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Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events
  1. Kevin J O'Leary1,
  2. Vikram K Devisetty1,
  3. Amitkumar R Patel1,
  4. David Malkenson2,
  5. Pradeep Sama2,
  6. William K Thompson3,
  7. Matthew P Landler1,
  8. Cynthia Barnard2,
  9. Mark V Williams1
  1. 1Division of Hospital Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
  2. 2Northwestern Memorial Hospital, Chicago, Illinois, USA
  3. 3Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
  1. Correspondence to Dr Kevin J O'Leary, Division of Hospital Medicine, Northwestern University Feinberg School of Medicine, 211 E. Ontario Street, Suite 211, Chicago, IL 60611, USA; keoleary{at}nmh.org

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.

Method 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.

  • Patient safety
  • Adverse events, epidemiology and detection
  • Chart review methodologies

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