs for AMI, CHF, and pneumonia decreased after passage of the ACA compared with any preexisting trends in improvement before passage of the law. Second, we determined whether the acceleration of improvement in readmission rates after passage of the law was greater in the lowest-performing hospitals than in higher-performing hospitals. Methods Study Population We queried the Medicare Provider Analysis and Review files to identify all Medicare fee-for-service beneficiaries aged 65 years or older who were discharged alive from an acute care hospital in the United States after hospitalization for AMI, CHF, or pneumonia from 1 January 2000 through 30 November 2013. Hospitalizations for AMI, CHF, or pneumonia were defined as a principal discharge diagnosis of an International Classification of Diseases, Ninth Revision, Clinical Modification code for those conditions. These codes are used for public reporting and financial penalties (7, 8). We restricted the sample to patients who were enrolled in the fee-for-service plan for at least 12 months before an index hospitalization and continued enrollment in the plan for at least 1 month after the index discharge. Patient Characteristics and Comorbidities Patient demographic information included age; sex; and major comorbidities, including 7 cardiovascular history variables and 14 variables representing other comorbidities. Clinical variables were obtained from secondary diagnosis codes in the index condition-specific hospitalization as well as from principal and secondary diagnosis codes from all hospitalizations for 12 months before the index hospitalization. These comorbidities were classified using the Hierarchical Condition Categories method (9). Inpatient claims from 1999 were used to obtain comorbidities for patients hospitalized in 2000. Hospital Characteristics and Penalty Information Hospital characteristics included teaching status (teaching vs. nonteaching); Joint Commission certification (yes/no); hospital geographic location (urban vs. rural); ownership (public or private not-for-profit); number of beds; mean age of Medicare fee-for-service admitted patients; percentage of admitted patients who were white, black, and female; median hospital volume; 30-day mortality and readmission rates; and percentage of admissions for dual-eligible patients (those eligible for both Medicare and Medicaid). We obtained publicly available hospital penalty information (2). The Centers for Medicare & Medicaid Services (CMS) combined the hospital-specific diagnosis-related group payments and hospital-specific excess readmission ratios for AMI, CHF, and pneumonia to determine penalties (2). In fiscal year 2013, the maximum penalty was 1% of total Medicare payments. We used penalties for fiscal year 2013, the first year of the penalties. For this analysis, we divided hospitals into 4 groups based on the size of the penalty: highest-performing (0% penalty), average-performing (>0% and <0.50%), low-performing (0.50% and <0.99%), and lowest-performing (0.99%). We excluded hospitals not in the CMS HRRP penalty list; many of these hospitals were small and were acquired by other hospitals during our study period (10). Only hospitals that existed during the entire period were included in this analysis. Outcomes The primary outcomes were hospital-specific, 30-day, all-cause RSRRs for patients discharged with AMI, CHF, or pneumonia. All-cause readmissions were defined as 1 or more hospitalizations in any acute care hospital in the United States for any cause within 30 days of discharge from an index hospitalization. For the index hospitalization, patients transferred from 1 acute care hospital to another were considered to have 1 episode of care, attributed to the last hospital in the transfer chain. To permit complete 30-day follow-up, we used December 2013 inpatient data to obtain readmission information for patients discharged in November 2013. Using the CMS risk-standardized method for profiling hospitals (7, 8, 11), for each hospital in each quarter, we estimated 30-day all-cause RSRRs after hospitalizations for AMI, CHF, or pneumonia. Briefly, we fitted a hierarchical generalized linear model with a logit link function and hospital-specific random intercepts to model 30-day all-cause readmissions as a function of patients' age, sex, and comorbidities described previously. This model accounts for patient case mix and permits hospital-level random intercepts. We also calculated a combined RSRR that reflected readmissions after hospitalizations for any of the 3 conditions. Definition of the Intervention Point The ACA was signed into law on 23 March 2010, but because readmissions are categorized by quarter, we considered the end of the first quarter of 2010 (31 March) as the time point of intervention. We chose the actual enactment of the ACAa law that included the HRRPas the time point of intervention rather than the start of financial penalties for 2 reasons. First, at the time the law was passed, hospitals became aware of impending penalties and began implementing administrative changes, including redesigns of care processes, in anticipation of the penalties. Second, because readmission rates at the time of passage of the law would influence future penalties, hospitals were incentivized to reduce readmissions starting in 2010 (Figure 1). Although we made our decision before we examined the data, visual inspection of the data confirmed an inflection point in readmission rates occurring at the time the law was passed. To assess the effect of the law's passage, we defined a prelaw period of 40 quarters before passage of the law (1 January 2000 to 31 March 2010) and a postlaw period of 15 quarters after passage of the law (1 April 2010 to 31 December 2013). The span of the prelaw period was also varied in a sensitivity analysis (see Sensitivity Analysis section). Statistical Analysis In the main analysis, we used a 2-stage approach to test the following 2 hypotheses: 1) hospital-specific RSRRs have improved over time in the postlaw period, after control for any prelaw preexisting secular trends in each of the 4 performance groups; and 2) any increase in the rate of reduction in hospital RSRRs (postlaw vs. prelaw period) was greater for hospitals with lower performance than for those with higher performance. Specifically, at the first stage of the hierarchical model, we specified and fitted a logistic regression to patient-level data to estimate the RSRRs for each quarter. At the second stage, we specified and fitted a piecewise linear model with a change point placed at the time of passage of the ACA (the intervention point, between the prelaw and postlaw periods), with the estimated RSRRs from the first stage as the dependent variable (trend model). The equation is defined mathematically and represented visually in the Supplement. For the trend models, we used generalized estimating equations with robust SEs and an autoregressive correlation structure. Unless otherwise specified, we report both the absolute proportion of readmitted patients and the rate of change in the RSRRs over time as the number of readmissions per 10000 discharges per year. Supplement. Additional Information Sensitivity Analysis The piecewise model described earlier was fitted to the estimated RSRRs, ignoring that RSRRs may be more or less precise depending on hospital volume. Therefore, in a sensitivity analysis, we refitted our piecewise model weighted by hospital volume for each disease condition. We also adjusted for hospital characteristics in the piecewise linear models to control for hospital differences among performance categories. In a third sensitivity analysis, we performed the original statistical analysis using 15 quarters as the prelaw period, as opposed to the 40-quarter prelaw period used in the primary analysis. To assess whether our findings were sensitive to the performance group definitions, we repeated the analyses after recategorizing the performance categories into equally sized quartiles. We repeated the piecewise models using hospital-unadjusted readmission rates instead of the RSRRs as the dependent variable to detect any effect of the risk-adjustment methods on our main findings. Finally, we repeated the analysis using a full hierarchical model, with a logistic regression for pati