title: How pooling fragmented healthcare encounter data affects hospital profiling.
abstract: OBJECTIVE
  People receiving healthcare from multiple payers (eg, Medicare and the Veterans Health Administration [VA]) have fragmented health records. How the use of more complete data affects hospital profiling has not been examined.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  We examined 30-day mortality following acute myocardial infarction at 104 VA hospitals for veterans 66 years and older from 2006 through 2010 who were also Medicare beneficiaries. Using VA-only data versus combined VA/Medicare data, we calculated 2 risk-standardized mortality rates (RSMRs): 1 based on observed mortality (O/E) and the other from CMS' Hospital Compare program, based on model-predicted mortality (P/E). We also categorized hospital outlier status based on RSMR relative to overall VA mortality: average, better than average, and worse than average. We tested whether hospitals whose patients received more of their care through Medicare would look relatively better when including those data in risk adjustment, rather than including VA data alone.
  
  
  RESULTS
  Thirty-day mortality was 14.8%. Adding Medicare data caused both RSMR measures to significantly increase in about half the hospitals and decrease in the other half. O/E RSMR increased in 53 hospitals, on average, by 2.2%, and decreased in 51 hospitals by -2.6%. P/E RSMR increased, on average, by 1.2% in 56 hospitals, and decreased in the others by -1.3%. Outlier designation changed for 4 hospitals using O/E measure, but for no hospitals using P/E measure.
  
  
  CONCLUSIONS
  VA hospitals vary in their patients' use of Medicare-covered care and completeness of health records based on VA data alone. Using combined VA/Medicare data provides modestly different hospital profiles compared with those using VA-alone data.
authors.affiliation: Boston University School of Medicine, 801 Massachusetts Ave, Boston, MA 02118. E-mail: hanchate@bu.edu.
authors.forename: Amresh D
authors.initials: AD
authors.lastname: Hanchate
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.forename: Ann
authors.initials: A
authors.lastname: Borzecki
authors.forename: Hassen
authors.initials: H
authors.lastname: Abdulkerim
authors.forename: Kelly L
authors.initials: KL
authors.lastname: Stolzmann
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.forename: Aaron S
authors.initials: AS
authors.lastname: Fink
authors.forename: Mary Jo V
authors.initials: MJ
authors.lastname: Pugh
authors.forename: Priti
authors.initials: P
authors.lastname: Shokeen
authors.forename: Michael
authors.initials: M
authors.lastname: Shwartz
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D016208:Databases, Factual; D017052:Hospital Mortality; D006787:Hospitals, Veterans / Q000592:standards / Q000639:trends; D006801:Humans; D007345:Insurance Claim Review; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D009203:Myocardial Infarction / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D011785:Quality Assurance, Health Care; D012189:Retrospective Studies; D020379:Risk Adjustment; D014481:United States