Improving emergency department flow through Rapid Medical Evaluation unit
Bibliographic record
Abstract
The Toronto Western Hospital is an academic hospital in Toronto, Canada, with an annual Emergency Department (ED) volume of 64,000 patients. Despite increases in patient volumes of almost six percent per annum over the last decade, there have been no commensurate increases in resources, infrastructure, and staffing. This has led to substantial increase in patient wait times, most specifically for those patients with lower acuity presentations. Despite requiring only minimal care, these patients contribute disproportionately to ED congestion, which can adversely impact resource utilization and quality of care for all patients. We undertook a retrospective evaluation of a quality improvement initiative aimed at improving wait times experienced by patients with lower acuity presentations. A rapid improvement event was organized by frontline workers to rapidly overhaul processes of care, leading to the creation of the Rapid Medical Evaluation (RME) unit - a new pathway of care for patients with lower acuity presentations. The RME unit was designed by re-purposing existing resources and re-assigning one physician and one nurse towards the specific care of these patients. We evaluated the performance of the RME unit through measurement of physician initial assessment (PIA) times and total length of stay (LOS) times for multiple groups of patients assigned to various ED care pathways, during three periods lasting three months each. Weekly measurements of mean and 90th percentile of PIA and LOS times showed special cause variation in all targeted patient groups. Of note, the patients seen in the RME unit saw their median PIA and LOS times decrease from 98min to 70min and from 165min to 130min, respectively, from baseline. Despite ever-growing numbers of patient visits, wait times for all patients with lower acuity presentations remained low, and wait times of patients with higher acuity presentations assigned to other ED care pathways were not adversely affected. By specifically re-purposing a fraction of existing staff, resources, and infrastructure for patients with lower acuity presentations, we were able to streamline their care and decrease wait times in the ED. These results were achieved through the incremental improvements afforded by rapidly cycling through PDSA cycles, with strong frontline staff involvement and continuously eliciting feedback for improvement. We believe the model to be replicable in other academic medical centres.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".