CANES family health team EMR optimization pilot.
Bibliographic record
Abstract
129 Background: While Electronic Medical Records (EMRs) have been widely adopted within primary care, practices are at various stages of maturity in their use. Cancer Care Ontario (CCO) has embarked on an EMR Optimization initiative, in collaboration with primary care providers (PCPs) and OntarioMD. The project is intended to support PCPs in leveraging the functionality in their EMR systems in order to support cancer screening through the development of training guides and tools. Working with the CANES Family Health Team (FHT), CCO is piloting the usefulness and effectiveness of these tools in meeting these objectives and using the results to inform how to support provincial pilots. Methods: The EMR Optimization guides and tools provide practices with training on five key areas: practice workflows, data discipline, searches, reminders and preventative care summary reports. Working with a multi-disciplinary team at the CANES FHT, CCO provided the team with the EMR Optimization tools and training on these five key areas. The training was supplemented with exercises on topics such as data discipline and practice workflow. The practice team then had opportunities to work with the guides and tools and integrate them into their daily workflow. Results: Preliminary results of an EMR maturity survey show that even after a short period of time, the team has developed an awareness of and a focus on improving data quality through standardization. In addition, they have learned how to leverage the functionality of their EMR system for preventative care purposes. Participant feedback indicates that they found the EMR guide and tip-sheets easy to understand and follow. Conclusions: The CANES pilot has resulted in practice staff moving up the EMR maturity curve and has taught the team how to leverage their EMR to support cancer screening. In addition, the pilot has helped inform CCO’s training strategy and refine the guides and tools. The next steps are to develop self-directed training tools and resources that will support a broader scale pilot to be implemented throughout 14 regions in Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".