Patient Prioritization in Emergency Department Triage Systems: An Empirical Study of the Canadian Triage and Acuity Scale (CTAS)
Bibliographic record
Abstract
Emergency departments (EDs) typically use a triage system to classify patients into priority levels. However, most triage systems do not specify how exactly to route patients across and within the assigned triage levels. Therefore, decision makers in EDs often have to use their own discretion to route patients. Also, how patient waiting is perceived and accounted for in ED operations is not clearly understood. In this paper, using patient-level ED visit data, we structurally estimate the waiting cost structure of ED patients as perceived by the decision makers who make ED patient routing decisions. We derive policy implications and make suggestions for improving triage systems. We analyze the patient routing behaviors of ED decision makers in four EDs in the metro Vancouver, British Columbia, area. They all use the Canadian Triage and Acuity Scale, which has a wait time–related target service level objective. We propose a general discrete choice framework, consistent with queueing literature, as a tool to analyze prioritization behaviors in multiclass queues under mild assumptions. We find that the decision makers in all four EDs (1) apply a delay-dependent prioritization across different triage levels; (2) have a perceived marginal ED patient waiting cost that is best fit by a piece-wise linear concave function in wait time; (3) generally follow, in the same triage level, the first-come first-served principle, but their adherence to the principle decreases for patients who wait past a certain threshold; and (4) do not use patient complexity as a major criterion in prioritization decisions.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".