Implementation and evaluation of a national quality improvement initiative in cancer surgery
Bibliographic record
Abstract
BACKGROUND: In 2017, the Canadian Partnership Against Cancer, a Canadian federally sponsored organisation, initiated a national multijurisdictional quality improvement (QI) initiative to maximise the use of synoptic data to drive cancer system improvements, known as the Evidence for Surgical Synoptic Quality Improvement Programme. The goal of our study was to evaluate the outcomes, determinants and learning of this nationally led initiative across six jurisdictions in Canada, integrating a mix of cancer surgery disease sites and clinicians. METHODS: A mixed-methods evaluation (surveys, semistructured interviews and focus groups) of this initiative was focused on the ability of each jurisdiction to use synoptic reporting data to successfully implement and sustain QI projects to beyond the completion of the initiative and the lessons learnt in the process. Resources provided to the jurisdictions included operational funding, training in QI methodology, national forums, expert coaches, and ad hoc monitoring and support. The programme emphasised foundational concepts of the QI process including data literacy, audit and feedback reports, communities of practice (CoP) and positive deviance methodology. RESULTS: 101 CoP meetings were held and 337 clinicians received feedback reports. There were 23 projects, and 22 of 23 (95%) showed improvements with 15 of 23 (65%) achieving the proposed targets. Enablers of effective data utilisation/feedback reports for QI included the need for clinicians to trust the data, have comparative data for feedback, and the engagement of both data scientists and clinicians in designing feedback reports. Enablers of sustainability of QI within each jurisdiction included QI training for clinicians, the ability to continue CoP meetings, executive and broad stakeholder engagement, and the ability to use pre-existing organisational infrastructures and processes. Barriers to continue QI work included lack of funding for core team members, lack of automated data collection processes and lack of clinician incentives (financial and other). CONCLUSION: Success and sustainability in data-driven QI in cancer surgery require skills in QI methodology, data literacy and feedback, dedicated supportive personnel and an environment that promotes the process of collective learning and shared accountability. Building these capabilities in jurisdictional teams, tailoring interventions to facility contexts and strong leadership engagement will create the capacity for continued success in QI for cancer surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".