Sustainable improvement in quality indicators in Ontario's surgical oncology program (SOP)
Bibliographic record
Abstract
6638 Traditional approaches to quality improvement (QI) have in general utilized performance data against benchmarks, and pay for performance to drive quality. In 2002 the SOP of Ontario, Canada, population 12 million, combined such a QI strategy with philosophical, cultural change aimed on the development of a workforce capable of setting and implementing improvements through communities of practice (CoPs) and application of evidence. The objective was to facilitate implementation of a rigorously developed set of performance indicators, guidelines and standards of care. The most tangible examples of early success include the reduction in operative mortality of pancreatic cancer and the improvement in harvesting lymph nodes in colorectal cancer. A series of indicators, guidelines and standards for colorectal, prostate, thoracic, and hepatobiliary cancers, were developed in partnership with the program in evidence-based care utilizing a rigorous and transparent methodological framework. A value-based purchasing contract (volume-based funding dependent on submission of wait time data). Health service delivery research to identify gaps and evaluate success. CoPs approach to engaging surgeons in solving quality gaps, including opinion leaders, workshops, multidisciplinary teamwork and website tools. Significant improvements have been seen in colorectal and pancreatic caancer indicators. In pancreatic cancer they include a reduction in 30 day operative mortality, in Ontario from a mean of 10.2% in 1988–1996 to 4.5% in 2002–2004, a reduction in numbers of surgeons and hospitals performing pancreatic surgery, and an increase in volumes performed in high volume institutions. In colorectal cancer the compliance with the NCI guideline of 12 lymph nodes harvested increased from 27% in 1997–2000 to 69% in 2005. Oncologists have been engaged in solving system gaps through participation in guideline and standard development and implementation using the CoPs approach. Our experience supports the hypothesis that guidelines, standards and performance indicators, value- based purchasing, and CoPs social influence elements are essential components that can change practice and improve quality in a large jurisdiction cancer system. No significant financial relationships to disclose.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".