Feedback and training tool to improve provision of preventive careby physicians using EMRs: a randomised control trial
Bibliographic record
Abstract
BACKGROUND: Electronic medical records (EMRs) have the potential to improve the provision of preventive care by allowing general practitioners (GPs) to track and recall eligible patients and record testing for feedback on their service provision. OBJECTIVE: This study evaluates the effect of an educational intervention and feedback tool designed to teach GPs how to use their EMRs to improve their provision of preventive care. METHODS: A randomised controlled trial comparing rates of mammography, Papanicolaou tests, faecal occult blood tests and albumin creatinine ratios one-year pre- and post-intervention was conducted. Nine primary care practices (PCPs) representing over 30 000 patients were paired by practice size and experience of GPs, and randomly allocated to intervention or control groups. Physicians at the four intervention practices received a two-hour feedback session on their current level of preventive care and training to generate eligible patient lists for preventive services from their EMR database. RESULTS: One-year post-intervention results provided no evidence of a difference. The intervention was not a significant predictor of the one-year postintervention test rates for any of the four tests. On average, the intervention practices increased postintervention test rates on all tests by 16.8%, and control practices increased by 22.3%. CONCLUSION: The non-significant results may be due to a variety of reasons, including the level of intensity of the educational intervention, the cointervention of a government programme which provided incentives to GPs meeting specific targets for preventive care testing or the level of recording of tests performed in the EMR.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".