Development of a Draft Pan-Canadian Primary Health Care Electronic Medical Record Content Standard
Bibliographic record
Abstract
In collaboration with a broad range of stakeholders, the Canadian Institute for Health Information (CIHI) led the development of the draft pan-Canadian primary health care (PHC) electronic medical record (EMR) content standard to be used in EMR applications across the country to support PHC data capture and information use and improved health system management. To achieve this goal, CIHI initiated the following activities: stakeholder engagement, information requirements gathering and adoption and implementation promotion of the common content standard for wide-spread use. The resulting pan-Canadian standardized data set will allow consistent data capture that will improve understanding and ability to report on PHC utilization and access, chronic disease prevention and management, health promotion, medication usage, patient safety, quality of care including patient safety and outcomes. The standard will improve patient care information by providing the structured comparable information needed to care for patients over time and across the continuum of care. Standards support clinical practice reminders and alerts, improvements in operating efficiencies, onscreen feedback reports to PHC providers and the ability to look at clinical trends over time. This standard will improve the flow of information by providing standardized information to providers at points on the continuum of care leading to better coordination of care and a reduction of repeat tests. Lastly, a common content standard will improve the health system use of data; by enabling aggregation and analysis of comparable standardized health information, clinicians, jurisdictions, and regions can benefit from using this data for more effective planning and policy decisions. The jurisdictions and clinicians, supported by CIHI and Canada Health Infoway will continue to work together with other key stakeholders, such as vendors to support the adoption and implementation of this standard into future jurisdictional EMR vendor specifications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".