Driving action through data: Delivering online cancer screening reports to primary care providers.
Bibliographic record
Abstract
174 Background: The Screening Activity Report (SAR), a supplementary tool for primary care providers (PCPs), was released in April, 2014. Providers are able to access this comprehensive report securely via an online solution and view the screening activity of their patients across Cancer Care Ontario (CCO)’s three organized cancer screening programs; breast, cervical and colorectal. The objectives of the SAR are to improve the quality of cancer screening by increasing provincial screening rates, improving the rate of appropriate follow-up of abnormal results and promote the alignment of cancer screening practices with CCO’s evidence-based clinical guidelines. Methods: CCO partnered with eHealth Ontario in 2012 to leverage their identity and access management system to provide safe and secure online access to the report. Since this time, CCO has implemented a multi-faceted campaign to support registrations to the system, encourage report access, and gather feedback on how to improve the report for future iterations. Using a detailed methodology developed by a wide range of subject matter experts at CCO, the SAR employs numerous provincial data sources to provide an overview of the patient rosters. Actionable categories are assigned at the patient level using a unique algorithm based on the latest clinical guidelines. Results: Previous to April 2014, the SAR was referred to as the ColonCancerCheck SAR (CCC SAR) as it included colorectal cancer screening data only. The last release of the CCC SAR was in October, 2013. At this time 4,824 providers were registered to the identity and access management system and adoption of this report had reached 31% after being available for five months to providers. To date, 4,992 providers are now registered and adoption of the April SAR has already reached 27% after being available for almost two months. Conclusions: The SAR is the first tool of its kind to make widespread use of eHealth’s identity and access management system service and target a broad user base of PCPs. The successful launch of the SAR has provided key insights into how technology can be leveraged to share provincial data in a meaningful way with providers and support them in improving the quality of cancer screening.
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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 | 0.035 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".