Bibliographic record
Abstract
OBJECTIVE: To determine the rate of adoption of electronic medical records (EMRs) by physicians across Canada, provincial incentives, and perceived benefits of and barriers to EMR adoption. DATA SOURCES: Data on EMR adoption in Canada were collected from CINAHL, MEDLINE, PubMed, EMBASE, the Cochrane Library, the Health Council of Canada, Canada Health Infoway, government websites, regional EMR associations, and health professional association websites. STUDY SELECTION: After removal of duplicate articles, 236 documents were found matching the original search. After using the filter Canada, 12 documents remained. Additional documents were obtained from each province's EMR website and from the Canada Health Infoway website. SYNTHESIS: Since 2006, Canadian EMR adoption rates have increased from about 20% of practitioners to an estimated 62% of practitioners in 2013, with substantial regional disparities ranging from roughly 40% of physicians in New Brunswick and Quebec to more than 75% of physicians in Alberta. Provincial incentives vary widely but appear to have only a weak relationship with the rate of adoption. Many adopters use only a fraction of their software's available functions. User-cited benefits to adoption include time savings, improved record keeping, heightened patient safety, and confidence in retrieved data when EMRs are used efficiently. Barriers to adoption include financial and time constraints, lack of knowledgeable support personnel, and lack of interoperability with hospital and pharmacy systems. CONCLUSION: Canadian physicians remain at the stage of EMR adoption. Progression in EMR use requires experienced, knowledgeable technical support during implementation, and financial support for the transcription of patient data from paper to electronic media. The interoperability of EMR offerings for hospitals, pharmacies, and clinics is the rate-limiting factor in achieving a unified EMR solution for 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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".