A Platform to Collect Structured Data from Multiple EMRs
Bibliographic record
Abstract
Adoption and use of Electronic Medical Records (EMRs) is continuing to rise across Canada, leading to more data being generated. These data, however, are not being captured in a standardized manner, they are not available for research, surveillance or health system management, and they are not having a real-time impact on healthcare providers at the point of care. Multiple stakeholders, including researchers and system evaluators, require easy access to high quality, structured data. As current EMRs are not able to effectively meet their needs, we engaged multiple stakeholders to assist in designing a solution. A total of 90 stakeholders from various backgrounds participated in an iterative joint design process. After incorporating the feedback of all stakeholders, we developed the design for a scalable platform for capturing structured, evidence-based data from all EMRs in Canada for research, health system management, clinical decision support and other purposes. We discuss the design specification for our proposed solution and explain how, using clinical forms, we can not only capture structured, high quality data from multiple EMRs, but also provide real-time guideline advice to providers at the point of care. The scalability of this proposed solution across multiple diseases and multiple EMRs is also explained. We further discuss the benefits and limitations of this proposed solution to several key stakeholder groups and address issues of privacy and security.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".