Improving Emergency Department flow through optimized bed utilization
Bibliographic record
Abstract
Over the last decade, patient volumes in the emergency department (ED) have grown disproportionately compared to the increase in staffing and resources at the Toronto Western Hospital, an academic tertiary care centre in Toronto, Canada. The resultant congestion has spilled over to the ED waiting room, where medically undifferentiated and potentially unstable patients must wait until a bed becomes available. The aim of this quality improvement project was to decrease the 90th percentile of wait time between triage and bed assignment (time-to-bed) by half, from 120 to 60 minutes, for our highest acuity patients. We engaged key stakeholders to identify barriers and potential strategies to achieve optimal flow of patients into the ED. We first identified multiple flow-interrupting challenges, including operational bottlenecks and cultural issues. We then generated change ideas to address two main underlying causes of ED congestion: unnecessary patient utilization of ED beds and communication breakdown causing bed turnaround delays. We subsequently performed seven tests of change through sequential plan-do-study-act (PDSA) cycles. The most significant gains were made by improving communication strategies: small gains were achieved through the optimization of in-house digital information management systems, while significant improvements were achieved through the implementation of a low-tech direct contact mechanism (a two-way radio or walkie-talkie). In the post-intervention phase, time-to-bed for the 90th percentile of high-acuity patients decreased from 120 minutes to 66 minutes, with special cause variation showing a significant shift in the weekly measurements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".