A Patient-Facing Diabetes Dashboard Embedded in a Patient Web Portal: Design Sprint and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Health apps and Web-based interventions designed for patients with diabetes offer novel and scalable approaches to engage patients and improve outcomes. However, careful attention to the design and usability of these apps and Web-based interventions is essential to reduce the barriers to engagement and maximize use. OBJECTIVE: The aim of this study was to apply design sprint methodology paired with mixed-methods, task-based usability testing to design and evaluate an innovative, patient-facing diabetes dashboard embedded in an existing patient portal and integrated into an electronic health record. METHODS: We applied a 5-day design sprint methodology developed by Google Ventures (Alphabet Inc, Mountain View, CA) to create our initial dashboard prototype. We identified recommended strategies from the literature for using patient-facing technologies to enhance patient activation and designed a dashboard functionality to match each strategy. We then conducted a mixed-methods, task-based usability assessment of dashboard prototypes with individual patients. Measures included validated metrics of task performance on 5 common and standardized tasks, semistructured interviews, and a validated usability satisfaction questionnaire. After each round of usability testing, we revised the dashboard prototype in response to usability findings before the next round of testing until the majority of participants successfully completed tasks, expressed high satisfaction, and identified no new usability concerns (ie, stop criterion was met). RESULTS: ). The dashboard used graphics to visualize and summarize health data and reinforce understanding, incorporated motivational strategies (eg, social comparisons and gamification), and provided educational resources and secure-messaging capability. More than 80% of participants were able to successfully complete all 5 tasks using the final prototype. Interviews revealed usability concerns with design, the efficiency of use, and content and terminology, which led to improvements. Overall satisfaction (0=worst and 7=best) improved from the initial to the final prototype (mean 5.8, SD 0.4 vs mean 6.7, SD 0.5). CONCLUSIONS: Our results demonstrate the utility of the design sprint methodology paired with mixed-methods, task-based usability testing to efficiently and effectively design a patient-facing, Web-based diabetes dashboard that is satisfying for patients to use.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".