The emergency department trigger tool: Multicenter trigger query validation

Abstract

ObjectivesWe previously described derivation and validation of the emergency department trigger tool (EDTT) for adverse event (AE) detection. As the first step in our multicenter study of the tool, we validated our computerized screen for triggers against manual review, establishing our use of this automated process for selecting records to review for AEs.MethodsThis is a retrospective observational study of visits to three urban, academic EDs over 18 months by patients ≥ 18 years old. We reviewed 912 records: 852 with at least one of 34 triggers found by the query and 60 records with none. Two first‐level reviewers per site each manually screened for triggers. After completion, computerized query results were revealed, and reviewers could revise their findings. Second‐level reviewers arbitrated discrepancies. We compare automated versus manual screening by positive and negative predictive values (PPVs, NPVs), present population trigger frequencies, proportions of records triggered, and how often manual ratings were changed to conform with the query.ResultsTrigger frequencies ranged from common (>25%) to rare (1/1000) were comparable at U.S. sites and slightly lower at the Canadian site. Proportions of triggered records ranged from 31% to 49.4%. Overall query PPV was 95.4%; NPV was 99.2%. PPVs for individual trigger queries exceeded 90% for 28–31 triggers/site and NPVs were >90% for all but three triggers at one site. Inter‐rater reliability was excellent, with disagreement on manual screening results less than 5% of the time. Overall, reviewers amended their findings 1.5% of the time when discordant with query findings, more often when the query was positive than when negative (47% vs. 23%).ConclusionsThe EDTT trigger query performed very well compared to manual review. With some expected variability, trigger frequencies were similar across sites and proportions of triggered records ranged 31%–49%. This demonstrates the feasibility and generalizability of implementing the EDTT query, providing a solid foundation for testing the triggers’ utility in detecting AEs.