The Impact of Involving a Senior Emergency Physician in the Triage Process
Bibliographic record
Abstract
BACKGROUND: Emergency Department (ED) overcrowding has been demonstrated in several studies to be associated with undesirable effects such as longer waiting times, reduced patient satisfaction, and, most importantly, poor patient outcomes. Furthermore, long waiting times for walk-ins result in more complaints and patient dissatisfaction than illness management itself, with the majority of issues arising as a result of real and perceived waiting periods before being seen by the doctor. AIM: We set out to investigate whether introducing a senior emergency physician into the triage system would reduce waiting time, door-to-decision time, and door-to-doctor time, as well as increase patient satisfaction across the ED. METHOD: This was an interventional pre-post study that utilised retrospective data to evaluate the effect on ED throughput of triage by senior emergency physicians. We aimed to measure its impact on waiting time, door-to-decision time, and door-to-doctor time, along with ED patient satisfaction. RESULTS: Patient satisfaction, the overall assessment of treatment received during the visit, increased, from 74.975 to 77.425, and the likelihood of patients recommending the ED increased from 71.36 to 75.21. Operational metrics revealed a considerable drop in door-to-decision time (admit or discharge) of 46 minutes and 3 seconds, as well as a 1 minute and 21 second reduction in time from door to doctor (arrival to first provider). CONCLUSION: The mixed results hint at an effective but iterative process of enhancing patient flow and experience in the ED through senior physician triage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".