Quality improvement study in emergency department waiting room times in a private hospital in Kuwait
Bibliographic record
Abstract
Background: The emergency department (ED) waiting room duration has been a challenge in many EDs. This study has implemented a focus plan-do-study-act (PDSA) cycle study on ED waiting room patients in a private hospital in Kuwait. Each PDSA cycle included one of the following interventions: hospitality measures, number of triage nurses, number of ED physicians, and number of receptionists to see their impact on the ED waiting times. Methods: The waiting times were collected per patient coming into the ED and their assigned Canadian Triage Acuity Scale (CTAS) level over a 12-month period, as well as the number of patients left without being seen (LWBS). Each intervention was introduced into the ED and a 2-month period following each was given to see the effect on the waiting time. Results: As divided per CTAS level, there were 38,157 patients included in the analysis. The results showed that for every increase in one triage nurse, there was a reduction of 15.09, 20.7, and 20.8 minutes for CTAS 3, 4, and 5 patients, respectively, and for every increase in one doctor there was a reduction in the total ED waiting room time of 11.4, 10.0, and 8.6 minutes for CTAS 3, 4, and 5 patients, respectively, keeping all other variables constant. These quality parameters reduced the LWBS from 6.1% to 2.5%. Conclusion: This study concluded that increasing triage nurses and ED physicians successfully reduces total ED waiting room times and reduces the number of patients LWBS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".