OP24 A Framework For Improved Systems Of Care In Myocardial Infarction
Bibliographic record
Abstract
INTRODUCTION: In the past decade numerous efforts have been made to enhance quality of care in the province of Québec for patients with ST-elevation myocardial infarction (STEMI). Despite two prior field evaluations and diffusion of a systematic review as well as recommendations, a third audit revealed persistent gaps in care, specifically excessive treatment delays. Our cardiovascular evaluation unit thus aimed to develop a more comprehensive quality improvement framework that further engaged healthcare professionals. METHODS: A literature update identified best practices and ways to reduce treatment delays and improve outcomes. This review, combined with the latest evaluation results, was used to establish structural and process quality standards adapted to the Québec context, via a consensus process with a panel of clinical experts. The standards identified quality-of-care targets and key elements of a governance structure to guide the improvement process. Quality indicators to monitor change were also developed. An implementation plan was then created, likewise based on literature and evaluation results. RESULTS: For the first time, the unit publicly disseminated the results of the third evaluation according to region, in addition to standard individual hospital “report cards”. A summit conference was held during which the standards and indicators were presented to clinicians and other stakeholders, in collaboration with the health ministry and a panel of cardiovascular experts. Site visits are planned to facilitate change and establishment of local improvement plans and committees. A “tool kit” was developed containing a treatment algorithm, a drug protocol, five quality indicators each for processes and care networks, and measurement tools for indicators. A 75 percent minimal achievement target was set for treatment times. CONCLUSIONS: A comprehensive framework aimed at improving quality of care for STEMI patients and monitoring change was created by combining evidence from the literature and “real world” data and mobilizing key stakeholders.
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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 | 0.043 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".