Development of Electronic Medical Record Content Standards to Collect Pan-Canadian Primary Health Care Indicator Data
Bibliographic record
Abstract
In 2006 the Canadian Institute for Health Information (CIHI) released a set of 105 pan-Canadian Primary Health Care (PHC) indicators. This was followed by an assessment of data gaps, which prevented the calculation of the indicators, and the data collection options available to close the gaps. A quality review of Electronic Medical Record (EMR) data indicated a requirement for content standards. In order to assist the provinces as they developed requests for proposal for PHC-based EMRs, the EMR content standards project was born. Considerable effort was made to identify standards for the Electronic Health Record (EHR) including existing national and international EHR content. As well, CIHI attempted to align the content standards with those of other projects such as the Physician Office System Requirements (POSR). The outcome of this project was a set of EMR content standards for 12 pan-Canadian PHC indicators. The standards will be used to develop a prototype of a PHC reporting system that collects and analyzes data to generate clinical quality indicators for regional and longitudinal comparisons. In late 2008, CIHI will release the pan-Canadian PHC Core Reporting Data Set. This project has developed EMR content standards to better understand PHC in Canada.
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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 | 0.122 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".