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      Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence

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          Abstract

          Introduction

          Hospital readmission rates are increasingly used for both quality improvement and cost control. However, the validity of readmission rates as a measure of quality of hospital care is not evident. We aimed to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of care.

          Methods

          We conducted a systematic literature review, using the bibliographic databases Embase, Medline OvidSP, Web-of-Science, Cochrane central and PubMed for the period of January 2001 to May 2013.

          Results

          The search resulted in 102 included papers. We found that definition of the context in which readmissions are used as a quality indicator is crucial. This context includes the patient group and the specific aspects of care of which the quality is aimed to be assessed. Methodological flaws like unreliable data and insufficient case-mix correction may confound the comparison of readmission rates between hospitals. Another problem occurs when the basic distinction between planned and unplanned readmissions cannot be made. Finally, the multi-faceted nature of quality of care and the correlation between readmissions and other outcomes limit the indicator's validity.

          Conclusions

          Although readmission rates are a promising quality indicator, several methodological concerns identified in this study need to be addressed, especially when the indicator is intended for accountability or pay for performance. We recommend investing resources in accurate data registration, improved indicator description, and bundling outcome measures to provide a more complete picture of hospital care.

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          Most cited references84

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          Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission.

          In 2009, the Centers for Medicare & Medicaid Services is publicly reporting hospital-level risk-standardized 30-day mortality and readmission rates after acute myocardial infarction (AMI) and heart failure (HF). We provide patterns of hospital performance, based on these measures. We calculated the 30-day mortality and readmission rates for all Medicare fee-for-service beneficiaries ages 65 years or older with a primary diagnosis of AMI or HF, discharged between July 2005 and June 2008. We compared weighted risk-standardized mortality and readmission rates across Hospital Referral Regions and hospital structural characteristics. The median 30-day mortality rate was 16.6% for AMI (range, 10.9% to 24.9%; 25th to 75th percentile, 15.8% to 17.4%; 10th to 90th percentile, 14.7% to 18.4%) and 11.1% for HF (range, 6.6% to 19.8%; 25th to 75th percentile, 10.3% to 12.0%; 10th to 90th percentile, 9.4% to 13.1%). The median 30-day readmission rate was 19.9% for AMI (range, 15.3% to 29.4%; 25th to 75th percentile, 19.5% to 20.4%; 10th to 90th percentile, 18.8% to 21.1%) and 24.4% for HF (range, 15.9% to 34.4%; 25th to 75th percentile, 23.4% to 25.6%; 10th to 90th percentile, 22.3% to 27.0%). We observed geographic differences in performance across the country. Although there were some differences in average performance by hospital characteristics, there were high and low hospital performers among all types of hospitals. In a recent 3-year period, 30-day risk-standardized mortality rates for AMI and HF varied among hospitals and across the country. The readmission rates were particularly high.
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            Variation in surgical-readmission rates and quality of hospital care.

            Reducing hospital-readmission rates is a clinical and policy priority, but little is known about variation in rates of readmission after major surgery and whether these rates at a given hospital are related to other markers of the quality of surgical care. Using national Medicare data, we calculated 30-day readmission rates after hospitalization for coronary-artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement. We used bivariate and multivariate techniques to assess the relationships between readmission rates and other measures of surgical quality, including adherence to surgical process measures, procedure volume, and mortality. For the six index procedures, there were 479,471 discharges from 3004 hospitals. The median risk-adjusted composite readmission rate at 30 days was 13.1% (interquartile range, 9.9 to 17.1). In a multivariate model adjusting for hospital characteristics, we found that hospitals in the highest quartile for surgical volume had a significantly lower composite readmission rate than hospitals in the lowest quartile (12.7% vs. 16.8%, P<0.001), and hospitals with the lowest surgical mortality rates had a significantly lower readmission rate than hospitals with the highest mortality rates (13.3% vs. 14.2%, P<0.001). High adherence to reported surgical process measures was only marginally associated with reduced readmission rates (highest quartile vs. lowest quartile, 13.1% vs. 13.6%; P=0.02). Patterns were similar when each of the six major surgical procedures was examined individually. Nearly one in seven patients hospitalized for a major surgical procedure is readmitted to the hospital within 30 days after discharge. Hospitals with high surgical volume and low surgical mortality have lower rates of surgical readmission than other hospitals.
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              Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia.

              The Centers for Medicare & Medicaid Services publicly reports hospital 30-day, all-cause, risk-standardized mortality rates (RSMRs) and 30-day, all-cause, risk-standardized readmission rates (RSRRs) for acute myocardial infarction, heart failure, and pneumonia. The evaluation of hospital performance as measured by RSMRs and RSRRs has not been well characterized. To determine the relationship between hospital RSMRs and RSRRs overall and within subgroups defined by hospital characteristics. We studied Medicare fee-for-service beneficiaries discharged with acute myocardial infarction, heart failure, or pneumonia between July 1, 2005, and June 30, 2008 (4506 hospitals for acute myocardial infarction, 4767 hospitals for heart failure, and 4811 hospitals for pneumonia). We quantified the correlation between hospital RSMRs and RSRRs using weighted linear correlation; evaluated correlations in groups defined by hospital characteristics; and determined the proportion of hospitals with better and worse performance on both measures. Hospital 30-day RSMRs and RSRRs. Mean RSMRs and RSRRs, respectively, were 16.60% and 19.94% for acute myocardial infarction, 11.17% and 24.56% for heart failure, and 11.64% and 18.22% for pneumonia. The correlations between RSMRs and RSRRs were 0.03 (95% CI, -0.002 to 0.06) for acute myocardial infarction, -0.17 (95% CI, -0.20 to -0.14) for heart failure, and 0.002 (95% CI, -0.03 to 0.03) for pneumonia. The results were similar for subgroups defined by hospital characteristics. Although there was a significant negative linear relationship between RSMRs and RSRRs for heart failure, the shared variance between them was only 2.9% (r2 = 0.029), with the correlation most prominent for hospitals with RSMR <11%. Risk-standardized mortality rates and readmission rates were not associated for patients admitted with an acute myocardial infarction or pneumonia and were only weakly associated, within a certain range, for patients admitted with heart failure.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                7 November 2014
                : 9
                : 11
                : e112282
                Affiliations
                [1 ]Department of Public Health, Centre for Medical Decision Making, Erasmus MC, Rotterdam, the Netherlands
                [2 ]Department of Medical Decision Making, Leiden University Medical Centre, Leiden, the Netherlands
                [3 ]Department of Public Health, Amsterdam Medical Centre, Amsterdam, the Netherlands
                Providence VA Medical Center and Brown University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CF NK DK HL. Performed the experiments: CF HL. Analyzed the data: CF HL. Contributed reagents/materials/analysis tools: CF HL PM ES DK NK. Wrote the paper: CF HL PM ES DK NK.

                Article
                PONE-D-14-29023
                10.1371/journal.pone.0112282
                4224424
                25379675
                c672857b-9446-49d6-a89f-d3afbc42f5df
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 July 2014
                : 3 October 2014
                Page count
                Pages: 9
                Funding
                This research has been funding by the Dutch Health Care Authority (NZa). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Health Care
                Quality of Care
                Research and Analysis Methods
                Research Assessment
                Research Quality Assessment
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

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