A Randomized Trial Assessing the Impact of a Personal Printed Feedback Portrait on Statin Prescribing in Primary Care
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation (KT) initiatives have the potential to improve prescribing quality and produce savings that exceed the cost of the KT program itself, including the cost of evaluation using pragmatic study methods. Our objective was to measure the impact and estimated savings resulting from the distribution of individualized physician portraits of statin prescribing along with therapeutic recommendations in British Columbia, Canada. METHODS: A paired community design was used to study 2 725 family physicians in British Columbia. Communities were paired according to number of physicians and geographic location, with one community of each pair randomly assigned to an early (n = 1 349) or delayed (n = 1 376) intervention group. The intervention was a personalized prescribing portrait on statins that included therapeutic recommendations. The primary outcome was the impact on new prescribing (defined as statin naive) for primary prevention (defined as no diagnosis of cardiovascular disease) as recorded in the administrative claims databases of the BC Ministry of Health. RESULTS: Compared to the delayed control group, the relative probability of a new statin prescription for primary prevention decreased by 6% in the 12 months after the Education for Quality Improvement in Patient Care (EQIP) portrait compared to the preceding 12 months (relative risk [RR] 0.94; 95% confidence interval [CI] 0.91-0.98). There was also a non-statistically significant decrease in new prescribing for secondary prevention in patients diagnosed with cardiovascular disease (RR 0.96; 95% CI 0.91-1.01). We estimated that 572 fewer patients started statins for primary prevention in the first year after the portrait was mailed compared to patients in the delayed practices. We estimated the statin cost for those patients as $465,000 in the first two years, while the KT program to provide statin portraits cost less than $100,000. DISCUSSION: The individualized prescribing portrait had a significant beneficial effect on new statin prescribing for primary prevention but not secondary prevention. Provincial drug plan costs appear to have been reduced to a level that exceeded the cost of the program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".