Internal medicine consults within the emergency department: A workflow intervention to match workforce with workload
Bibliographic record
Abstract
Background. Emergency departments (ED) are increasingly overcrowded, exacerbated by patients awaiting consultations and inpatient beds. Internal Medicine (IM) is the most consulted specialty service from the ED. Patients experience long delays after being consulted to Internal Medicine (IM). Objective. Decrease these delays by improving the IM consult process. Methods. A three stage process was designed. First: Analysis of the IM consult workflow over a 27-day period to identify the longest delays. Then implement a data-driven intervention. Second: Quantitatively assess the intervention by comparing three-month periods Pre-Intervention (Time A) with Post-Intervention (Time B). Third: Qualitatively assess the intervention by surveying residents. Results. The first stage included 398 consults. The longest delays were awaiting bed availability post admission order completion (mean 19.5 hours) and awaiting the initial junior resident assessment (mean 3.6 hours). This assessment delay significantly increased during busy shifts. The intervention involved the addition of an extra IM resident during the busiest four hours of the day. The second stage compared 1162 patients from Time A with 1263 patients from Time B. There was no significant change in assessment times post-intervention. However, there was an 8% increase in consult volume in Time B as compared to Time A. The third stage captured 100% of the 23 senior residents. Overall, residents reported the change as beneficial to themselves and the patients. Conclusions. For patients consulted to IM in the ED, inpatient bed availability contributed to the largest delay to leave the ED. Increasing IM resident staffing during peak hours did not decrease time to complete consults. However, increased IM resident staffing was perceived as beneficial to both residents and patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".