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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".