Paving the Way for Electronic Patient-Centered Measurement in Team-Based Primary Care: Integrated Knowledge Translation Approach (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Patient-centered measurement (PCM) aims to improve the overall quality of care through the collection and sharing of patient values, outcomes, and perspectives. However, the use of PCM in care team decisions remains limited. Integrated knowledge translation (IKT) offers a collaborative, adaptive approach to explore best practices for incorporating PCM into primary care practices by involving knowledge users, including patients and providers, in the exploratory process. </sec> <sec> <title>OBJECTIVE</title> This study aims to test the feasibility of using patient-generated data in team-based care; describe the use of these data for team-based mental health care; and summarize patient and provider care experiences with PCM. </sec> <sec> <title>METHODS</title> We conducted a multi-method exploratory study in a rural team-based primary care clinic using IKT to co-design, implement, and evaluate the use of PCM in team-based mental health care. Care pathways, workflows, and quality improvement activities were adjusted iteratively to improve integration efforts. Patient and provider experiences were evaluated using individual interviews relating to the use of PCM and patient portals in practice. All meeting notes, interview summaries, and emails were analyzed to create a narrative evaluation. </sec> <sec> <title>RESULTS</title> During co-design, a care workflow was developed to incorporate electronically collected patient-generated data from the patient portal into the electronic medical record, and customized educational tools and resources were added. During implementation, care pathways and patient workflows for PCM were developed. Patients found portal use easy, educational, and validating, but data entries were not used during care visits. Providers saw the portal as extra work, and the lack of portal and electronic medical record integration was a major barrier. The IKT approach was invaluable for addressing workflow changes and understanding the ongoing barriers to PCM use and quality improvement. </sec> <sec> <title>CONCLUSIONS</title> Although the culture toward using PCM is changing, the use of PCM during care has not been successful. Patients felt validated and supported through portal use and could be empowered to bring these data to their visits. Training, modeling, and adaptable PCM methods are required before PCM can be integrated into routine care. </sec>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".