Audit feedback interventions to address high-risk prescriptions in long-term care homes: a costing study and return on investment analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it