Addressing Barriers to Reducing Prescribing and Implementing Deprescribing of Sedative-Hypnotics in Primary Care
Bibliographic record
Abstract
OBJECTIVE: To describe interventions that target patient, provider, and system barriers to sedative-hypnotic (SH) deprescribing in the community and suggest strategies for healthcare teams. DATA SOURCES: Ovid MEDLINE ALL and EMBASE Classic + EMBASE (March 10, 2021). STUDY SELECTION AND DATA EXTRACTION: English-language studies in primary care settings. DATA SYNTHESIS: 20 studies were themed as patient-related and prescriber inertia, physician skills and awareness, and health system constraints. Patient education strategies reduced SH dose for 10% to 62% of participants, leading to discontinuation in 13% to 80% of participants. Policy interventions reduced targeted medication use by 10% to 50%. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: Patient engagement and empowerment successfully convince patients to deprescribe chronic SHs. Quality improvement strategies should also consider interventions directed at prescribers, including education and training, drug utilization reviews, or computer alerts indicating a potentially inappropriate prescription by medication, age, dose, or disease. Educational interventions were effective when they facilitated patient engagement and provided information on the harms and limited evidence supporting chronic use as well as the effectiveness of alternatives. Decision support tools were less effective than prescriber education with patient engagement, although they can be readily incorporated in the workflow through prescribing software. CONCLUSIONS: Several strategies with demonstrated efficacy in reducing SH use in community practice were identified. Education regarding SH risks, how to taper, and potential alternatives are essential details to provide to clinicians, patients, and families. The strategies presented can guide community healthcare teams toward reducing the community burden of SH use.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".