title: Evaluating the patient safety indicators: how well do they perform on Veterans Health Administration data?
abstract: BACKGROUND
  The Patient Safety Indicators (PSIs), an administrative data-based tool developed by the Agency for Healthcare Research and Quality, are increasingly being used to screen for potential in-hospital patient safety problems. Although the Veterans Health Administration (VA) is a national leader in patient safety, accurate information on the epidemiology of patient safety events in the VA is still unavailable.
  
  
  OBJECTIVE
  Our objectives were to: (1) apply the AHRQ PSI software to VA administrative data to identify potential instances of compromised patient safety; (2) determine occurrence rates of PSI events in the VA; and (3) examine the construct validity of the PSIs.
  
  
  METHODS
  We examined differences between observed and risk-adjusted PSI rates in the VA, compared VA and non-VA PSI rates, and investigated the construct validity of the PSIs by examining correlations of the PSIs with other outcomes of VA hospitalizations.
  
  
  RESULTS
  We identified 11,411 PSI events in the VA nationwide in FY'01. Observed PSI rates per 1000 discharges ranged from 0.007 for "transfusion reaction" to 155.5 for "failure to rescue." There were significant, although small, differences between VA and non-VA risk-adjusted PSI rates. Hospitalizations with PSI events had longer lengths of stay, higher mortality, and higher costs than those without PSI events.
  
  
  CONCLUSIONS
  Our results suggest that the PSIs may be useful as a patient safety screening tool in the VA. Our PSI rates were consistent with the national incidence of low rates; however, differences between VA and non-VA rates suggest that inadequate case-mix adjustment may be contributing to these findings.
authors.affiliation: Center for Health Quality, Outcomes and Economic Research, Bedford VAMC (152), Bedford, Massachusetts 01730, USA. akrosen@bu.edu
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.forename: Peter
authors.initials: P
authors.lastname: Rivard
authors.forename: Shibei
authors.initials: S
authors.lastname: Zhao
authors.forename: Susan
authors.initials: S
authors.lastname: Loveland
authors.forename: Dennis
authors.initials: D
authors.lastname: Tsilimingras
authors.forename: Cindy L
authors.initials: CL
authors.lastname: Christiansen
authors.forename: Anne
authors.initials: A
authors.lastname: Elixhauser
authors.forename: Patrick S
authors.initials: PS
authors.lastname: Romano
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D005260:Female; D006787:Hospitals, Veterans / Q000458:organization & administration / Q000592:standards*; D006801:Humans; D008297:Male; D019300:Medical Errors / Q000517:prevention & control / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D011785:Quality Assurance, Health Care; D019984:Quality Indicators, Health Care / Q000145:classification*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D017751:Safety Management / Q000379:methods*; D012494:Sampling Studies; D018571:Sentinel Surveillance; D012984:Software; D014481:United States / Q000453:epidemiology; D016270:United States Agency for Healthcare Research and Quality; D014493:United States Department of Veterans Affairs