Multicenter Trial of the Emergency Department Trigger Tool for Adverse Event Detection
Bibliographic record
Abstract
STUDY OBJECTIVES: We previously developed and validated the Emergency Department Trigger Tool (EDTT) for detection of adverse events, featuring an automated screen for evidence-based triggers and high-yield sampling. We now report on a recently completed multicenter study of the EDTT, performance of the tool, event types detected, and site differences. METHODS: This retrospective, observational study used 18 months of data at 3 sites (452,719 records screened; 187,345 with more than or equal to 1 trigger). We performed 2-tiered dual independent review of a sample of 8,996 records (∼3,000 per site), selected from a balanced set of records, based on trigger count per record. We characterized adverse events identified by occurrence (emergency department [ED] or present on arrival), severity, and type, focusing here specifically on ED adverse events. RESULTS: In reviews of 8,996 records, we found 5,473 adverse events in 3,983 unique visits (yield: 44.3% of visits), including 2,359 ED adverse events and 3,114 present on arrival adverse events, with an ED adverse event rate of 0.26 per visit reviewed. ED adverse event yields using the EDTT were similar across sites (22.8%, 18.4%, 22.1%). Individual trigger performance was largely similar across sites and the EDTT performed well in detecting adverse events (area under the curve 78% across sites, range: 77% to 78%). Medication and patient-care related categories comprised the majority (81%) of ED adverse events, with some site differences. CONCLUSION: The EDTT provides a robust approach for ED quality and safety review with performance in detecting ED adverse events that surpasses traditional approaches and demonstrates generalizability across academic centers. Results are consistent with those obtained in our previous single-center study and are now replicated in multiple sites using a different electronic medical record system and reviewers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".