Implementing quality improvement into practice: Exploring the work to develop and implement a QI project in primary care
Bibliographic record
Abstract
Context: The College of Physicians and Surgeons of Alberta launched the Physician Practice Improvement Program in 2021 requiring physicians to incorporate one personal development and two quality improvement (QI) activities into their practice over a continuous five-year cycle. Physicians receive little QI training in their formal education and require professional development opportunities to learn necessary QI training and skills. This presentation explores the work to implement a practice-driven QI project, what resources and education was needed, and what constraints exist for primary care physicians to conduct QI. Objective: The purpose of this study was to explore what is required to conduct QI work, the supports and resources necessary, and perceptions of QI prioritization to improve healthcare. Study Design and Analysis: Interviews and focus groups were conducted with the physicians who co-led the project, resident physicians and staff who participated in QI training for the project, resident physicians who assisted in project implementation and reporting, and content experts in QI in healthcare. Questions were developed using the normalization process theory (NPT) to explore the social organization of the work and necessary activities in development and implementation of the project, and the interactive process framework (IPF) to explore the non-linear and dynamic nature of the real-world implementation. Transcripts were coded deductively using the NPT and IPF constructs, with additional codes developed inductively using reflexive thematic analysis. Analysis meetings with co-investigators resolved any disagreements in coding for each transcript and developed code consensus. Results: Key themes were needing additional QI training and project support, reliance on resident physicians and clinic staff to assist in implementation, and the constraints on physicians to complete QI work. Conclusions: This project highlights work and time required to conduct quality QI projects in practice, the supports and structures necessary for this work, and the variance in QI support and structure across health systems in Alberta. In response to training and support needs, project co-leads developed a more comprehensive QI course with mentorship and support for ongoing QI project development and implementation. The challenges and constraints faced by the motivated physicians of this project to implement QI in practice provide important context for QI work.
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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 | 0.138 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".