The Emergency Department Trigger Tool: A Novel Approach to Screening for Quality and Safety Events

Study objective: Trigger tools improve surveillance for harm by focusing reviews on records with “triggers” whose presence increases the likelihood of an adverse event. We refine and automate a previously developed emergency department (ED) trigger tool and present record selection strategies to further optimize yield. Methods: We specified 97 triggers for extraction from our electronic medical record, identifying 76,894 ED visits with greater than or equal to 1 trigger. We reviewed 1,726 records with greater than or equal to 1 trigger, following a standard trigger tool review process. We validated query performance against manual review and evaluated individual triggers, retaining only those associated with adverse events in the ED. We explored 2 approaches to enhance record selection: on number of triggers present and using trigger weights derived with least absolute shrinkage and selection operator logistic regression. Results: The automated query performed well compared with manual review (sensitivity >70% for 80 triggers; specificity >92% for all). Review yielded 374 adverse events (21.6 adverse events per 100 records). Thirty triggers were associated with risk of harm in the ED. An estimated 10.3% of records with greater than 1 of these triggers would include an adverse event in the ED. Selecting only records with greater than or equal to 4 or greater than or equal to 9 triggers improves yield to 17% and 34.8%, respectively, whereas use of least absolute shrinkage and selection operator trigger weighting enhances the yield to as high as 52%. Conclusion: The ED trigger tool is a promising approach to improve yield, scope, and efficiency of review for all-cause harm in emergency medicine. Beginning with a broad set of candidate triggers, we validated a computerized query that eliminates the need for manual screening for triggers and identified a refined set of triggers associated with adverse events in the ED. Review efficiency can be further enhanced with enhanced record selection.