Cross-Canada EMR Case Studies: Analysis of Physicians' Perspectives on Benefits and Barriers
Bibliographic record
Abstract
Objective: Our objective was to provide physicians with practical information on best practices and lessons learned with regards to implementation and use of electronic medical record (EMR) systems in ambulatory clinical practice settings. Methodology: A cross-Canada EMR study—the first of its kind—used case study methodology to investigate how EMRs were implemented and used in primary care. Knowledge transfer methods included print and web publications by the Canadian Medical Association (CMA) and a workshop. Results: The 20 case studies informed us in detail of the critical success factors for implementation. These were validated and augmented through a workshop. Conclusions: Electronic medical record (EMR) uptake in Canada and the US significantly lags behind other countries. Hence, there is a need to spread the good news about the actual benefits of EMRs to patients, physicians and the health care system and to mitigate barriers to EMR adoption and use.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".