title: Implications of metric choice for common applications of readmission metrics.
abstract: OBJECTIVE
  To quantify the differential impact on hospital performance of three readmission metrics: all-cause readmission (ACR), 3M Potential Preventable Readmission (PPR), and Centers for Medicare and Medicaid 30-day readmission (CMS).
  
  
  METHODS
  2000-2009 California Office of Statewide Health Planning and Development Patient Discharge Data Nonpublic file.
  
  
  METHODS
  We calculated 30-day readmission rates using three metrics, for three disease groups: heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Using each metric, we calculated the absolute change and correlation between performance; the percent of hospitals remaining in extreme deciles and level of agreement; and differences in longitudinal performance.
  
  
  RESULTS
  Average hospital rates for HF patients and the CMS metric were generally higher than for other conditions and metrics. Correlations between the ACR and CMS metrics were highest (r = 0.67-0.84). Rates calculated using the PPR and either ACR or CMS metrics were moderately correlated (r = 0.50-0.67). Between 47 and 75 percent of hospitals in an extreme decile according to one metric remained when using a different metric. Correlations among metrics were modest when measuring hospital longitudinal change.
  
  
  CONCLUSIONS
  Different approaches to computing readmissions can produce different hospital rankings and impact pay-for-performance. Careful consideration should be placed on readmission metric choice for these applications.
authors.affiliation: Center for Primary Care and Outcomes Research, Stanford School of Medicine, Stanford, CA.
authors.forename: Sheryl
authors.initials: S
authors.lastname: Davies
authors.forename: Olga
authors.initials: O
authors.lastname: Saynina
authors.forename: Ellen
authors.initials: E
authors.lastname: Schultz
authors.forename: Kathryn M
authors.initials: KM
authors.lastname: McDonald
authors.forename: Laurence C
authors.initials: LC
authors.lastname: Baker
mesh_terms: D019985:Benchmarking / Q000379:methods*; D002140:California; D014487:Centers for Medicare and Medicaid Services, U.S. / Q000706:statistics & numerical data*; D006333:Heart Failure / Q000628:therapy*; D006801:Humans; D009203:Myocardial Infarction / Q000628:therapy*; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011014:Pneumonia / Q000628:therapy*; D011787:Quality of Health Care / Q000458:organization & administration; D020379:Risk Adjustment; D014481:United States
keywords: Administrative data uses; hospitals; quality of care