Increases in Emergency Department Occupancy Are Associated With Adverse 30‐day Outcomes
Bibliographic record
Abstract
OBJECTIVES: The associations between emergency department (ED) crowding and patient outcomes have not been investigated comprehensively in different types of ED. The study objective was to examine the associations of changes over time in ED occupancy with patient outcomes in a sample of EDs that vary by size and location. A secondary objective was to explore whether the relationship between ED occupancy and patient outcomes differed by ED characteristics (size/type and medical and nursing staffing ratios). METHODS: Using linked administrative databases, the authors constructed a cohort of 677,475 patients who visited one of 42 hospital EDs with complete data for 2005 on ED bed and waiting room occupancy. Crowding was measured with the relative occupancy ratio separately for ED bed and waiting room patients, defined as the ratio of ED occupancy on the day of the index ED visit to the average annual occupancy at that same ED. Multivariable logistic regression (adjusting for patient and ED characteristics) was used to analyze 30-day outcomes: mortality, return ED visits, and hospital admission at the first return ED visit. RESULTS: After adjustment for ED and patient characteristics, a 10% increase in ED bed relative occupancy ratio was associated with 3% increases in death and hospital admission at a return visit. A 10% increase in ED waiting room crowding was associated with a small decrease in return visits. There was a stronger association between bed crowding and mortality among larger EDs. CONCLUSIONS: In Quebec EDs, increases in bed occupancy are associated with an increase in the rates of 30-day adverse outcomes, even after adjustment for patient and ED characteristics. The results raise important concerns about the quality of care during periods of ED crowding.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".