title: Signal and noise: applying a laboratory trigger tool to identify adverse drug events among primary care patients.
abstract: BACKGROUND
  The extent of outpatient adverse drug events (ADEs) remains unclear. Trigger tools are used as a screening method to identify care episodes that may be ADEs, but their value in a population with high chronic-illness burden remains unclear.
  
  
  METHODS
  The authors used six abnormal laboratory triggers for detecting ADEs among adults in outpatient care. Eligible patients were included if they were >18 years, sought primary or urgent care between November 2008 and November 2009 and were prescribed at least one medication. The authors then used the clinical / administrative database to identity patients with these triggers. Two physicians conducted in-depth chart review of any medical records with identified triggers.
  
  
  RESULTS
  The authors reviewed 1342 triggers representing 622 unique episodes among 516 patients. The trigger tool identified 91 (15%) ADEs. Of the 91 ADEs included in the analysis, 49 (54%) occurred during medication monitoring, 41 (45%) during patient self-administration, and one could not be determined. 96% of abnormal international normalised ratio triggers were ADEs, followed by 12% of abnormal blood urea nitrogen triggers, 9% of abnormal alanine aminotransferase triggers, 8% of abnormal serum creatinine triggers and 3% of aspartate aminotransferase triggers.
  
  
  CONCLUSIONS
  The findings imply that other tools such as text triggers or more complex automated screening rules, which combine data hierarchically are needed to effectively screen for ADEs in chronically ill adults seen in primary care.
authors.affiliation: Division of General Internal Medicine, San Francisco General Hospital, University of California, San Francisco, San Francisco, CA 94110, USA.
authors.forename: Stacey
authors.initials: S
authors.lastname: Brenner
authors.forename: Alissa
authors.initials: A
authors.lastname: Detz
authors.forename: Andrea
authors.initials: A
authors.lastname: López
authors.forename: Claire
authors.initials: C
authors.lastname: Horton
authors.forename: Urmimala
authors.initials: U
authors.lastname: Sarkar
mesh_terms: D000328:Adult; D000368:Aged; D064420:Drug-Related Side Effects and Adverse Reactions / Q000097:blood* / Q000453:epidemiology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006403:Hematologic Tests / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D011320:Primary Health Care / Q000706:statistics & numerical data*; D011787:Quality of Health Care; D012189:Retrospective Studies