Analysis of Emergency Department Use by Non-Urgent Patients and Their Visit Characteristics at an Academic Center
Bibliographic record
Abstract
Objective: We studied the extent and reasons for non-urgent emergency department (ED) visits in a single university hospital, their predictors, and patient outcomes to propose solutions suitable for Middle Eastern healthcare systems. Design: We conducted a retrospective review of electronic medical records, including all non- and less-urgent ED visits with complete triage records (levels 4 and 5 triage based on the Canadian Triage and Acuity Scale (CTAS) over one year. The data on patient demographics, visit characteristics, and patient disposition were analyzed using SPSS software. Setting: The study was conducted in the ED at King Abdullah Bin Abdul-Aziz University Hospital (KAAUH), a Saudi university hospital located within the campus of Princess Nourah Bint Abdulrahman University. Participants: A chart review was carried out for 18,880 patients with CTAS 4 or 5 visiting the KAAUH ED between July 2020 and July 2021. Additionally, a total of "11,857" patients with missing triage acuity or CTAS levels 1, 2, or 3 were excluded from the study. Results: The majority (61.4%) of the 30,737 ED visits were less-urgent or non-urgent. The most common reasons for non-urgent visits were routine examination/investigation (40.9%), medication refilling (14.6%), and upper respiratory tract infection/symptoms (9.9%). Most visits (73.4%) were during weekdays and resulted in the prescription of medication (94.2%), laboratory tests (62.8%), sick leaves (4.7%), radiology examinations (3.6%), and a visit to primary healthcare clinics (family medicine) within a week of the emergency visit (3.6%). Conclusion: Less- and non-urgent ED visits often did not need any further follow-ups or admission and represented a burden better managed by a primary healthcare center. Policymakers should mitigate unnecessary ED visits through public awareness, establish clear regulations for ED visits, improve the quality of care in primary healthcare centers, facilitate booking for outpatient department appointments, and regulate the systems of payment coverage/insurance and referral by other organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".