title: Appending limited clinical data to an administrative database for assessing institutional quality outliers for AMI care: does it matter?
abstract: BACKGROUND
  The major objective of this study was to determine whether individual hospital performance would be assessed differently if clinical data were added to an administrative dataset.
  
  
  METHODS
  Patients in the 2004 New York State AMI Registry (AMI registry) who could be matched to patients in the New York State Hospital Discharge Database (SPARCS model) comprised the study sample (n=3153). Stepwise logistic regression models were developed (SPARCS model, SPARCS/AMI registry model). Risk-adjusted mortality rates (RAMR) for each hospital in the matched dataset were determined and compared for the SPARCS and the SPARCS/AMI registry model. The RAMR for each hospital was determined by dividing its observed mortality rate by its expected mortality rate and multiplying by the overall mortality rate for the state of New York. Hospitals were considered outliers if they had a RAMR significantly higher or lower than the overall statewide mortality rate. Hierarchical Models were also used to identify hospital outliers.
  
  
  RESULTS
  The SPARCS logistic model identified two high hospital outliers; the SPARCS/AMI registry model identified one of those outliers and no others. When Hierarchical Models were used, the SPARCS model also identified two high outliers (one in common with the logistic model) and the SPARCS/AMI registry model identified one high outlier (the same as identified in the logistic model).
  
  
  CONCLUSIONS
  It is worth exploring the impact of the addition of a small number of clinical data elements to administrative datasets on hospital outlier status.
authors.affiliation: Department of Health Policy, Management, and Behavior, School of Public Health, State University of New York, University at Albany, Rensselaer, NY 12144-3456, USA. wweller@albany.edu
authors.forename: Wendy E
authors.initials: WE
authors.lastname: Weller
authors.forename: Garima
authors.initials: G
authors.lastname: Kabra
authors.forename: Kimberly S
authors.initials: KS
authors.lastname: Cozzens
authors.forename: Edward L
authors.initials: EL
authors.lastname: Hannan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D005260:Female; D017052:Hospital Mortality; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000401:mortality / Q000628:therapy*; D009518:New York / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000379:methods; D012042:Registries / Q000706:statistics & numerical data; D012307:Risk Factors