Using Standardized Electronic Pathology and Surgery Data to Inform Clinical Quality Improvement and Health System Planning
Bibliographic record
Abstract
Background: Practice variation in diagnosis and treatment exists between clinicians and jurisdictions across Canada. This variation can impact the quality of care that patients receive and patient outcomes. Knowledge of the scale and type of variation is the first step to developing action plans to improve consistency and enhance patient care. Aim: We aimed to establish a method by which to examine the magnitude of practice variation between clinicians and interjurisdictionally within the cancer system. We leveraged and derived evidence from discrete pathology data collected by five Canadian jurisdictions at the point of care to identify areas to improve quality of cancer care services and to direct patient care. Methods: Fifty pathologists, surgeons, and medical oncologists from 10 jurisdictions conferred to leverage literature and data standards (developed by the College of American Pathologists (CAP)) to create 48 descriptive and outcome indicators related to five cancers: breast, lung, colorectal, endometrial, and prostate cancer. Five jurisdictions collected and used data to generate the indicators. This baseline data were reviewed by 65 clinicians. Results: Interjurisdictional comparative baseline data analyses on 48 indicators showed clinical validity and relevance for use to direct downstream patient care. Data characterizing cancer type, stage, and grade distribution were consistently reported across geography and aligned with the evidence noted in the literature. The data also noted practice and performance variation across multiple cancer sites. For example, although the recommended guideline is to examine at least 12 lymph nodes in 90% of colorectal cancer patients, only one province met this target. Another example is Lynch syndrome testing, which may be important for patients with a diagnosis of colorectal or endometrial cancer depending on the age at diagnosis and family history. The data showed that 0%–70% of patients diagnosed with colorectal cancer prior to age 70 received testing for Lynch syndrome, and only 10%–40% of endometrial cancer cases were tested for markers of Lynch syndrome across the country. The value of these indicators is enormous to inform potential training opportunities and set standards of care at the local or broader clinical governance level so that consistent, high-quality care is delivered in accordance with evidence-based guidelines. Conclusion: Practice variation exists between clinicians and jurisdictions, and comparative pathology data can be used to create a cancer learning system. Four jurisdictions are now embarking on leveraging indicator data analysis to generate physician-level feedback reports and convening communities of practice with the goal of facilitating peer-to-peer conversations, and establishing benchmarks and targets to improve the quality of care, refine or develop clinical guidelines, and inform health system planning in Canada. These lessons can be applied in other cancer systems.
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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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".