title: A systematic review to evaluate the accuracy of electronic adverse drug event detection.
abstract: OBJECTIVE
  Adverse drug events (ADEs), defined as adverse patient outcomes caused by medications, are common and difficult to detect. Electronic detection of ADEs is a promising method to identify ADEs. We performed this systematic review to characterize established electronic detection systems and their accuracy.
  
  
  METHODS
  We identified studies evaluating electronic ADE detection from the MEDLINE and EMBASE databases. We included studies if they contained original data and involved detection of electronic triggers using information systems. We abstracted data regarding rule characteristics including type, accuracy, and rationale.
  
  
  RESULTS
  Forty-eight studies met our inclusion criteria. Twenty-four (50%) studies reported rule accuracy but only 9 (18.8%) utilized a proper gold standard (chart review in all patients). Rule accuracy was variable and often poor (range of sensitivity: 40%-94%; specificity: 1.4%-89.8%; positive predictive value: 0.9%-64%). 5 (10.4%) studies derived or used detection rules that were defined by clinical need or the underlying ADE prevalence. Detection rules in 8 (16.7%) studies detected specific types of ADEs.
  
  
  CONCLUSIONS
  Several factors led to inaccurate ADE detection algorithms, including immature underlying information systems, non-standard event definitions, and variable methods for detection rule validation. Few ADE detection algorithms considered clinical priorities. To enhance the utility of electronic detection systems, there is a need to systematically address these factors.
authors.affiliation: Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. aforster@ohri.ca
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Forster
authors.forename: Alison
authors.initials: A
authors.lastname: Jennings
authors.forename: Claire
authors.initials: C
authors.lastname: Chow
authors.forename: Ciera
authors.initials: C
authors.lastname: Leeder
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
mesh_terms: D000465:Algorithms; D002984:Clinical Laboratory Information Systems; D016903:Drug Monitoring / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis* / Q000453:epidemiology; D005069:Evaluation Studies as Topic; D006801:Humans; D007256:Information Systems; D008509:Medication Systems; D060735:Pharmacovigilance