Impact of a Two-step Emergency Department Triage Model with START, then CTAS, on Patient Flow During a Simulated Mass-casualty Incident
Bibliographic record
Abstract
INTRODUCTION: A high influx of patients during a mass-casualty incident (MCI) may disrupt patient flow in an already overcrowded emergency department (ED) that is functioning beyond its operating capacity. This pilot study examined the impact of a two-step ED triage model using Simple Triage and Rapid Treatment (START) for pre-triage, followed by triage with the Canadian Triage and Acuity Scale (CTAS), on patient flow during a MCI simulation exercise. Hypothesis/Problem It was hypothesized that there would be no difference in time intervals nor patient volumes at each patient-flow milestone. METHODS: Physicians and nurses participated in a computer-based tabletop disaster simulation exercise. Physicians were randomized into the intervention group using START, then CTAS, or the control group using START alone. Patient-flow milestones including time intervals and patient volumes from ED arrival to triage, ED arrival to bed assignment, ED arrival to physician assessment, and ED arrival to disposition decision were compared. Triage accuracy was compared for secondary purposes. RESULTS: There were no significant differences in the time interval from ED arrival to triage (mean difference 108 seconds; 95% CI, -353 to 596 seconds; P=1.0), ED arrival to bed assignment (mean difference 362 seconds; 95% CI, -1,269 to 545 seconds; P=1.0), ED arrival to physician assessment (mean difference 31 seconds; 95% CI, -1,104 to 348 seconds; P=0.92), and ED arrival to disposition decision (mean difference 175 seconds; 95% CI, -1,650 to 1,300 seconds; P=1.0) between the two groups. There were no significant differences in the volume of patients to be triaged (32% vs 34%; 95% CI for the difference -16% to 21%; P=1.0), assigned a bed (16% vs 21%; 95% CI for the difference -11% to 20%; P=1.0), assessed by a physician (20% vs 22%; 95% CI for the difference -14% to 19%; P=1.0), and with a disposition decision (20% vs 9%; 95% CI for the difference -25% to 4%; P=.34) between the two groups. The accuracy of triage was similar in both groups (57% vs 70%; 95% CI for the difference -15% to 41%; P=.46). CONCLUSION: Experienced triage nurses were able to apply CTAS effectively during a MCI simulation exercise. A two-step ED triage model using START, then CTAS, had similar patient flow and triage accuracy when compared to START alone.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".