Characteristics of Hospitals Receiving Penalties Under the Hospital Readmissions Reduction Program

Cardiovascular Division, Brigham and Women’s Hospital, Boston, Massachusetts, USA.
JAMA The Journal of the American Medical Association (Impact Factor: 30.39). 01/2013; 309(4):342-3. DOI: 10.1001/jama.2012.94856
Source: PubMed
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    ABSTRACT: Background Early hospital readmissions, defined as rehospitalization within 30 days from a previous discharge, represent an economic and social burden for public health management. As data about early readmission in Italy are scarce, we aimed to relate the phenomenon of 30-day readmission to factors identified at the time of emergency department (ED) visits in subjects admitted to medical wards of a general hospital in Italy.Methods We performed a retrospective 30-month observational study, evaluating all patients admitted to the Department of Medicine of the Hospital of Ferrara, Italy. Our study compared early and late readmission: patients were evaluated on the basis of the ED admission diagnosis and classified differently on the basis of a concordant or discordant readmission diagnosis in respect to the diagnosis of a first hospitalization.ResultsOut of 13,237 patients admitted during the study period, 3,631 (27.4%) were readmitted; of those, 656 were 30-day rehospitalizations (5% of total admissions). Early rehospitalization occurred 12 days (median) later than previous discharge. The most frequent causes of rehospitalization were cardiovascular disease (CVD) in 29.3% and pulmonary disease (PD) in 29.7% of cases. Patients admitted with the same diagnosis were younger, had lower length of stay (LOS) and higher prevalence of CVD, PD and cancer. Age, CVD and PD were independently associated with 30-day readmission with concordant diagnosis and kidney disease with 30-day rehospitalization with a discordant diagnosis.Conclusions Comorbid patients are at higher risk for 30-day readmission. Reduction of LOS, especially in elderly subjects, could increase early rehospitalization rates.
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    ABSTRACT: The Hospital Readmissions Reduction Program incentivizes hospitals to reduce early readmissions for heart failure (HF), acute myocardial infarction (AMI), and pneumonia (PNE). To examine the contribution of each diagnosis to readmissions penalty size, data were obtained from the Center for Medicare and Medicaid Services, American Hospital Association and United States Census Bureau including number of cases; Readmissions Payment Adjustment Factor (values <1 indicate a penalty for excess readmissions); Excess Readmission Ratio (ERR or ratio of adjusted predicted readmission based on comorbidities, frailty and individual patient demographics to expected probability of readmission at an average hospital) for each diagnosis; hospital teaching status; bed number; and zip code socioeconomic status. Of 2228 hospitals with ≥25 cases per diagnosis, 1636 received a penalty. Univariate correlation coefficients between penalty and ERR were -0.66, -0.61 and -0.43 for HF, PNE, and MI respectively (all P<0.001). Correlation between ERRs was greatest for PNE and HF (0.30, P<0.001) and weakest for PNE and MI (0.12, P<0.001). In regression analyses, the HF ERR explained the most variance in the penalty (R(2) range=0.20-0.43). HF ERR but not the number of cases is related to penalty magnitude. These findings have implications for the design of hospital-based quality initiatives regarding readmissions. Copyright © 2014 Elsevier Inc. All rights reserved.
    Journal of Cardiac Failure 12/2014; 21(2). DOI:10.1016/j.cardfail.2014.12.002 · 3.07 Impact Factor
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    ABSTRACT: A disparity between evidence and practice in the management of ischemic heart disease is frequently observed. Guideline adherence and clinical outcomes are influenced by system, provider, and patient factors. Recently, performance improvement measures for cardiovascular disease have gained a lot of popularity worldwide. These measures may facilitate the uptake of evidence-based recommendations and improve patient outcomes. While apparently valid as quality metrics, their impacts on clinical outcomes remain limited and are areas of further research. Several methods for optimizing performance have been instituted and essentially involve three different approaches-improvement in the reporting of data on guideline adherence, providing infrastructure and tools, and providing incentives to improve guideline adherence. Public reporting of quality metrics and "pay-for-performance" are some novel performance improvement tools. The impact of these approaches on patient outcomes will be pivotal in improving cardiovascular outcomes in the future.
    Current Cardiology Reports 02/2015; 17(2):551. DOI:10.1007/s11886-014-0551-y