Reducing prescribing of benzodiazepines in older adults: a comparison of four physician-focused interventions by a medical regulatory authority
Bibliographic record
Abstract
BACKGROUND: The inappropriate and/or high prescribing of benzodiazepine and 'Z' drugs (BDZ +) is a major health concern. The purpose of this study was to determine whether physician or pharmacist led interventions or a simple letter or a personalized prescribing report from a medical regulatory authority (MRA) was the most effective intervention for reducing BDZ + prescribing by physicians to patients 65 years of age or older. METHODS: quarter of 2016. All physician-participants were sent a personalized prescribing profile by the MRA. They were then randomized into four groups that received either nothing more, an additional personal warning letter from the MRA, a personal phone call from an MRA pharmacist or a personal phone call from an MRA physician. The main outcomes were prescribing behavior change of physicians at one year in terms of: change in mean number of older patients receiving 4 + DDD BDZ + and mean dose BDZ + prescribed per physician. To adjust for multiple statistical testing, we used MANCOVA to test both main outcome measures simultaneously by group whilst controlling for any baseline differences. RESULTS: All groups experienced a significant fall in the total number of older patients receiving 4 + DDD of BDZ + by about 50% (range 43-54%) per physician at one year, and a fall in the mean dose of BDZ + prescribed of about 13% (range 10-16%). However, there was no significant difference between each group. CONCLUSIONS: A personalized prescribing report alone sent from the MRA appears to be an effective intervention for reducing very high levels of BDZ + prescribing in older patients. Additional interventions by a pharmacist or physician did not result in additional benefit. The intervention needs to be tested further on a more general population of physicians, prescribing less extreme doses of BDZ + and that looks at more clinical and healthcare utilization outcomes.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".