Effects of an academic detailing service on benzodiazepine prescribing patterns in primary care
Bibliographic record
Abstract
BACKGROUND: Benzodiazepines are commonly used to treat anxiety and/or insomnia but are associated with substantial safety risks. Changes to prescribing patterns in primary care may be facilitated through tailored quality improvement strategies. Academic detailing (AD) may be an effective method of promoting safe benzodiazepine prescribing. The objective of this study was to evaluate the effectiveness of AD on benzodiazepine prescribing among family physicians. METHODS AND FINDINGS: We used an interrupted time series matched cohort design using population-based administrative claims databases. Participants were family physicians practicing in Ontario, Canada. The intervention was a voluntary AD service which involves brief service-oriented educational outreach visits by a trained pharmacist. The focus was on key messages for safer benzodiazepine prescribing in primary care with an emphasis on judicious prescribing to older adults aged 65 and older. Physicians in the intervention group were those who received at least one AD visit on benzodiazepine use between June 2019 and February 2020. Physicians in the control group were included if they did not receive an AD visit during the study period. Intervention physicians were matched to control physicians 1:4, on a variety of characteristics. Physicians were excluded if they had inactive billing or billing of less than 100 unique patient visits in the calendar year prior to the index date. The primary outcome was mean total benzodiazepine prescriptions at the level of the physician. Secondary outcomes were rate (per 100) of patients with long-term prescriptions, high-risk prescriptions, newly started prescriptions, and benzodiazepine-related patient harms. Data were analyzed using a repeated measures pre-post comparison with an intention-to-treat. Analyses were then stratified to focus on effects within higher-prescribing physicians. There were 1337 physicians were included in the study; 237 who received AD and 1064 who did not. There was no significant change in benzodiazepine prescribing when considering all physicians in the intervention and matched control groups. Although not significant, a greater reduction in total benzodiazepine prescriptions was observed amongst the highest-volume prescribing physicians who received the intervention (% change in slope = -0.53, 95%CI = -2.34 to 1.30, p > .05). The main limitation of our study was the voluntary nature of the AD intervention, which may have introduced a self-selection bias of physicians most open to changing their prescribing. CONCLUSION: This study suggests that future AD interventions should focus on physicians with the greatest room for improvement to their prescribing.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".