Implementation and impact of Choosing Wisely recommendations in oncology: A scoping review.
Bibliographic record
Abstract
21 Background: The Choosing Wisely (CW) campaign launched in 2012 includes oncology-related recommendations (CWR) aimed at promoting evidence-based care and de-implementing low value practices; however, it remains unclear to what extent practice has changed as a result of the campaign. We undertook a scoping review to evaluate the extent of CWR uptake in oncology, and barriers and facilitators to implementation. Methods: A systematic search of MEDLINE, EMBASE, Cochrane, Emcare Nursing and Scopus was conducted for articles published between 2012 and March 6, 2020 pertaining to cancer-specific CWR. Articles were excluded if they did not report on cancer-specific recommendations, were commentaries/opinions, reported on screening or prevention, focused on pediatric populations, or were not in English. Retained articles were thematically grouped based on study objective. Implementation strategies, barriers and facilitators were summarized for articles implementing a recommendation into practice. Results: The search yielded 8565 articles; 98 articles were retained, addressing 32 unique recommendations. Use of active surveillance for low-risk prostate cancer (14/98) and reduction of imaging in early breast cancer (13/98) were the most commonly evaluated CWR. Of the 29 articles reporting on pre-post CW campaign adherence, 20 reported improved compliance without further intervention. In articles evaluating factors associated with CWR-concordance (62/98), age (23/62), stage or risk (22/62), and geographic location (21/62) most commonly influenced care decisions. Few articles described the development of interventions to improve concordance (1/98) or a protocol to evaluate implementation (2/98) with no further testing of their design. Of the 10/98 articles that evaluated implementation of a CWR, all reported improved compliance (range: 3-73% improvement). Implementation strategies were used in combination and most often included provider education (8/10), stakeholder engagement (6/10), and forced function (4/10). Preconceived views and reluctance to adopt new practices were the most commonly reported barriers to implementation; the use of technology to update practice and the use of education and evidence to build provider buy-in were the most common facilitators. Conclusions: In the eight years since the initial publication of oncology-specific CW recommendations, uptake has been limited. Publication of the recommendations has resulted in some passive improvements in care. Further adoption of CWR likely requires an implementation strategy that includes building stakeholder buy-in, and utilization of updated order sets and forced functions in technology to facilitate change. Future studies should assess the impact of the COVID-19 pandemic on the adoption of CWR and the de-implementation of 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 | 0.054 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".