Evolution of a National Approach to Evaluating the Benefits of the Electronic Health Record
Bibliographic record
Abstract
Demonstrating the value of eHealth investments to users and other stakeholders, and driving ongoing optimization of benefits are increasingly important components of eHealth implementations in Canada. For these reasons, Canada Health Infoway is working with provincial partners to make research and evaluation an important component in Electronic Health Record (EHR) investments. Leading provinces working with Infoway, including British Columbia, the Province of Quebec, and Newfoundland and Labrador, are making evaluation a central part of their eHealth strategies and using evaluation results to optimize the benefits of investments. We provide an overview of benefits evaluation strategies and lessons learned. Examples of early evaluations completed and those currently underway provide insights into the challenges and benefits of investments in EHR.
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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 | 0.324 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| 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".