title: Patient Safety Indicators: using administrative data to identify potential patient safety concerns.
abstract: OBJECTIVE
  To develop Patient Safety Indicators (PSI) to identify potential in-hospital patient safety problems for the purpose of quality improvement.
  
  
  METHODS
  The data source was 2,400,000 discharge records in the 1997 New York State Inpatient Database. PSI algorithms were developed using systematic literature reviews of indicators and hand searches of the ICD-9-CM code book. The prevalence of PSI events and associations between PSI events and patient-level and hospital-level characteristics, length of stay, in-hospital mortality, and hospital charges were examined.
  
  
  RESULTS
  PSIs were developed for 12 distinct clinical situations and an overall summary measure. The 1997 event rates per 10,000 discharges varied from 1.1 for foreign bodies left during procedure to 84.7 for birth traumas. Discharge records with PSI events had twofold to threefold longer hospital stays, twofold to 20-fold higher rates of in-hospital mortality, and twofold to eightfold higher total charges than records without PSI events. Multivariate logistic regression revealed that PSI events were primarily associated with increasing age (p < .001), hospitals performing more inpatient surgery (p < .001), and hospitals with higher percentage of beds in intensive care units (p < .001).
  
  
  CONCLUSIONS
  The PSIs provide an efficient and user-friendly tool to identify potential inhospital patient safety problems for targeted institution-level quality improvement efforts. Until better error-reporting systems are developed the PSIs can serve to shed light on the problem of medical errors not limited solely to mortality because of errors.
authors.affiliation: Center for Quality Improvement and Patient Safety, Agency for Healthcare Research and Quality, Rockville, MD 20852, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Miller
authors.forename: A
authors.initials: A
authors.lastname: Elixhauser
authors.forename: C
authors.initials: C
authors.lastname: Zhan
authors.forename: G S
authors.initials: GS
authors.lastname: Meyer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006761:Hospitals / Q000145:classification / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007049:Iatrogenic Disease / Q000453:epidemiology*; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D019300:Medical Errors / Q000145:classification* / Q000706:statistics & numerical data; D008875:Middle Aged; D009518:New York / Q000453:epidemiology; D016730:Program Development; D011785:Quality Assurance, Health Care; D019984:Quality Indicators, Health Care; D017751:Safety Management; D018571:Sentinel Surveillance