Implementation of an audit and feedback module targeting low-value clinical practices in a provincial trauma quality assurance program: a cost-effectiveness study
Bibliographic record
Abstract
BACKGROUND: Audit and Feedback (A&F) interventions based on quality indicators have been shown to lead to significant improvements in compliance with evidence-based care including de-adoption of low-value practices (LVPs). Our primary aim was to evaluate the cost-effectiveness of adding a hypothetical A&F module targeting LVPs for trauma admissions to an existing quality assurance intervention targeting high-value care and risk-adjusted outcomes. A secondary aim was to assess how certain A&F characteristics might influence its cost-effectiveness. METHODS: We conducted a cost-effectiveness analysis using a probabilistic static decision analytic model in the Québec trauma care continuum. We considered the Québec Ministry of Health perspective. Our economic evaluation compared a hypothetical scenario in which the A&F module targeting LVPs is implemented in a Canadian provincial trauma quality assurance program to a status quo scenario in which the A&F module is not implemented. In scenarios analyses we assessed the impact of A&F characteristics on its cost-effectiveness. Results are presented in terms of incremental costs per LVP avoided. RESULTS: Results suggest that the implementation of A&F module (Cost = $1,480,850; Number of LVPs = 6,005) is associated with higher costs and higher effectiveness compared to status quo (Cost = $1,124,661; Number of LVPs = 8,228). The A&F module would cost $160 per LVP avoided compared to status quo. The A&F module becomes more cost-effective with the addition of facilitation visits; more frequent evaluation; and when only high-volume trauma centers are considered. CONCLUSION: A&F module targeting LVPs is associated with higher costs and higher effectiveness than status quo and has the potential to be cost-effective if the decision-makers' willingness-to-pay is at least $160 per LVP avoided. This likely represents an underestimate of true ICER due to underestimated costs or missed opportunity costs. Results suggest that virtual facilitation visits, frequent evaluation, and implementing the module in high-volume centers can improve cost-effectiveness.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".