Mapping Implementation Strategies to Address Barriers to Commission on Cancer Accreditation Standards in Breast Surgery
Bibliographic record
Abstract
BACKGROUND: The Commission on Cancer (CoC) introduced synoptic operative reports (SOR) as accreditation standards to increase adherence to cancer surgical standards. Due to large variations in implementation of past CoC accreditation standards, we used a theory-informed method to identify optimal implementation strategies for SOR integration. STUDY DESIGN: Using the Consolidated Framework for Implementation Research (CFIR), we conducted semi-structured interviews from December 2021-May 2022 focused on implementing the breast SOR with 31 stakeholders sampled from 4 CoC sites. Implementation barriers were mapped to theory-informed strategies using the validated CFIR-Expert Recommendations for Implementation Change (ERIC) matching tool. Using the "name it, define it, specify it" method for describing implementation strategies, actions, actors, and action targets were specified for each correlating ERIC cluster and strategy. RESULTS: Participants included 10 surgeons, 4 cancer liaison physicians, 11 cancer program administrators, and 6 IT engineers. Strategies addressing the most common barriers were 1) determining readiness to implement the SOR; 2) identifying champions to promote the SOR; and 3) having stakeholder discussions to highlight the importance of templated documentation and determine whether the SOR will adequately address this. The common themes across the top strategies were workflow changes, developing an actionable plan, engaging champions, and leveraging champion relationships with other surgeons. Training and educating stakeholders was not a key recommended strategy in our study. CONCLUSION: Our study demonstrated that assessment of workflow changes along with local champions and their relationship with surgeons were the most important strategies for successful SOR implementation. Programs may benefit from utilizing these strategies for future SOR implementation and CoC initiatives.
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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 | 0.082 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".