title: Description and evaluation of adaptations to the global trigger tool to enhance value to adverse event reduction efforts.
abstract: OBJECTIVE
  To adapt the Global Trigger Tool (GTT) as a sustainable monitoring tool able to characterize adverse events (AEs) for organizational learning, within the context of limited resources.
  
  
  METHODS
  Baylor Health Care System (BHCS) expanded the AE data collected to include judgments of preventability, presence on admission, relation to care provided or not provided, and narrative descriptions. To reduce costs, we focused on patients with length of stay (LOS) of 3 days or more, suspecting greater likelihood they had experienced an AE; adapted the sample size and frequency of review; and used a single nurse reviewer followed by quality assurance review within the Office of Patient Safety. We compared AE rates in patients with LOS of less than 3 days versus 3 days or greater, assessed trigger yields and interrater reliability, and submitted identified AEs to each hospital for validation as event types targeted for reduction.
  
  
  RESULTS
  In 2008, 91% of identified AEs were in patients with LOS of 3 days or greater; there were 6.4 AEs per 100 discharges with LOS of less than 3 days versus 27.1 AEs per 100 discharges with LOS of 3 days or greater. Over 4 years, we reviewed 16,172 medical records; 14,184 had positive triggers, 17.1% of which were associated with an AE. Most AEs were identified via the "surgical" (36.3%) and "patient care" (36.0%) trigger modules. Reviewers showed fair to good agreement (κ = 0.62), and hospital clinical leaders strongly agreed that the identified events were AEs.
  
  
  CONCLUSIONS
  The GTT can be adapted to health-care organizations' goals and resource limitations. This flexibility was essential in crossing our organization's "value threshold."
authors: Office of Patient Safety, Baylor Health Care System, Dallas, Texas 75206, USA. Donald.kennerly@baylorhealth.edu
authors: Donald A
authors: DA
authors: Kennerly
authors: Margaret
authors: M
authors: Saldaña
authors: Rustam
authors: R
authors: Kudyakov
authors: Briget
authors: B
authors: da Graca
authors: David
authors: D
authors: Nicewander
authors: Jan
authors: J
authors: Compton
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards; D057225:Data Mining; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control*; D057286:Electronic Health Records; D006751:Hospital Information Systems; D006801:Humans; D007902:Length of Stay; D019300:Medical Errors / Q000517:prevention & control*; D015588:Observer Variation; D010351:Patient Discharge; D061214:Patient Safety / Q000592:standards; D011379:Prognosis; D058996:Quality Improvement; D015203:Reproducibility of Results; D013781:Texas; D013997:Time Factors