The impact of consultation on length of stay in tertiary care emergency departments
Bibliographic record
Abstract
BACKGROUND: Consultations in the emergency department (ED) are infrequently studied. This study quantifies the contribution of consultations to ED length of stay (LOS) and examines patient and consultation characteristics associated with prolonged ED LOS. METHODS: Prospective cohort study of a convenience sample of shifts by volunteering emergency physicians (EP) at two urban tertiary care Canadian EDs. EPs completed standardised forms on all patients for whom a consultation was requested. Medical chart reviews and secondary analyses of administrative databases were also performed. Factors associated with longer LOS were determined through linear regression modelling. RESULTS: 1180 patients received at least one consultation during study shifts and EPs completed data collection on 841 (71%) of these. Median patient age was 54 years, 53.3% were male, and 2.9% had documented dementia. Admitted patients receiving consultations had a longer overall LOS compared to discharged patients. Median time from triage to consultation request accounted for approximately 28% of the total median LOS in admitted patients compared to 46% for discharged patients. Consultation decision time accounted for 33% and 54% of the LOS for admitted and discharged patients, respectively. Linear regression modelling revealed that advanced age, longer latency between arrival and first consultation request, history of dementia and multiple consultations were significantly associated with longer LOS. Conversely, undergoing procedures while in the ED was associated with a shorter LOS. CONCLUSIONS: Consultation decision time contributes significantly to ED LOS. Further efforts are needed to validate these results in other ED settings and improve this aspect of ED throughput.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".