title: Comparing 2 methods of assessing 30-day readmissions: what is the impact on hospital profiling in the veterans health administration?
abstract: 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.
  
  
  OBJECTIVE
  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.affiliation: Center for Organization, Leadership, and Management Research (COLMR), VA Boston Healthcare System, Boston, MA, USA.
authors.forename: Hillary J
authors.initials: HJ
authors.lastname: Mull
authors.forename: Qi
authors.initials: Q
authors.lastname: Chen
authors.forename: William J
authors.initials: WJ
authors.lastname: O'Brien
authors.forename: Michael
authors.initials: M
authors.lastname: Shwartz
authors.forename: Ann M
authors.initials: AM
authors.lastname: Borzecki
authors.forename: Amresh
authors.initials: A
authors.lastname: Hanchate
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
mesh_terms: D000368:Aged; D014487:Centers for Medicare and Medicaid Services, U.S.; D006333:Heart Failure; D006801:Humans; D007349:Insurance, Health, Reimbursement; D019221:Mandatory Reporting; D008875:Middle Aged; D009203:Myocardial Infarction; D010359:Patient Readmission / Q000706:statistics & numerical data; D011014:Pneumonia; D011785:Quality Assurance, Health Care / Q000379:methods*; D020379:Risk Adjustment; D014481:United States; D014493:United States Department of Veterans Affairs