The impact of non-urgent visits on Emergency Department’s waiting time in Saudi Arabia general hospitals’ main regions: an observation study
Bibliographic record
Abstract
Abstract Background: Emergency department (ED) overcrowding is a serious issue worldwide. It is associated with longer waiting times for patients, which can have negative consequences for patient care. Non-urgent cases that are seen in the ED can often be treated in alternative primary health settings. This study aimed to assess the relationship between non-urgent patients' Canadian Emergency Department Triage and Acuity Scale (CTAS) scores and waiting times for urgent patients. Methods: This was a cross-sectional, retrospective observational study using secondary data. Data were presented as frequencies and means. T-tests and chi-square tests were used to assess the relationship between waiting times and level of cases' urgency. Results: A total of 136,668 ED visits were included in the study from three regions. The percentage of non-urgent cases ranged from 40% to 51%, which exceeded national and international acceptable levels. There was a significant difference between mean waiting times for urgent cases and non-urgent cases for the four key performance indicators (KPIs): door-to-doctor, doctor-to-decision, decision-to-disposition, and door-to-disposition (percentage of patients seen within 4 hours). Conclusion: The availability of non-urgent cases prolonged waiting times for urgent cases. Further measures need to be adopted to reduce waiting times and assess patient and visit characteristics for non-urgent cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".