Identifying Contextual Factors and Strategies for Practice Facilitation in Primary Care Quality Improvement Using an Informatics-Driven Model: Framework Development and Mixed Methods Case Study
Bibliographic record
Abstract
BACKGROUND: The past decade has seen increasing opportunities and efforts to integrate quality improvement into health care. Practice facilitation is a proven strategy to support redesign and improvement in primary care practices that focuses on building organizational capacity for continuous improvement. Practice leadership, staff, and practice facilitators all play important roles in supporting quality improvement in primary care. However, little is known about their perspectives on the context, enablers, barriers, and strategies that impact quality improvement initiatives. OBJECTIVE: This study aimed to develop a framework to enable assessment of contextual factors, challenges, and strategies that impact practice facilitation, clinical measure performance, and the implementation of quality improvement interventions. We also illustrated the application of the framework using a real-world case study. METHODS: We developed the TITO (task, individual, technology, and organization) framework by conducting participatory stakeholder workshops and incorporating their perspectives to identify enablers and barriers to quality improvement and practice facilitation. We conducted a case study using a mixed methods approach to demonstrate the use of the framework and describe practice facilitation and factors that impact quality improvement in a primary care practice that participated in the Healthy Hearts in the Heartland study. RESULTS: The proposed framework was used to organize and analyze different stakeholders' perspectives and key factors based on framework domains. The case study showed that practice leaders, staff, and practice facilitators all influenced the success of the quality improvement program. However, these participants faced different challenges and used different strategies. The framework showed that barriers stemmed from patients' social determinants of health, a lack of staff and time, and unsystematic facilitation resources, while enablers included practice culture, staff buy-in, implementation of effective practice facilitation strategies, practice capacity for change, and shared complementary resources from similar, ongoing programs. CONCLUSIONS: Our framework provided a useful and generalizable structure to guide and support assessment of future practice facilitation projects, quality improvement initiatives, and health care intervention implementation studies. The practice leader, staff, and practice facilitator all saw value in the quality improvement program and practice facilitation. Practice facilitators are key liaisons to help the quality improvement program; they help all stakeholders work toward a shared target and leverage tailored strategies. Taking advantage of resources from competing, yet complementary, programs as additional support may accelerate the effective achievement of quality improvement goals. Practice facilitation-supported quality improvement programs may be opportunities to assist primary care practices in achieving improved quality of care through focused and targeted efforts. The case study demonstrated how our framework can support a better understanding of contextual factors for practice facilitation, which could enable well-prepared and more successful quality improvement programs for primary care practices. Combining implementation science and informatics thinking, our TITO framework may facilitate interdisciplinary research in both fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".