The Effect of Urgent Care Centers on Emergency Department KPIs: An Institution-based Study
Bibliographic record
Abstract
Background: Emergency departments in Saudi Arabia face increasing pressure due to high patient volumes and inappropriate utilization by non-urgent cases, leading to overcrowding and delays in care. Urgent care centers have been introduced as an intermediate solution to divert non-life-threatening cases and improve the operational efficiency of Emergency departments. Objective: This study evaluated the impact of introducing an urgent care center at Jubail General Hospital, focusing on key performance indicators, the volume of non-urgent visits, and patient satisfaction levels. Method: A quasi-experimental, before-and-after design was adopted to analyze 157,925 valid emergency department records collected between January 2021 to June 2024. Four key performance indicators were assessed: door to doctor time, doctor to decision time, decision to disposition time, and door to disposition time (percentage seen within four hours). Patient acuity was measured using the Canadian Triage and Acuity Scale, and satisfaction was evaluated through Press Ganey surveys. Results: The implementation of the UCC was significantly associated with improved ED performance metrics. Gamma regression analysis showed that door-to-doctor time increased (95% CI: 1.571–1.608; p < .001), while doctor-to-decision time (95% CI: 0.606–0.620; p < .001), decision-to-disposition time (95% CI: 0.826–0.846; p < .001), and door-to-disposition time (95% CI: 0.971–0.994; p = .002) significantly decreased. The proportion of non-urgent ED visits decreased from 52.2% to 43.5% (p < .001), indicating a 16.7% relative reduction in non-urgent caseload, with logistic regression confirming a 29.4% reduction in odds post-intervention (OR = 0.706; 95% CI: 0.690–0.722). CTAS distribution shifted significantly following UCC implementation (p < .001): Level 1 cases increased from 0.1% to 0.2%, Level 2 cases decreased from 1.5% to 1.1%, and Level 3 cases rose from 46.3% to 55.3%, indicating a higher share of moderate-acuity patients. Conversely, low-acuity Level 4 and Level 5 cases declined from 36.3% to 31.3% and 15.8% to 12.2%, respectively. Patient satisfaction, analyzed using two-way ANOVA weighted by domain sample size, showed statistically significant variation across domains (p < .05), with the highest ratings in the test’s domain and the lowest in the arrival domain. However, overall satisfaction scores remained unchanged. Conclusion: In conclusion, UCC implementation improved ED performance and led to a more appropriate use of emergency services, supporting broader healthcare efficiency goals under Saudi Vision 2030.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".