The Current State of Mobile Apps Owned by Large Pediatric Hospitals in the United States: Systematic Search and Analysis on Google Play and Apple App Stores
Notice bibliographique
Résumé
BACKGROUND: Pediatric hospitals in the United States are increasingly leveraging patient-facing mobile apps as their digital front doors for patients, families, and caretakers. These mobile health apps are sanctioned by pediatric hospitals to inform the public or populations about pediatric care to provide individualized information, to enhance communication, and to improve patient experience. Yet the functionalities and user feedback of these hospital mobile apps have not been systematically investigated. OBJECTIVE: Our aim was to understand the current state of hospital-owned mobile apps provided by large pediatric hospitals, comparatively analyze and report the services provided, and identify potential gaps to inform developers and providers. The American Hospital Association defines large hospitals as those having a bed count of more than 400. METHODS: We conducted a systematic search on Google Play and Apple App Store to identify all hospital-owned mobile apps from the large pediatric hospitals included in our review. Our inclusion criteria were (1) apps provided by large pediatric hospitals; (2) hospital-owned apps available in Apple App Store and Google Play; and (3) apps that are provided for general populations. Specialty apps that serve specific user groups or populations focusing on education, telehealth, specific conditions or procedures, or apps intended for research or clinician use were excluded. The features and functionality of the included apps were examined. RESULTS: Of the 16 pediatric hospitals included in our review, 4 (25%) had no general patient-facing apps, 4 (25%) had one app, and 8 (50%) had more than one app available on Google Play or Apple App Store. The 12 hospitals with at least one mobile app had a combined total of 72 apps. Of these 72 apps, 61 (85%) were considered specialty and were excluded from our review, leaving a total of 11 (15%) apps to analyze. Among the 11 apps analyzed, the most common feature was appointment scheduling or reminder (n=9, 82%). Doctor search (n=8, 73%) and patient resources (n=8, 73%) were the second most common, followed by payment, billing, or claims (n=7, 64%), patient portal integration (n=6, 55%), personal health management (n=6, 55%), hospital way finding (n=5, 45%), message a provider (n=4, 36%), urgent care wait times (n=4, 36%), video chat (n=4, 36%), and health information access (n=4, 36%). Parking information (n=3, 27%) was the least common. CONCLUSIONS: Out of the 16 pediatric hospitals identified for our review, 75% (n=12) offer mobile apps. Based on the most common features, these apps were intended to help improve accessibility for patients and families in terms of finding providers, scheduling appointments, and accessing patient resources. We believe the findings will inform pediatric hospital administrators, developers, and other stakeholders to improve app feature offerings and increase their impact on service accessibility and patient experience.
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,002 | 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.
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