Creating a Parent-Informed Pediatric Emergency Department Wait Time App: Human-Centered Design Approach to Creating an AI Health Care Tool
Bibliographic record
Abstract
Background: Waiting has become an unfortunate reality for parents seeking care for their child in the emergency department (ED). Long wait times are known to increase morbidity and mortality. Providing patients with information about their wait time increases their satisfaction and sense of control. There are very few patient-facing artificial intelligence (AI) tools currently in use in EDs, particularly tools that are co-designed with patients and caregivers. Objective: The aim of this study is to use insights from parents and health care providers to inform the design of an AI tool that provides personalized wait time and health information to parents during their child's ED visit. Methods: The study followed a human-centered design methodology. The study was conducted in a large urban tertiary care academic children's hospital. Data were collected through demographic surveys, semistructured interviews, card sorting, structured observations, and prototype testing with parents and triage nurses. Quantitative data from demographic surveys and card sorting were analyzed using descriptive statistics, including means, medians, and interquartile ranges. Qualitative data from semistructured interviews and observations were analyzed using a thematic analysis. The thematic analysis informed the design criteria of the tool. The tool was implemented in the ED and improved through iterative rounds of usability testing. Results: Between May 30, 2023, and August 30, 2023, a total of 64 semistructured interviews were conducted with parents in the waiting room. Five interviews were conducted with triage nurses. Parents primarily were mothers (38/64, 59%), were college/university graduates (37/64, 58%), and had a preferred language of English (51/64, 80%). All parents had a smartphone and 97% (62/64) used apps on their smartphone. Children were a median of 7 years old (IQR 4-12 years old) and had a median of 4 lifetime visits to the ED (IQR 1 to >5). The thematic analysis revealed 5 key themes that informed the development of the tool: (1) anxiety due to uncertainty, (2) feeling forgotten, (3) low health literacy, (4) not understanding the ED process, and (5) no indication of progress. Conclusions: This study used a human-centered design approach to explore parents' experience waiting in the pediatric ED to develop an AI tool to improve the waiting experience. By prioritizing parents' experiences and insights, we created a solution that addresses the challenges of communicating wait times and contributes to a more compassionate and efficient health care environment. The implementation of this tool has given patients and families the control and certainty they were lacking by providing information about their wait time. Successful implementation of technology in health care requires a design approach so that solutions are clinically relevant, user-centered, and tested for acceptability and usability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".