An Integrated Interprofessional Continuing Medical Education and Quality Improvement Initiative to Address Cardiovascular and Renal Risk in Patients with Type 2 Diabetes in Community-Based Primary Care Practices
Bibliographic record
Abstract
A Quality Improvement and Continuing Medical Education intervention (QICMEi) was developed to improve competencies and performance of interprofessional primary care providers (PCPs), as well as patient care outcomes for individuals with type 2 diabetes with, or at risk of cardiovascular or chronic kidney disease that could benefit from SGLT-2i/GLP-1RA treatment. The QICMEi was implemented at two community-based family health centres within an integrated delivery system. The intervention was based on an analysis of treatment patterns using electronic health records (EHR) and evaluation and outcomes were assessed on from surveys and interviews from learners and EHR data. Healthcare teams were recruited, pre-intervention treatment patterns were reviewed to establish quality of care goals, education intervention needs and to guide HCP team discussions and QI goals. Community centre site leaders directed the CME, led case-based team QI discussions, developed process improvements with QI coaches and patient education materials explaining treatments were developed to improve adherence. PCPs' knowledge, competence and performance in interpreting and applying best-practices in treatment selection increased, while perceived challenges (managing side effects of intensified therapy, identifying SGLT-2i/GLP-1RAs-eligible profiles) decreased post-intervention. EHR data across a large patient volume showed slight but non-clinically significant changes in SGLT-2i/GLP-1RAs prescription patterns. Significance was likely hindered due to high baseline prescribing levels and contextual challenges in the delivery system (e.g. insurance authorisations, medication costs). Results indicated enhanced communication with patients, and sustained utilisation of patient materials/EHR tools. The QICMEi advanced participant knowledge, improved performance and teamwork, enhanced the system of care, exposed barriers and facilitated the adoption of materials for patient education. It demonstrated the value of CME's role in improving care while identifying the complexities in addressing and sustaining community-level patient care goals. This underscores the need for further research into QICMEi in systems of care to ultimately change provider treatment patterns.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".