title: Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events.
abstract: BACKGROUND
  Research supports medical record review using screening triggers as the optimal method to detect hospital adverse events (AE), yet the method is labour-intensive.
  
  
  METHODS
  This study compared a traditional trigger tool with an enterprise data warehouse (EDW) based screening method to detect AEs. We created 51 automated queries based on 33 traditional triggers from prior research, and then applied them to 250 randomly selected medical patients hospitalised between 1 September 2009 and 31 August 2010. Two physicians each abstracted records from half the patients using a traditional trigger tool and then performed targeted abstractions for patients with positive EDW queries in the complementary half of the sample. A third physician confirmed presence of AEs and assessed preventability and severity.
  
  
  RESULTS
  Traditional trigger tool and EDW based screening identified 54 (22%) and 53 (21%) patients with one or more AE. Overall, 140 (56%) patients had one or more positive EDW screens (total 366 positive screens). Of the 137 AEs detected by at least one method, 86 (63%) were detected by a traditional trigger tool, 97 (71%) by EDW based screening and 46 (34%) by both methods. Of the 11 total preventable AEs, 6 (55%) were detected by traditional trigger tool, 7 (64%) by EDW based screening and 2 (18%) by both methods. Of the 43 total serious AEs, 28 (65%) were detected by traditional trigger tool, 29 (67%) by EDW based screening and 14 (33%) by both.
  
  
  CONCLUSIONS
  We found relatively poor agreement between traditional trigger tool and EDW based screening with only approximately a third of all AEs detected by both methods. A combination of complementary methods is the optimal approach to detecting AEs among hospitalised patients.
authors.affiliation: Division of Hospital Medicine, Northwestern University Feinberg School of Medicine, 211 E. Ontario Street, Chicago, IL 60611, USA. keoleary@nmh.org
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: O'Leary
authors.forename: Vikram K
authors.initials: VK
authors.lastname: Devisetty
authors.forename: Amitkumar R
authors.initials: AR
authors.lastname: Patel
authors.forename: David
authors.initials: D
authors.lastname: Malkenson
authors.forename: Pradeep
authors.initials: P
authors.lastname: Sama
authors.forename: William K
authors.initials: WK
authors.lastname: Thompson
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Landler
authors.forename: Cynthia
authors.initials: C
authors.lastname: Barnard
authors.forename: Mark V
authors.initials: MV
authors.lastname: Williams
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D054869:Clinical Audit; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records; D006761:Hospitals; D006801:Humans; D016247:Information Storage and Retrieval; D019300:Medical Errors / Q000517:prevention & control / Q000706:statistics & numerical data*; D008498:Medical Record Linkage / Q000379:methods* / Q000592:standards; D008508:Medication Errors / Q000517:prevention & control / Q000706:statistics & numerical data; D061214:Patient Safety / Q000592:standards; D019984:Quality Indicators, Health Care; D012308:Risk Management / Q000379:methods*