Driving quality improvements of radiation treatment services through a centralized performance management approach.
Bibliographic record
Abstract
188 Background: Radiation treatment services are delivered in 16 facilities spread across the province of Ontario, centralized through Cancer Care Ontario’s oversight of quality of care, equipment, hospital funding, clinical and technical guidelines. Methods: In order to ensure high quality care, Cancer Care Ontario employs a systematic approach performance management, whereby facilities are held accountable to achieving provincial quality targets. For radiation treatment, the quality improvement priorities that have leveraged this approach over the last 10 years have included: reduction of wait times to consultation; reduction of wait times to start of treatment; adoption of Intensity Modulated Radiation Therapy (IMRT) where appropriate; and implementation of peer review for treatment plans. In each case, key performance indicators were developed for use in provincial scorecards designed to focus the attention of local clinical and administrative leadership. Regular performance discussions with senior leaders took place throughout implementation, and targeted intervention occurred with facilities that were lagging behind their peers. Results: See table. Conclusions: The ability to centrally monitor the implementation of quality improvement initiatives across a large jurisdiction, and to hold the leadership of each facility accountable to provincial targets through regular feedback and escalation, has been a key component of highly successful change management initiatives in radiation treatment in Ontario. [Table: see text]
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".