Emergency department crowding negatively influences outcomes for adults presenting with asthma: a population-based retrospective cohort study
Bibliographic record
Abstract
Abstract Background Access to emergency department (ED) services is important for patients with acute asthma; however, ED crowding may impact the quality of care and compromise outcomes. We examine the association between ED crowding metrics and individual patient outcomes for adults presenting with asthma. Methods This population-based retrospective cohort study extracted all ED presentations made by patients aged 18 to 55 years to 18 high-volume EDs in Alberta from April 2014 to March 2019. Physician initial assessment (PIA) time and ED length of stay (LOS) for discharged and admitted patients were calculated. Other metrics and patient outcomes were also obtained. Linear and generalized linear models were fit for continuous and categorical outcomes. Cox proportional hazards models were used for time-to-event outcomes. Results There were 17,724 ED presentations by 12,569 adults. The median age was 33 years, and females (58.7%) made more presentations. ED crowding affected the PIA time for all triage groups. For the high acuity group (Canadian Triage and Acuity Scale [CTAS] 1/2), 1 h increase in median facility-specific PIA was associated with 26 min (95%CI: 24,28) increase; for the moderate acuity (CTAS 3) and low acuity (CTAS 4/5) groups, the individual-level PIA increased by 54 min (95%CI: 53,55) and 61 min (95%CI: 59,63), respectively adjusted by other predictors. Increases in facility PIA resulted in increase in odds of admissions for the high acuity group and increase odds of left without completion of care for the moderate and low acuity groups. Conclusion The care provided for patients from all triage groups was impacted when EDs experienced crowding. Effective interventions are needed to mitigate ED crowding and improve care and outcomes for this important patient group.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".