Measuring and improving cervical, breast, and colorectal cancer screening rates in a multi-site urban practice in Toronto, Canada
Bibliographic record
Abstract
Our Family Health Team is located in Toronto, Canada and provides care to over 35 000 patients. Like many practices in Canada, we took an opportunistic approach to cervical, breast, and colorectal cancer screening. We wanted to shift to a proactive, population-based approach but were unable to systematically identify patients overdue for screening or calculate baseline screening rates. Our initiative had two goals: (1) to develop a method for systematically identifying patients eligible for screening and whether they were overdue and (2) to increase screening rates for cervical, breast, and colorectal cancer. Using external government data in combination with our practice's electronic medical record, we developed a process to identify patients eligible and overdue for cancer screening. After generating baseline data, we implemented an evidence-based, multifaceted intervention to improve cancer screening rates. We sent a personalized reminder letter to overdue patients, provided physicians with practice-level audit and feedback, and improved our electronic reminder function by updating charts with accurate data on the Fecal Occult Blood Test (FOBT). Following our initial intervention, we sought to maintain and further improve our screening rates by experimenting with alternative recall methods and collecting patient feedback. Screening rates significantly improved for all three cancers. Between March 2014 and December 2016, the cervical cancer screening rate increased from 60% to 71% (p<0.05), the breast cancer screening rate increased from 56% to 65% (p<0.05), and the overall colorectal screening rate increased from 59% to 70% (p<0.05). The increase in colorectal screening rates was largely due to an increase in FOBT screening from 18% to 25%, while colonoscopy screening remained relatively unchanged, shifting from 45% to 46%. We also found that patients living in low income neighbourhoods were less likely to be screened. Following our intervention, this equity gap narrowed modestly for breast and colorectal cancer but did not change for cervical cancer screening. Our future improvement efforts will be focused on reducing the gap in screening between patients living in low-income and high-income neighbourhoods while maintaining overall gains.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".