A Novel Hospital-to-Home System for Children With Medical Complexities: Usability Testing Study
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) are a group of young people who have severe complex chronic conditions, substantial family-identified service needs, functional limitations, and high health care resource use. Technology-enabled hospital-to-home interventions designed to deliver comprehensive care in the home setting are needed to ease CMC family stress, provide proactive and comprehensive care to this fragile population, and avoid hospital admissions, where possible. OBJECTIVE: In this usability testing study, we aimed to assess areas of strength and opportunity within the DigiComp Kids system, a hospital-to-home intervention for CMC and their families and care providers. METHODS: Hospital-based clinicians, family members of medically complex children, and home-based clinicians participated in DigiComp Kids usability testing. Participants were recorded and tasked to think aloud while completing usability testing tasks. Participants were scored on the metrics of effectiveness, efficiency, and satisfaction, and the total usability score was calculated using the Single Usability Metric. Participants also provided insights into user experiences during the postusability testing interviews. RESULTS: A total of 15 participants (5 hospital-based clinicians, 6 family members, and 4 home-based clinicians) participated in DigiComp Kids usability testing. The participants were able to complete all assigned tasks independently. Error-free rates for tasks ranged from 58% to 100%; the average satisfaction rating across groups was ≥80%, as measured by the Single Ease Question. Task times of participants were variable compared with the task times of an expert DigiComp Kids user. Single Usability Metric scores ranged from 80.5% to 89.5%. In qualitative interviews, participants stressed the need to find the right fit between user needs and the effort required to use the system. Interviews also revealed that the value of the DigiComp Kids system was in its ability to create a digital bridge between hospital and home, enabling participants to foster and maintain connections across boundaries. CONCLUSIONS: Usability testing revealed strong scores across the groups. Insights gained include the importance of tailoring the implementation of the system to match individual user needs, streamlining key system features, and consideration of the meaning attached to system use by participants to allow for insight into system adoption and sustainment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".