An approach to implementing meaningful change in a provincial health care system.
Bibliographic record
Abstract
120 Background: Radiation treatment (RT) is essential to cancer management, contributing to cure and symptom control. With increasing cancer incidence and treatment complexity, health systems must adapt to ensure patients (pts) receive the highest quality of care. Methods: With the objective of ensuring equitable access to high-quality, safe care, Cancer Care Ontario (CCO), a provincial government agency, identified provincial variability in RT activities. As a result, CCO prioritized 3 quality initiatives over the past 7 years: 1) Access to Intensity Modulated RT (IMRT) (2008-2013); 2) Peer Review of RT plans due to increasing RT planning complexity and the existence of high-profile RT errors (2012-present); and 3) Ensuring equitable access to RT (RT Utilization) (2014-present). Strategic plans were developed using change management framework adapted from the Kotter process for leading change (Kotter, JP. Harvard Bus Rev 73:59-67, 1995). In each initiative, CCO created a climate for change, engaged the provincial RT community to move priorities forward, and worked to sustain achieved gains. Results: CCO found that building a project team, communicating a clear understanding of goals and objectives, providing sufficient resources to cancer centres, and public reporting of results were key contributing success factors. IMRT project: Currently in sustainability phase. IMRT rates increased from 20% in 2008/09 - full implementation and target attainment in 2012/13. Public reporting continues. Peer Review of RT plans: Currently moving from engagement to implementation phase. Increase from 44% of RT cases undergoing peer review in 2013/14 to 68% in 2014/15. RT Utilization Project: Currently in engagement phase. Provincial shortfall of 11% in annual RT rates correlates to roughly 2500 pts who do not receive RT as needed. Engaging data experts and consulting with regional administrators, RT utilization is the current change priority for CCO’s RT program. Conclusions: These projects demonstrate the possibility of using change management practices to achieve quality improvement in healthcare. Ongoing work continues to ensure that pts in Ontario receive the highest quality cancer care.
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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 | 0.062 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".