Learnings in Digital Health Design: Insights From a Pilot Web App for Structured Note-Taking for Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
BACKGROUND: Patients fail to accurately remember 40% to 80% of medical information relayed during doctor appointments, and most standard after-visit summaries fail to effectively help patients comply with behaviors to manage their health conditions. The value of technology to empower and engage patients in their health management has been shown, and here we apply technology to help patients remember and act upon information communicated during their medical appointments. OBJECTIVE: We describe the development of WellNote, a digital notebook designed for patients to create a customized plan to manage their condition, plan for their appointments, track important actions (eg, medications and labs), and receive reminders for appointments and labs. METHODS: For this pilot, we chose to focus on rheumatoid arthritis, a chronic condition that relies on many of these features. The development of WellNote followed a structured method based on design thinking and co-design principles, with the app built in close collaboration with patients and a physician partner to ensure clinical relevance. Our design process consisted of 3 rounds: patient and physician interviews, visual prototypes, and a functional pilot app. RESULTS: Over the course of the design process, WellNote's features were refined, with the final version being a digital notebook designed for patients with rheumatoid arthritis to manage their health by helping them track medications and labs and plan for appointments. It features several pages, like a dashboard, patient profile, appointment notes, preplanning, medication management, lab tracking, appointment archives, reminders, and a pillbox for medication visualization. CONCLUSIONS: WellNote's active and structured note-taking features allow patients to clearly document the information from their physician without detracting from the conversation, helping the patient to become more empowered and engaged in their health management. The co-design process empowered these stakeholders to share their needs and participate in the development of a solution that truly solves pain points for these groups. This viewpoint highlights the role of digital health tools and the co-design of new health care innovations to empower patients and support clinicians.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".