Usability Testing of a Patient-Centered Mobile Health App for Supporting and Guiding the Pediatric Emergency Department Patient Journey: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Patient experience in emergency departments (EDs) remains often suboptimal and can be a source of stress, particularly in pediatric settings. In an attempt to support patients and their families before, during, and after their visit to a pediatric ED, a mobile health (mHealth) app was developed by a multidisciplinary team based on patient-centered care principles. OBJECTIVE: This study aims to evaluate the usability (effectiveness, efficiency, and satisfaction) of a new mHealth app, InfoKids, by potential end users through usability testing. METHODS: The app was assessed through an in-laboratory, video-recorded evaluation in which participants had to execute 9 goal-oriented tasks, ranging from account creation to the reception of a diagnostic sheet at the end of the emergency care episode. Effectiveness was measured based on the task completion rate, efficiency on time on task, and user satisfaction according to answers to the System Usability Scale questionnaire. Think-aloud usability sessions were also transcribed and analyzed. Usability problems were rated for their severity and categorized according to ergonomic criteria. RESULTS: A total of 17 parents participated in the study. The overall completion rate was 97.4% (149/153). Overall, they reported good effectiveness, with the task successfully completed in 88.2% (135/153) of cases (95% CI 83%-93%). Each task, with the exception of the first, created difficulties for some participants but did not prevent their completion by most participants. Users reported an overall good to excellent perceived usability of the app. However, ergonomic evaluation identified 14 usability problems occurring 81 time. Among these, 50% (7/14) were serious as their severity was rated as either major or catastrophic and indicated areas of improvements for the app. Following the suggested usability improvements by participants, mitigation measures were listed to further improve the app and avoid barriers to its adoption. CONCLUSIONS: Usability of the InfoKids app was evaluated as good to excellent by users. Areas of improvement were identified, and mitigation measures were proposed to inform its development toward a universal app for all ED patients visiting a digitalized institution. Its contribution could also be useful in paving the way for further research on mobile apps aimed at supporting and accompanying patients in their care episodes, as research in this area is scarce.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".