Creating Sustained Improvements in Patient Access and Flow: Experiences from Three Ontario Healthcare Institutions
Bibliographic record
Abstract
Ensuring that patients receive timely, high-quality healthcare is the highest priority of Ontario's hospitals, physicians and nurses. Given that the emergency department (ED) is often the "front door" to our healthcare system, developing approaches to improve access and flow in the ED is important - made more challenging by rising patient demand and acuity. Long-standing efforts to improve the ED system have outlined promising approaches and pushed access and flow up the priority list. Recently, in partnership with the Ministry of Health and Long-Term Care (MOHLTC), several Ontario hospitals participated in an intensive and sustained effort to improve access and flow, with promising results. Participants in these efforts described the initiatives as transformational, and the results have been promising and sustained. This article chronicles the efforts of three hospitals to enable other hospitals, physicians and nurses to learn from these experiences and gain confidence that a similar impact can be achieved in their facilities. Specifically, it discusses the following: The three pillars of sustainable transformation. Hospital case studies. St. Joseph's Health Centre (SJHC), Toronto. London Health Sciences Centre (LHSC) - University Hospital. University Health Network (UHN) - Toronto General and Toronto Western. Advice for other hospitals
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".