Development of MyREADY Transition BBD Mobile App, a Health Intervention Technology Platform, to Improve Care Transition for Youth With Brain-Based Disabilities: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Transition from pediatric to adult health care varies and is resource intensive. Patient-centered health information technology (HIT) interventions are increasingly being developed in partnership with patients. OBJECTIVE: This study aims to develop an internet-based mobile app intervention for patients with brain-based disabilities to improve transition in care readiness. METHODS: The app was designed for patients aged 15 to 17 years with brain-based disabilities having the ability to use a mobile app. A multidisciplinary team, an industry partner, and a patient and family advisory council was assembled. We hypothesized that existing tools could be migrated into the app to address education, empowerment, and navigation. We used cognitive learning theory to support chapters targeting transition in care skill sets. We used the agile iterative methodology to engage stakeholders. RESULTS: We developed a novel MyREADY Transition HIT platform. An electronic mentor supported cognitive learning with messaging, quizzes, rewards, and videos. We used gaming to guide navigation through a fictitious health care city. Adapting existing tools was achieved by the patient and family advisory council requesting personalization. Our iterative design required time-consuming back-end technology management. Developing the platform took 24 months instead of our grant-approved 12 months, impacting the onset of the planned trial within the allotted budget. CONCLUSIONS: A novel patient-centered HIT platform to improve health care transition was successfully developed in partnership with patients and industry. Careful resource management was needed to achieve timely delivery of the end product, flagging the cautious planning required to deliver HIT tools in time for the much-needed trials informing their clinical application. TRIAL REGISTRATION: ClinicalTrials.gov NCT03852550; https://clinicaltrials.gov/study/NCT03852550.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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".