Resident-driven Quality Improvement Pre-post Intervention Targeting Reduction of Emergency Department Decision to Admit Time
Bibliographic record
Abstract
Long Emergency Department (ED) wait times represent a key point for quality improvement in many healthcare systems. A delayed ED disposition decision may lead to increased length of hospital stay, healthcare cost, and mortality. The objective of this resident-driven quality improvement (QI) intervention was to determine if a standardized resident admission protocol could reduce the ‘decision-to-admit’ (DTA) time of patients being assessed for admission to internal medicine (IM) at 3 tertiary care teaching hospitals. A standardized admission protocol was developed by a focus group of senior IM residents. DTA time data were tracked over a 6-month period, following implementation of the intervention. Residents identified potential barriers to timely DTA. A regular electronic newsletter summarized DTA time trends and reinforced the admission protocol. All data were extracted in aggregate form from a regional health authority database. There was an overall decline in DTA times for Medical Teaching Unit (MTU) admissions with our intervention. Over a 6-month period, when adjusted for junior learner numbers and admission volumes, DTA times at all 3 sites decreased by an average of 1.3 hours. Cost effectiveness analysis using a case mix group model yielded an average cost savings of $36.63 per admitted patient across the 3 sites. Reported barriers to admission included unclear patient disposition, high consult volume and unstable patient status. We have shown that a resident-driven QI intervention can be effective in reducing DTA times, and is cost saving to the healthcare system.
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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 | 0.001 | 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) | 0.003 | 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 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".