A novel approach to improving emergency department consultant response times
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) overcrowding is a threat to patient safety and public health. Availability of specialty consultation to the ED may contribute to overcrowding. We implemented a novel intervention using education, goal setting and real-time performance feedback to improve time to admission for patients referred to general internal medicine (GIM). METHODS: Using a time-series design, we examined the effects of a quality improvement intervention on ED wait-times in an academic medical centre. The multifaceted approach included a didactic session for GIM housestaff on medicine triage principles and methods; setting a goal to have disposition decisions and, where appropriate, admission order within 4 h of consultation request; and providing personal data feedback on their performance on this metric to GIM housestaff during their rotation on the inpatient teaching service over a 1-year period. We compared time from consultation request to disposition decision and overall ED length of stay (LOS) for all patients referred to GIM during the intervention period (February 2011-February 2012) with data from the control period (January 2010-January 2011). RESULTS: Mean time from GIM consultation request to admission order entry decreased by 92 min (SD, 5, p<0.05) from 321min in the control period to 229 min in the intervention period. Overall ED LOS for GIM patients decreased by 59 min (SD, 14, p<0.05) for admitted patients from 1022 min in the control period to 963 min in the intervention period, and by 40 min (SD, 13, p<0.05) for all patients referred to GIM. GIM staffing and patient characteristics remained stable across the two periods. DISCUSSION: ED throughput for admitted medical patients improved with a quality improvement initiative involving education, goal setting and performance feedback.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".