Economic evaluations of audit and feedback interventions: a systematic review
Bibliographic record
Abstract
BACKGROUND: The effectiveness of audit and feedback (A&F) interventions to improve compliance to healthcare guidelines is supported by randomised controlled trials (RCTs) and meta-analyses of RCTs. However, there is currently a knowledge gap on their cost-effectiveness. OBJECTIVE: We aimed to assess whether A&F interventions targeting improvements in compliance to recommended care are economically favourable. METHODS: We conducted a systematic review including experimental, observational and simulation-based economic evaluation studies of A&F interventions targeting healthcare providers. Comparators were a 'do nothing' strategy, or any other intervention not involving A&F or involving a subset of A&F intervention components. We searched MEDLINE, CINAHL, CENTRAL, Econlit, EMBASE, Health Technology Assessment Database, MEDLINE, NHS Economic Evaluation Database, ABI/INFORM, Web of Science, ProQuest and websites of healthcare quality associations to December 2021. Outcomes were incremental cost-effectiveness ratios, incremental cost-utility ratios, incremental net benefit and incremental cost-benefit ratios. Pairs of reviewers independently selected eligible studies and extracted relevant data. Reporting quality was evaluated using CHEERS (Consolidated Health Economic Evaluation Reporting Standards). Results were synthesised using permutation matrices for all studies and predefined subgroups. RESULTS: Of 13 221 unique citations, 35 studies met our inclusion criteria. The A&F intervention was dominant (ie, at least as effective with lower cost) in 7 studies, potentially cost-effective in 26 and was dominated (ie, the same or less effectiveness and higher costs) in 2 studies. A&F interventions were more likely to be economically favourable in studies based on health outcomes rather than compliance to recommended practice, considering medical costs in addition to intervention costs, published since 2010, and with high reporting quality. DISCUSSION: Results suggest that A&F interventions may have a high potential to be cost-effective. However, as is common in systematic reviews of economic evaluations, publication bias could have led to an overestimation of their economic value.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.105 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".