Using Breakthrough Series Collaborative Methodology to Improve Safe Delivery of Chemotherapy in Ontario
Bibliographic record
Abstract
PURPOSE: Chemotherapy delivery is complex, involving multiple providers across settings to deliver safe, effective care. Cancer Care Ontario initiated a provincial breakthrough series collaborative, based on methodology from the Institute for Healthcare Improvement (IHI), to improve the safe delivery of chemotherapy, from ordering through preparation and administration. METHODS: Over the 1-year period of the collaborative, three in-person sessions educated participants on improvement methodology. Twenty teams tested and implemented elements of a predefined change package in their local systems. Monthly teleconferences supplemented the education while encouraging a culture of knowledge sharing. Teams completed monthly self-assessment surveys that evaluated their progress using a 6-point scale, where 1 indicated no evidence of improvement and 5 indicated achievement of all goals and improvement objectives. RESULTS: Monthly self-assessment surveys revealed that over time, scores improved from 1 to 4, indicating significant progress. Moreover, 100% of participants reported in an exit survey that the collaborative had improved the culture of safety in their organizations. The gains of the collaborative have been sustained through development of a practice community and provision of ongoing coaching through the IHI Open School. CONCLUSION: Participation in the collaborative enabled local interdisciplinary teams to develop processes and structures to support ongoing quality improvement, including formation of a sustainable structure for knowledge translation and exchange. However, lack of a shared provincial target limited overall evaluation. Other lessons learned included providing adequate time for planning and clearly defining roles and responsibilities of involved teams and project sponsors.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".