The Crucial Role of Clinician Engagement in System-Wide Quality Improvement: The Cancer Care Ontario Experience
Bibliographic record
Abstract
In 2004, Cancer Care Ontario's (CCO) role changed from providing direct cancer service to oversight, with a mission to improve the performance of the cancer system by driving quality, accountability and innovation in all cancer-related services. Since then, CCO has built a model for province-wide quality improvement and oversight--the Performance Improvement Cycle--that exemplifies the key elements of the Excellent Care for All Act, 2010. While ensuring that quality of the cancer system is by necessity a continuous process, the approach taken thus far has achieved measurable results and will continue to form the basis of CCO's future work. Clinician engagement has been critical to the success of CCO's approach to quality oversight and improvement. CCO uses a variety of formal and informal clinical engagement structures at each step of the Performance Improvement Cycle, and has developed operational processes to support quality improvement, and educational and mentorship programs to build clinician leadership capacity in that area. An example of sustained quality improvement in system performance is illustrated in a case study of the surgical treatment of prostate cancer. The improvement was achieved with strong collaboration across CCO's surgery and pathology clinical programs, with support from informatics staff.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".