Analysis of Emergency Department Use by Non-Urgent Patients and Their Visit Characteristics at an Academic Center
Bibliographic record
Abstract
Sara Alnasser,1 Maryam Alharbi,1 Ahmad AAlibrahim,2 Ali Aal ibrahim,3 Osama Kentab,2 Wajdan Alassaf,2 Muna Aljahany1 1Department of Clinical Sciences, College of Medicine, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia; 2Department of Emergency, King Abdullah Bin Abdulaziz University Hospital, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; 3College of Medicine, Sulaiman Al Rajhi University, Bukairiyah, Saudi ArabiaCorrespondence: Muna Aljahany, Department of Clinical Sciences, College of Medicine, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia, Email msaljahany@pnu.edu.saObjective: 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.Keywords: emergency department, triage, non-urgent visits
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".