Analyzing the Reasons and Hospital Admission Rates of 72‐Hour Emergency Department Revisits
Bibliographic record
Abstract
Introduction: Emergency department (ED) revisits are considered a significant indicator of the quality of care provided and are used as a benchmark for the performance of individual providers and institutions. The aim of this study is to assess ED revisit rates, reasons, and hospital admission rates among our adult ED patients. Methods: This is a retrospective chart review study conducted in a single-center tertiary referral hospital in Riyadh, Saudi Arabia. Study participants comprised adult patients who attended the ED, had been discharged, and had an ED revisit within 72 h from April 2019 to January 2020. Results: A total of 573 patients met our inclusion criteria, of whom 53.1% were males. The majority of the patients (74.5%) revisiting the ED were categorized as CTAS Level 3, with gastrointestinal complaints being the most common presentation for revisits (23.6%). During the second visit, 94%, 4%, 0.7%, and 0.3% of the participants were discharged, admitted, discharged against medical advice, and died, respectively. Disease progression was the most common cause of revisits at 96.5%. The factors that showed statistically significant associations with nondischarge disposition in the second visit were CTAS levels in the first and second visits, dementia, functional dependency, and reason for the revisit. Conclusion: Most ED visits within 72 h are due to disease progression rather than system- or physician-related issues, and the majority of these patients are safely discharged after the second visit. Identification of high-risk patients-such as those with higher CTAS levels, dementia, or functional dependency-may aid emergency physicians in implementing targeted discharge planning and coordinated outpatient follow-up to reduce unnecessary revisits and optimize use of emergency services. Our findings highlight the importance of structured post-discharge support and underscore the need for tailored interventions in resource-limited healthcare settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".