Outcomes of End-User Testing of a Care Coordination Mobile App With Families of Children With Special Health Care Needs: Simulation Study
Bibliographic record
Abstract
BACKGROUND: Care for children with special health care needs relies on a network of providers who work to address the medical, behavioral, developmental, educational, social, and economic needs of the child and their family. Family-directed, manually created visual depictions of care team composition (ie, care mapping) and detailed note-taking curated by caregivers (eg, care binders) have been shown to enhance care coordination for families of these children, but they are difficult to implement in clinical settings owing to a lack of integration with electronic health records and limited visibility of family-generated insights for care providers. Caremap is an electronic health record-integrated digital personal health record mobile app designed to integrate the benefits of care mapping and care binders. Currently, there is sparse literature describing end-user participation in the co-design of digital health tools. In this paper, we describe a project that evaluated the usability and proof of concept of the Caremap app through end-user simulation. OBJECTIVE: This study aimed to conduct proof-of-concept testing of the Caremap app to coordinate care for children with special health care needs and explore early end-user engagement in simulation testing. The specific aims included engaging end users in app co-design via app simulation, evaluating the usability of the app using validated measures, and exploring user perspectives on how to make further improvements to the app. METHODS: Caregivers of children with special health care needs were recruited to participate in a simulation exercise using Caremap to coordinate care for a simulated case of a child with complex medical and behavioral needs. Participants completed a postsimulation questionnaire adapted from 2 validated surveys: the Pediatric Integrated Care Survey (PICS) and the user version of the Mobile Application Rating Scale (uMARS). A key informant interview was also conducted with a liaison to Spanish-speaking families regarding app accessibility for non-English-speaking users. RESULTS: A Caremap simulation was successfully developed in partnership with families of children with special health care needs. Overall, 38 families recruited from 19 different US states participated in the simulation exercise and completed the survey. The average rating for the survey adapted from the PICS was 4.1 (SD 0.82) out of 5, and the average rating for the adapted uMARS survey was 4 (SD 0.83) out of 5. The highest-rated app feature was the ability to track progress toward short-term, patient- and family-defined care goals. CONCLUSIONS: Internet-based simulation successfully facilitated end-user engagement and feedback for a digital health care coordination app for families of children with special health care needs. The families who completed simulation with Caremap rated it highly across several domains related to care coordination. The simulation study results elucidated key areas for improvement that translated into actionable next steps in app development.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".