NO WAIT: new organised well-adapted immediate triage: a lean improvement project
Bibliographic record
Abstract
Long waiting times in the emergency department (ED) are associated with decreased patient satisfaction and increased morbidity and mortality. Triage may be a contributing factor to prolonged wait times in the ED. At Alhada Armed Forces Hospital (Taif, Saudi Arabia), patients other than level 1 and 2 on the Canadian Triage and Acuity Scale are requested to wait until triage. During peak hours (08:00-22:00), the waiting time prior to triage is prolonged, and several patients leave the ED before triage. In this project, a multidisciplinary team was assembled to revise patient flow from the time of arrival at the ED to the time of triage. Lean methodology was used to identify the redundancies and design a seamless flow process for ED patients. Through reorganising the triage area using minimal additional resources, the project team devised a novel floor plan for the triage area which provided a unique patient flow in the ED. The median patient wait time from arrival to triage was reduced from 27 min to 4.09 min and the percentage of patients leaving the ER before triage was reduced to 0%. This project is the first of its kind in Saudi Arabia, as well as in the Gulf region, and provides a radical solution to the problem of patient waiting in the ED during peak hours.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".