Using Human-Centered Design and Development to Create a Digital Sick Day Medication Guidance Application for People With Diabetes, Cardiovascular Disease, or Chronic Kidney Disease: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Diabetes, cardiovascular disease, and chronic kidney disease are associated with high morbidity and costs of care. Medications can reduce long-term complications but may contribute to complications such as hypoglycemia and acute kidney injury during acute illnesses. Sick day medication guidance (SDMG) could help prevent these adverse events, but evidence for effective strategies to deliver this guidance is lacking. OBJECTIVE: We iteratively designed and developed a digital prototype user interface (UI) to deliver SDMG for patient self-management. The application, called "Preventing medication complications during AcUte illness through Symptom Evaluation and sick day guidance" (PAUSE), delivers personalized knowledge and self-management guidance directly to patients to enhance medication self-management during acute illness, with the goal of reducing preventable emergency visits and hospitalizations and improving patient outcomes during acute illness. METHODS: Using a human-centered design (HCD) approach, we conducted iterative heuristic evaluation and usability testing paired with prototype revisions. Heuristic evaluation involved our team members evaluating the prototype's UI against established criteria. We also conducted formative usability testing with 6 patients (including a patient-caregiver dyad) to provide subjective lived experience perspectives. We analyzed data deductively and pragmatically to rapidly inform subsequent iterations. RESULTS: We identified 21 and 44 design issues through heuristics evaluation and usability testing, respectively. The development team iteratively revised the PAUSE UI prototype between evaluations, with the final design providing key user flows and integrated supports and reminders for acting on severe acute illness situations that recommend pausing certain medications. CONCLUSIONS: Using an iterative HCD approach, we designed and developed a digital health application to deliver SDMG for patient self-management. We addressed feasible technical and workflow barriers using iterative heuristic evaluations and usability testing resulting in a refined SDMG self-management prototype app for patients taking medications commonly used to treat diabetes, cardiovascular disease, and chronic kidney disease. Further research is needed to test the effectiveness of the current PAUSE app in helping people with these chronic conditions self-manage their medications during acute illness and evaluate the feasibility of integrating the app into community-based chronic disease 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 | 0.080 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".