De-implementing wisely: developing the evidence base to reduce low-value care
Bibliographic record
Abstract
Choosing Wisely (CW) campaigns globally have focused attention on the need to reduce low-value care, which can represent up to 30% of the costs of healthcare. Despite early enthusiasm for the CW initiative, few large-scale changes in rates of low-value care have been reported since the launch of these campaigns. Recent commentaries suggest that the focus of the campaign should be on implementation of evidence-based strategies to effectively reduce low-value care. This paper describes the Choosing Wisely De-Implementation Framework (CWDIF), a novel framework that builds on previous work in the field of implementation science and proposes a comprehensive approach to systematically reduce low-value care in both hospital and community settings and advance the science of de-implementation. The CWDIF consists of five phases: Phase 0 , identification of potential areas of low-value healthcare; Phase 1 , identification of local priorities for implementation of CW recommendations; Phase 2 , identification of barriers to implementing CW recommendations and potential interventions to overcome these; Phase 3 , rigorous evaluations of CW implementation programmes; Phase 4 , spread of effective CW implementation programmes. We provide a worked example of applying the CWDIF to develop and evaluate an implementation programme to reduce unnecessary preoperative testing in healthy patients undergoing low-risk surgeries and to further develop the evidence base to reduce low-value care.
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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 | 0.574 | 0.657 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.029 | 0.013 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.014 | 0.026 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".