Overcrowding and Its Association With Patient Outcomes in a Median-Low Volume Emergency Department
Bibliographic record
Abstract
BACKGROUND: Crowding occurs commonly in high volume emergency departments (ED) and has been associated with negative patient care outcomes. We aim to assess ED crowding in a median-low volume setting and evaluate associations with patient care outcomes. METHODS: This was a prospective single-center study from November 14, 2016 until December 14, 2016. ED crowding was measured every 2 h by three different estimation tools: National Emergency Department Overcrowding Score (NEDOCS); Community Emergency Department Overcrowding Score (CEDOCS); and Severely-overcrowding Overcrowding and Not-overcrowding Estimation Tool (SONET) categorized under six different levels of crowding (not busy, busy, extremely busy, overcrowded, severely overcrowded, and dangerously overcrowded). Crowding scores were assigned to each patient upon ED arrival. We evaluated the distributions of crowding and patient ED length of stay (ED LOS) across estimation tools. Accelerated failure time models were utilized to estimate time ratios and their corresponding 95% confidence intervals comparing median LOS across levels of crowding within each estimation tool. RESULTS: This study comprised 2,557 patients whose median ED LOS was 150 min. Approximately 2% of patients arrived during 2 h time intervals deemed overcrowded regardless of the crowding tool used. Median ED LOS increased with the increased level of ED crowding and prolonged median ED LOS (> 150 min) occurred at ED of extremely busy status. Time ratios ranged from 1.09 to 1.48 for NEDOCS, 1.25 - 1.56 for CEDOCS, and 1.26 - 1.72 for SONET. CONCLUSION: Overcrowding rarely occurred in study ED with median-low annual volume and might not be a valuable marker for ED crowding report. Though similar patterns of prolonged ED LOS occurred with increased levels of ED crowding, it seems crowding alerts should be initiated during extremely busy status in this ED setting.
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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 | 0.007 | 0.037 |
| 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 | 0.001 |
| 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".