Evaluation of the My Diabetes Care Patient Portal Intervention: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: My Diabetes Care (MDC) is a multi-faceted intervention embedded within an established patient portal, My Health at Vanderbilt. MDC is designed to help patients better understand their diabetes health data and support self-care. MDC uses infographics to visualize and summarize patients' diabetes health data, incorporates motivational strategies, provides literacy-level appropriate educational resources, and links to a diabetes online patient support community and diabetes news feeds. OBJECTIVE: This study aims to evaluate the effects of MDC on patient activation in adult patients with type 2 diabetes mellitus. Moreover, we plan to assess secondary outcomes, including system use and usability, and the effects of MDC on cognitive and behavioral outcomes (eg, self-care and self-efficacy). METHODS: We are conducting a 6-month, 2-arm, parallel-design, pragmatic pilot randomized controlled trial of the effect of MDC on patient activation. Adult patients with type 2 diabetes mellitus are recruited from primary care clinics affiliated with Vanderbilt University Medical Center. Participants are eligible for the study if they are currently being treated with at least one diabetes medication, are able to speak and read in English, are 21 years or older, and have an existing My Health at Vanderbilt account and reliable access to a desktop or laptop computer with internet access. We exclude patients living in long-term care facilities, patients with known cognitive deficits or severe visual impairment, and patients currently participating in any other diabetes-related research study. Participants are randomly assigned to MDC or usual care. We collect self-reported survey data, including the Patient Activation Measure (R) at baseline, 3 months, and 6 months. We will use mixed-effects regression models to estimate potentially time-varying intervention effects while adjusting for the baseline measure of the outcome. The mixed-effects model will use fixed effects for patient-level characteristics and random effects for health care provider variables (eg, primary care physicians). RESULTS: This study is ongoing. Recruitment was closed in May 2020; 270 patients were randomized. Of those randomized, most (214/267, 80.1%) were non-Hispanic White, and 13.1% (35/267) were non-Hispanic Black, 43.7% (118/270) reported being 65 years or older, and 33.6% (90/268) reported limited health literacy. We obtained at least 95.6% (258/270) completion among participants through the 3-month follow-up assessment. CONCLUSIONS: This randomized controlled trial will be one of the first to evaluate a patient-facing diabetes digital health intervention delivered via a patient portal. By embedding MDC into Epic's MyChart platform with more than 127 million patient records, our intervention is directly integrated into routine care, highly scalable, and sustainable. Our findings and evolving patient portal functionality will inform the continued development of MDC to best meet users' needs and a larger trial focused on the impact of MDC on clinical end points. TRIAL REGISTRATION: ClinicalTrials.gov NCT03947333; https://clinicaltrials.gov/ct2/show/NCT03947333. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25955.
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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 | 0.038 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.012 |
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".