Leveraging an Electronic Health Record Patient Portal to Help Patients Formulate Their Health Care Goals: Mixed Methods Evaluation of Pilot Interventions
Bibliographic record
Abstract
BACKGROUND: Persons with multiple chronic conditions face complex medical regimens and clinicians may not focus on what matters most to these patients who vary widely in their health priorities. Patient Priorities Care is a facilitator-led process designed to identify patients' priorities and align decision-making and care, but the need for a facilitator has limited its widespread adoption. OBJECTIVE: The aims of this study are to design and test mechanisms for patients to complete a self-directed process for identifying priorities and providing their priorities to clinicians. METHODS: The study involved patients of at least 65 years of age at 2 family medicine practices with 5 physicians each. We first tested 2 versions of an interactive website and asked patients to bring their results to their visit. We then tested an Epic previsit questionnaire derived from the website's questions and included standard previsit materials. We completed postintervention phone interviews and an online survey with participating patients and collected informal feedback and conducted a focus group with participating physicians. RESULTS: In the test of the first website version, 17.3% (35/202) of invited patients went to the website, 11.4% (23/202) completed all of the questions, 2.5% (5/202) brought results to their visits, and the median session time was 43.0 (IQR 28.0) minutes. Patients expressed confusion about bringing results to the visit. After clarifying that issue in the second version, 15.1% (32/212) of patients went to the website, 14.6% (31/212) completed the questions, 1.9% (4/212) brought results to the visit, and the median session time was 35.0 (IQR 35.0) minutes. In the test of the Epic questionnaire, 26.4% (198/750) of patients completed the questionnaire before at least 1 visit, and the median completion time was 14.0 (IQR 23.0) minutes. The 8 main questions were answered 62.9% (129/205) to 95.6% (196/205) of the time. Patients who completed questionnaires were younger than those who did not (72.3 vs 76.1 years) and were more likely to complete at least 1 of their other assigned questionnaires (99.5%, 197/198) than those who did not (10.3%, 57/552). A total of 140 of 198 (70.7%) patients responded to a survey, and 86 remembered completing the questionnaire; 78 (90.7%) did not remember having difficulty answering the questions and 57 (68.7%) agreed or somewhat agreed that it helped them and their clinicians to understand their priorities. Doctors noted that the sickest patients did not complete the questionnaire and that the discussion provided a good segue into end-of-life care. CONCLUSIONS: Embedding questionnaires assaying patient priorities into patient portals holds promise for expanding access to priorities-concordant care.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".