Non-urgent emergency department use and primary health care bypass in Saudi Arabia: a systematic review of determinants and outcomes
Bibliographic record
Abstract
Non-urgent emergency department (ED) use and primary healthcare (PHC) bypass result in crowding in Saudi Arabia EDs. This review aimed to summarize determinants and outcomes to inform management strategies. Electronic databases were searched from 2014 to 2025 for observational Saudi studies evaluating non-urgent Canadian Triage and Acuity Scale (CTAS) 4 and 5 ED visits or PHC bypass. Eligible designs were retrospective analyses and cross-sectional surveys. A total of 6 original articles were included in this systematic review, including two chart reviews and four surveys in tertiary and regional hospitals. Non-urgent cases burden was high: CTAS 4 and 5 formed 61.4% of patients in the largest study; 78.5% of non-life-threatening attendees were triaged non-urgent. Common motives include routine investigations, medication refills, and mild upper respiratory symptoms. Determinants include convenience, appointment unavailability, limited PHC diagnostics, and low awareness of PHC services. Most visits ended with prescriptions and basic laboratory tests; radiology use was not needed in most cases, as well as admissions and specialty referrals. Non-urgent ED visits are mainly affected by access and knowledge gaps and require high acuity interventions. Hence, ED demand can be decreased by expanding after-hours PHC capacity, offering rapid appointments, diagnostics, and implementing electronic referral.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".