Using emergency physicians’ abilities to predict patient admission to decrease admission delay time
Bibliographic record
Abstract
BACKGROUND: In many EDs, emergency physicians (EPs) do not have admitting privileges and must wait for consultants to further assess and admit patients. This delays bed requests and increases ED crowding. We measured EPs' abilities to predict patient admission prior to consultation and estimated the potential ED stretcher time saved if EPs requested a bed with consultation. METHODS: We conducted a prospective cohort study in an academic centre in Canada between October 2017 and February 2018 using a convenience sample of ED patient encounters requiring consultation. We excluded patients under 18 years or those clearly likely to be admitted (traumas, strokes, S-T elevation myocardial infarctions and Canadian Triage and Acuity Scale of 1). EPs predicted patient admission just before consultation. Potential ED stretcher time saved was estimated for correctly predicted admissions assuming bed requests were initiated with consultation and a constant time to inpatient bed. RESULTS: Characteristics of 454 patients were: mean age 60.1 years, 48.5% male, 46.9% evening presentation, 69.4% admitted and median time to bed request of 3.5 hours (IQR 2.0-5.3 hours). Overall, EPs prediction sensitivity, specificity, positive predictive value and negative predictive value were 90.5% (95% CI 86.7% to 93.5%), 84.2% (95% CI 77.0% to 89.8%), 92.8% (95% CI 89.8% to 95.0%) and 79.6% (95% CI 73.4% to 84.7%). Approximately 922.1 hours of ED stretcher time could have been saved during the 5-month study period if EPs initiated a bed request with consultation. CONCLUSION: Crowding is a reality for EDs worldwide, and many systems could benefit from EP-initiated hospital admissions to decrease the amount of time admitted patients wait in the ED.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.111 | 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".