Implementation and Impact of Choosing Wisely Recommendations in Oncology
Bibliographic record
Abstract
The Choosing Wisely (CW) campaign, launched in 2012, includes oncology-specific recommendations to promote evidence-based care and deimplementation of low-value practices. However, it is unclear to what extent the campaign has prompted practice change. We systematically reviewed the literature to evaluate the uptake of cancer-specific CW recommendations focusing on the period before the declaration of the COVID-19 pandemic. We used Grimshaw's deimplementation framework to thematically group the findings and extracted information on implementation strategies, barriers, and facilitators from articles reporting on active implementation. In the 98 articles addressing 32 unique recommendations, most reported on passive changes in adherence pre-post publication of CW recommendations. Use of active surveillance for low-risk prostate cancer and reduction in staging imaging for early breast cancer were the most commonly evaluated recommendations. Most articles assessing passive changes in adherence pre-post CW publication reported improvement. All articles evaluating active implementation (10 of 98) reported improved compliance (range: 3%-73% improvement). Most common implementation strategies included provider education and/or stakeholder engagement. Preconceived views and reluctance to adopt new practices were common barriers; common facilitators included the use of technology and provider education to increase provider buy-in. Given the limited uptake of oncology-specific CW recommendations thus far, more attention toward supporting active implementation is needed. Effective adoption of CW likely requires a multipronged approach that includes building stakeholder buy-in through engagement and education, using technology-enabled forced functions to facilitate change along with policy and reimbursement models that disincentivize low-value care. Professional societies have a role to play in supporting this next phase of CW.
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.
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 | 0.079 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".