title: Computerized indicators of potential drug-related emergency department and hospital admissions.
abstract: OBJECTIVE
  To computerize indicators of potential drug-related emergency department and hospital admissions and to report the incidence of these potential drug-related morbidities for a managed care organization.
  
  
  METHODS
  Retrospective review of healthcare organizations' pharmacy and administrative claims databases.
  
  
  METHODS
  Thirty-nine indicators were coded and were used in an automated search of claims data. The indicators of potential drug-related morbidities comprised a pattern of care and an associated adverse outcome. Poisson distribution regression analysis was performed to assess the association of patient factors with indicator positives.
  
  
  RESULTS
  The incidence densities for indicator positives were 1.96 (95% confidence interval, 1.60-2.40) per 1000 patient-years in the general population and 13.6 (95% confidence interval, 8.8-20.2) per 1000 patient-years among older persons. Age, male sex, number of medical conditions, and number of medications from different classes were associated with an increased rate of indicator positives.
  
  
  CONCLUSIONS
  Indicators of potential drug-related morbidities can be fully automated and used to search through medical and pharmacy claims. The indicators investigated in this study show promise as a quality improvement tool and should be further developed and evaluated.
authors.affiliation: Salt Lake City IDEAS Center (182), Veterans Affairs Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, UT 84148, USA. brian.sauer@utah.edu
authors.forename: Brian C
authors.initials: BC
authors.lastname: Sauer
authors.forename: Charles D
authors.initials: CD
authors.lastname: Hepler
authors.forename: Becky
authors.initials: B
authors.lastname: Cherney
authors.forename: Jacquelyn
authors.initials: J
authors.lastname: Williamson
mesh_terms: D000293:Adolescent; D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D000465:Algorithms; D002648:Child; D015331:Cohort Studies; D016001:Confidence Intervals; D004347:Drug Interactions; D016903:Drug Monitoring; D064420:Drug-Related Side Effects and Adverse Reactions; D001330:Electronic Data Processing; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008329:Managed Care Programs / Q000706:statistics & numerical data*; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D012189:Retrospective Studies; D014481:United States