Implementation of electronic medical records: effect on the provision of preventive services in a pay-for-performance environment.
Bibliographic record
Abstract
OBJECTIVE: To study the effect of electronic medical record (EMR) implementation on preventive services covered by Ontario's pay-for-performance program. DESIGN: Prospective double-cohort study. PARTICIPANTS: Twenty-seven community-based family physicians. SETTING: Toronto, Ont. INTERVENTION: Eighteen physicians implemented EMRs, while 9 physicians continued to use paper records. MAIN OUTCOME MEASURE: Provision of 4 preventive services affected by pay-for-performance incentives (Papanicolaou tests, screening mammograms, fecal occult blood testing, and influenza vaccinations) in the first 2 years of EMR implementation. RESULTS: After adjustment, combined preventive services for the EMR group increased by 0.7%, a smaller increase than that seen in the non-EMR group (P = .55, 95% confidence interval -2.8 to 3.9). CONCLUSION: When compared with paper records, EMR implementation had no significant effect on the provision of the 4 preventive services studied.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".