A Two-Way Interactive Text Messaging Application for Low-Income Patients with Chronic Medical Conditions: Design-Thinking Development Approach
Bibliographic record
Abstract
BACKGROUND: Two-way interactive text messaging between patient and community health workers (CHWs) through mobile phone SMS (short message service) text messaging is a form of digital health that can potentially enhance patient engagement in young adults and families that have a child with chronic medical conditions such as diabetes mellitus, sickle cell disease, and asthma. These patients have complex needs, and a user-centered way can be useful for designing a tool to address their needs. OBJECTIVE: The aim of this study was to utilize the user-centered approach of design thinking to develop a two-way interactive communication SMS text messaging tool for communication between patients or caregivers and CHWs. METHODS: We applied a design thinking methodology for development of the SMS text messaging tool. We collected qualitative data from 127 patients/caregivers and 13 CHWs, health care professionals, and experts. In total, 4 iterative phases were used to design the final prototype. RESULTS: The design thinking process led to the final SMS text messaging tool that was transformed from a one-dimensional, template-driven prototype (phases 1 and 2) into a dynamic, interactive, and individually tailored tool (phases 3 and 4). The individualized components consider social factors that influence patients' ability to engage such as transportation issues and appointment reminders. SMS text messaging components also include operational factors to support staff such as patient contact lists, SMS text messaging templates, and technology chat support. CONCLUSIONS: Design thinking can develop a tool to meet the engagement needs of patients with complex health care needs and be user-friendly for health care staff.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".