Creating a Parent-Informed Pediatric Emergency Department Wait Time App: Human-Centered Design Approach to Creating an AI Health Care Tool
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».