Barriers and Facilitators to the Implementation and Adoption of a Continuous Quality Improvement Program in Surgery: A Case Study
Bibliographic record
Abstract
INTRODUCTION: As postoperative adverse events (AEs) drive worsened patient experience, longer length of stay, and increased costs of care, surgeons have long sought to engage in innovative approaches aimed at reducing AEs to improve the quality and safety of surgical care. While data-driven AE performance measurement and feedback (PMF) as a form of continuing professional development (CPD) has been presented as a possible approach to continuous quality improvement (CQI), little is known about the barriers and facilitators that influence surgeons' engagement and uptake of these CPD programs. The purpose of this knowledge translation informed CPD study was to examine surgeons' perspectives of the challenges and facilitators to participating in surgical CQI with the broader objective of enhancing future improvements of such CPD interventions. METHODS: Using Everett Rogers diffusion of innovations framework as a sampling frame, the participants were recruited across five surgical divisions. An exploratory case study approach, including in-depth semistructured interviews, was employed. Interview transcripts were analyzed and directly coded using the Theoretical Domains Framework. RESULTS: Directed coding yielded a total of 527 coded barriers and facilitators to behavior change pertaining to the implementation and adoption of PMF with the majority of barriers and facilitators coded in four key theoretical domains environmental context and resources, social influences, knowledge, and beliefs about consequences. A key barrier was the lack of support from the hospital necessitating surgeons' self-funding their own PMF programs. Facilitators included having a champion to drive CQI and using seminars to facilitate discussions around CQI principles and practices. DISCUSSION: This study identified multiple barriers and facilitators to surgeons' engagement and uptake of a data-driven PMF system in surgery. A key finding of the study was the identification of the influential role of positive deviance seminars as a quality improvement and patient safety mechanism that encourages surgeon engagement in PMF systems.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".