Effectiveness of confidential reports to physicians on their prescribing of antipsychotic medications in nursing homes
Bibliographic record
Abstract
Abstract Background Antipsychotic medication use in nursing homes is associated with potential for harms. In Ontario, Canada, an agency of the provincial government offers nursing home physicians quarterly audit and feedback on their antipsychotic prescribing. We compared the characteristics of physicians who did and did not engage with the intervention, and assessed early changes in prescribing. Methods This population-level, retrospective cohort study used linked administrative databases to track prescribing practices in nursing homes pre-intervention (baseline), immediately post-initiative (3 months), and at follow-up (6 months). Exposure variables identified whether a physician signed up to participate (or not) or viewed the feedback following sign up (or not). Differences in the proportion of days that residents received antipsychotic medications at 6 months compared to baseline by exposure(s) were assessed using a linear mixed effects regression analysis to adjust for a range of resident, physician, and nursing home factors. Benzodiazepine and statin prescribing were assessed as a balance and tracer measures, respectively. Results Of 944 eligible physicians, 210 (22.3%) signed up to recieve the feedback report and 132 (13.9%) viewed their feedback. Physicians who signed up for feedback were more likely to have graduated from a Canadian medical school, work in urban nursing homes, and care for a larger number of residents. The clinical and functional characteristics of residents were similar across physician exposure groups. At 6 months, antipsychotic prescribing had decreased in all exposure groups. Those who viewed their feedback report had a signicantly greater reduction in antipsychotic prescribing than those who did not sign up (0.94% patient-days exposed; 95% CI 0.35 to 1.54%, p = 0.002). Trends in prescribing patterns across exposure groups for benzodiazepines and statins were not statistically significant. Interpretation Almost a quarter of eligible physicians engaged early in a voluntary audit and feedback intervention related to antipsychotic prescribing in nursing homes. Those who viewed their feedback achieved a small but statistically significant change in prescribing, equivalent to approximately 14,000 fewer days that nursing home residents received antipsychotic medications over 6 months. This study adds to the literature regarding the role of audit and feedback interventions to improve quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.128 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".