Audit feedback interventions to address high-risk prescriptions in long-term care homes: a costing study and return on investment analysis
Bibliographic record
Abstract
BACKGROUND: Audit and feedback is a common implementation strategy, but few studies describe its costs. 'MyPractice' is a province-wide audit and feedback initiative to improve prescribing in nursing homes. This study sought to estimate the costs of 'MyPractice' and assess whether the financial benefit of 'MyPractice' offsets those costs. METHODS: We conducted a costing study from the perspective of the Ontario government. Total cost of 'MyPractice' was calculated as the sum of the costs of producing and disseminating the reports (covering three report releases) which were obtained from Ontario Health staff interviews and document reviews. Return on investment (ROI) was calculated as the ratio of net cost-savings and the intervention cost. Cost savings were based on the effectiveness of 'MyPractice' derived from a published cohort study. Cost-savings attributable to 'MyPractice' were estimated from the changes in the rates of antipsychotics over time between physicians who signed up and viewed the reports and those who did not sign up to the reports. RESULTS: Total intervention costs were C$223,691 (C$838 per physician and C$74,564 per release). Costs incurred during the development phase accounted for 74% of the total cost (C$166,117), while implementation costs for three report releases were responsible for 26% of the total costs (C$57,575). The ROI for every C$1 spent on the 'MyPractice' intervention was 1.02 (95% CI 0.51, 1.93) for three report releases. CONCLUSION: 'MyPractice' report offers a good return on investment and the value for money could improve with greater number of report releases.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".