Effectiveness of different de-implementation strategies in primary care: systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To evaluate the effectiveness of various de-implementation interventions in primary care, targeting care (treatments or tests) that provides no or limited value for patients (low value care). Design: Systematic review and meta-analysis. Data sources: Medline and Scopus databases, from inception to 10 July 2024. Eligibility criteria for selecting studies: Randomised trials comparing de-implementation interventions with placebo or sham intervention, no intervention, or other de-implementation intervention strategies in primary care. Eligible trials provided information on the use of low value care, total volume of care, appropriate care, and health outcomes. Data extraction and synthesis: Titles, abstracts, and full texts were screened, data were extracted, and risk of bias was assessed independently and in duplicate. Random effects meta-analyses were conducted, and the certainty of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Results: 13 008 abstracts were screened and 140 were eligible for inclusion in the study. Median follow-up was 287 days (interquartile range 180-365). In 75 (54%) trials the aim was to reduce the use of antibiotics, in 42 (30%) to reduce other drug treatments, in 17 (12%) to reduce imaging, and in 15 (11%) to reduce laboratory testing. The certainty of the evidence was moderate that provider education combined with audit and feedback reduced the use of targeted low value care (odds ratio 0.73, 95% confidence interval (95% CI) 0.63 to 0.84). Provider education (0.86, 95% CI 0.72 to 1.03), audit and feedback (0.82, 0.67 to 1.00), and patient education (0.70, 0.30 to 1.66), and a combination of these strategies (point estimates for odds ratios ranging from 0.57 to 0.64) may reduce the use of targeted low value care (low certainty of evidence for all). Conclusions: The results suggested with moderate certainty of evidence that provider education combined with audit and feedback reduced the use of targeted low value care. Individual strategies may slightly reduce the use of targeted low value care, but achieving a meaningful impact on low value care may require the use of multiple strategies. The results may be useful for patients, clinicians, policy makers, and guideline developers when deciding on future de-implementation strategies and research priorities. Systematic review registration: PROSPERO CRD42023411768.
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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 | 0.041 | 0.081 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.061 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".