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Comparing 2 Methods of Assessing 30-Day Readmissions

What is the Impact on Hospital Profiling in the Veterans Health Administration

Bibliographic Data

ID9101074
AuthorsHillary J Mull (0000-0002-4484-0445, Boston University, corresponding author), Qi Chen (0000-0002-2835-5394, VA Boston Healthcare System, corresponding author), William J O’Brien (0000-0002-3366-3201, VA Boston Healthcare System, corresponding author), Michael Shwartz (0000-0003-4840-1682, VA Boston Healthcare System, corresponding author), Ann M Borzecki (0000-0003-1894-1274, Boston University), Amresh Hanchate (0000-0002-7038-4463, University School, corresponding author), Amy K Rosen (0000-0002-7539-7749, VA Boston Healthcare System, corresponding author)
Year2013
Volume51
Issue7
Pages589-596
Publication date2013-07-01
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e31829019a4
PMID23604016
PMCIDPMC4557787
OpenAlexW2019192942
LanguageEN
Citations received1
References cited1

BACKGROUND: The Centers for Medicare and Medicaid Services' (CMS) all-cause readmission measure and the 3M Health Information System Division Potentially Preventable Readmissions (PPR) measure are both used for public reporting. These 2 methods have not been directly compared in terms of how they identify high-performing and low-performing hospitals. OBJECTIVES: To examine how consistently the CMS and PPR methods identify performance outliers, and explore how the PPR preventability component impacts hospital readmission rates, public reporting on CMS' Hospital Compare website, and pay-for-performance under CMS' Hospital Readmission Reduction Program for 3 conditions (acute myocardial infarction, heart failure, and pneumonia). METHODS: We applied the CMS all-cause model and the PPR software to VA administrative data to calculate 30-day observed FY08-10 VA hospital readmission rates and hospital profiles. We then tested the effect of preventability on hospital readmission rates and outlier identification for reporting and pay-for-performance by replacing the dependent variable in the CMS all-cause model (Yes/No readmission) with the dichotomous PPR outcome (Yes/No preventable readmission). RESULTS: The CMS and PPR methods had moderate correlations in readmission rates for each condition. After controlling for all methodological differences but preventability, correlations increased to >90%. The assessment of preventability yielded different outlier results for public reporting in 7% of hospitals; for 30% of hospitals there would be an impact on Hospital Readmission Reduction Program reimbursement rates. CONCLUSIONS: Despite uncertainty over which readmission measure is superior in evaluating hospital performance, we confirmed that there are differences in CMS-generated and PPR-generated hospital profiles for reporting and pay-for-performance, because of methodological differences and the PPR's preventability component

Health care · Hospital readmission · Medicaid · Medical emergency · Reimbursement · Emergency Medicine · Heart Failure Treatment and Management · Medicine · Patient Satisfaction in Healthcare · Sepsis Diagnosis and Treatment

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    Rozalina G Mccoy, Stephanie M Peterson et al.•Medical Care•2018

Citations per year0.13
Citation span2018 - 2018 (1)
Highly citedNo

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