Implementation of an intervention aimed at deprescribing benzodiazepines in a large US healthcare system using patient education materials: a pre/post-observational study with a control group
Bibliographic record
Abstract
OBJECTIVES: Long-term benzodiazepine use is common despite known risks. In the original Eliminating Medications Through Patient Ownership of End Results (EMPOWER) Study set in Canada, patient education led to increased rates of benzodiazepine cessation. We aimed to determine the effectiveness of implementing an adapted EMPOWER quality improvement (QI) initiative in a US-based healthcare system. DESIGN: We used a pre-post design with a non-randomised control group. SETTING: A network of primary care clinics. PARTICIPANTS: Patients with ≥60 days' supply of benzodiazepines in 6 months and ≥1 risk factor (≥65 years of age, a concurrent high-risk medication prescribed or a diazepam equivalent daily dose ≥10) were eligible. INTERVENTION: In March 2022, we engaged 22 primary care physicians (PCPs), and 308 of their patients were mailed an educational brochure, physician letter and flyer detailing benzodiazepine risks; the control group included 4 PCPs and 291 of their patients. PRIMARY AND SECONDARY MEASURES: The primary measure was benzodiazepine cessation by 9 months. We used logistic regression and a generalised estimating equations approach to control for clustering by PCP, adjusting for demographics, frailty, number of risk factors, and diagnoses of arthritis, depression, diabetes, falls, and pain. RESULTS: Patients in the intervention and control groups were comparable across most covariates; however, a greater proportion of intervention patients had pain-related diagnoses and depression. By 9 months, 26% of intervention patients (81 of 308) had discontinued benzodiazepines, compared with 17% (49 of 291) of control patients. Intervention patients had 1.73 greater odds of benzodiazepine discontinuation compared with controls (95% CI: 1.09, 2.75, p=0.02). The unadjusted number needed to treat was 10.5 (95% CI: 6.30, 34.92) and the absolute risk reduction was 0.095 (95% CI: 0.03 to 0.16). CONCLUSIONS: Results from this non-randomised QI initiative indicate that patient education programmes using the EMPOWER brochures have the potential to promote cessation of benzodiazepines in primary care.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".