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.
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.
Authors: Norbert I Goldfield; Elizabeth C McCullough; John S Hughes; Ana M Tang; Beth Eastman; Lisa K Rawlins; Richard F Averill Journal: Health Care Financ Rev Date: 2008
Authors: Julia G Lavenberg; Brian Leas; Craig A Umscheid; Kendal Williams; David R Goldmann; Sunil Kripalani Journal: J Hosp Med Date: 2014-06-25 Impact factor: 2.960
Authors: Rozalina G McCoy; Stephanie M Peterson; Lynn S Borkenhagen; Paul Y Takahashi; Bjorg Thorsteinsdottir; Anupam Chandra; James M Naessens Journal: Med Care Date: 2018-08 Impact factor: 2.983
Authors: Roger K Khouri; Hechuan Hou; Apoorv Dhir; Juan J Andino; James M Dupree; David C Miller; Chad Ellimoottil Journal: BMC Health Serv Res Date: 2017-11-28 Impact factor: 2.655
Authors: Brigid Wilson; Chin-Lin Tseng; Orysya Soroka; Leonard M Pogach; David C Aron Journal: BMC Health Serv Res Date: 2017-11-16 Impact factor: 2.655
Authors: Ana H Jackson; Emily Fireman; Paul Feigenbaum; Estee Neuwirth; Patricia Kipnis; Jim Bellows Journal: BMC Med Inform Decis Mak Date: 2014-04-05 Impact factor: 2.796
Authors: Jennifer Townsend; Sara Keller; Martin Tibuakuu; Sameer Thakker; Bailey Webster; Maya Siegel; Kevin J Psoter; Omar Mansour; Trish M Perl Journal: Open Forum Infect Dis Date: 2018-10-24 Impact factor: 3.835