Data that Makes a Difference in Quality Improvements in Primary Health Care: Approaches through a Pan-Canadian Voluntary Electronic Medical Record Source
Bibliographic record
Abstract
Primary Health Care (PHC) is the most common health care experienced by Canadians and is an important source of chronic disease prevention and management; however, PHC providers say they have little information about their patient populations, especially groups of patients with multiple conditions. The Canadian Institute for Health Information in collaboration with 50 PHC providers examined the ability to extract and use a subset of PHC EMR data from four disparate environments in an agreed and privacy sensitive manner. Findings describing the feasibility of clinician engagement, EMR data extraction, EMR content standards and data utility gaps, information system requirements, and systemic enablers and barriers are described in this paper. Ability to collect and use discrete and standardized clinical and administrative information is fundamental to improving practice efficiency, optimal use of information, and patient quality of care. Improving quality of EMR data captured at the point of service will considerably enable our ability to measure and understand PHC across Canada; promote dialogue to identify priority information needs; and support health system information uses for clinical program and health system management, research, and population surveillance.
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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 | 0.216 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| 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".