Evaluation of Emergency Health-Care Initiatives to Reduce Overcrowding in a Referral Medical Complex, Jeddah, Saudi Arabia
Bibliographic record
Abstract
Purpose: Excessive delays and emergency department (ED) overcrowding have become an increasingly major problem for public health worldwide. This study was to assess the key strategies adopted by an ED, at a public hospital in Jeddah, to reduce delays and streamline patient flow. Materials and Methods: This study was a service evaluation for a Saudi patient population of all age-groups who attended the ED of a public hospital for the period between June 2016 and July 2019. The Saudi initiative to reduce the ED visits at the King Abdullah Medical Complex hospital has started on August 7, 2018. The initiative was to apply an urgency transfer policy which outlines the procedures to follow when patients arrive to the ED where they are reviewed based on the Canadian Triage and Acuity Scale (CTAS). Patients with less-urgent conditions (category 4 and 5) are referred to a primary health-care practice (where a family medicine consultant is available). Patients with urgent conditions (category 1–3) are referred to a specialized health-care centre if the service is not currently provided. To test the effectiveness of ED initiative on reducing the overcrowd, data were categorized into before and after the initiative. The bivariate analysis χ2 tests and 2 sample t-tests were run to explore the relationship of gender and age with dependent variable emergency. Results: A total of 233,998 patients were included in this study, 61.8% of them were males and the average age of ED patients were 35.5 ± 18.6 years. The majority of cases were those classified as “less urgent” (CTAS 4), which accounted for 65.4%. Number of ED visits before and after the initiative was 67 and 33%, respectively. ED waiting times after the initiative have statistically significantly decreased across all acuity levels compared to ED waiting times before the initiative. Conclusion and Implication: The findings suggest that the majority of patients arrive to the ED with less-urgent conditions and arrived by walking-in. The number of cases attending the ED significantly decreased following the introduction of the urgency transfer policy. Referral for less-urgent patients to primary health-care centre may be an important front-end operational strategy to relieve congestion.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".