title: The Hospital Compare mortality model and the volume-outcome relationship.
abstract: OBJECTIVE
  We ask whether Medicare's Hospital Compare random effects model correctly assesses acute myocardial infarction (AMI) hospital mortality rates when there is a volume-outcome relationship.
  
  
  METHODS
  Medicare claims on 208,157 AMI patients admitted in 3,629 acute care hospitals throughout the United States.
  
  
  METHODS
  We compared average-adjusted mortality using logistic regression with average adjusted mortality based on the Hospital Compare random effects model. We then fit random effects models with the same patient variables as in Medicare's Hospital Compare mortality model but also included terms for hospital Medicare AMI volume and another model that additionally included other hospital characteristics.
  
  
  RESULTS
  Hospital Compare's average adjusted mortality significantly underestimates average observed death rates in small volume hospitals. Placing hospital volume in the Hospital Compare model significantly improved predictions.
  
  
  CONCLUSIONS
  The Hospital Compare random effects model underestimates the typically poorer performance of low-volume hospitals. Placing hospital volume in the Hospital Compare model, and possibly other important hospital characteristics, appears indicated when using a random effects model to predict outcomes. Care must be taken to insure the proper method of reporting such models, especially if hospital characteristics are included in the random effects model.
authors.affiliation: Center for Outcomes Research, 3535 Market Street, Suite 1029, Philadelphia, PA 19104, USA. silberj@wharton.upenn.edu
authors.forename: Jeffrey H
authors.initials: JH
authors.lastname: Silber
authors.forename: Paul R
authors.initials: PR
authors.lastname: Rosenbaum
authors.forename: Tanguy J
authors.initials: TJ
authors.lastname: Brachet
authors.forename: Richard N
authors.initials: RN
authors.lastname: Ross
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Bressler
authors.forename: Orit
authors.initials: O
authors.lastname: Even-Shoshan
authors.forename: Scott A
authors.initials: SA
authors.lastname: Lorch
authors.forename: Kevin G
authors.initials: KG
authors.lastname: Volpp
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001509:Bed Occupancy / Q000706:statistics & numerical data; D015982:Bias; D006302:Health Services Research; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data; D020407:Internet; D016014:Linear Models; D016015:Logistic Models; D006278:Medicare / Q000706:statistics & numerical data*; D009203:Myocardial Infarction / Q000401:mortality; D017063:Outcome Assessment, Health Care / Q000458:organization & administration*; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D020379:Risk Adjustment; D014481:United States / Q000453:epidemiology