Impact of Digitalization on Pediatric Practice and Childhood Health Care in Spain: Nationwide Survey Study
Notice bibliographique
Résumé
BACKGROUND: Health care digitalization and pediatric information and communication technology have facilitated the use of telemedicine and digital communication tools in pediatric practice, improving accessibility and efficiency. Meanwhile, artificial intelligence (AI) is emerging as a promising tool in medicine. However, the rapid adoption of these technologies has raised concerns regarding reliability and ethics. OBJECTIVE: This study examines the level of digitalization in pediatric consultations and explores the perspectives of health care professionals (HCPs) on digital technologies in patient care, analyzing differences by age group, health care management, and institution type. METHODS: An observational, cross-sectional survey was conducted among Spanish HCPs dedicated to pediatric care. Participants completed an 18-question web-based questionnaire evaluating their use of digital communication tools, perceptions of online health information, and opinions on AI in clinical practice. Statistical analyses compared responses across age groups, health care management type, and institution type. RESULTS: A total of 495 pediatric specialists participated (female: 273/495, 58.2%; aged >45 y: 324/495, 69.8%). Most participants worked in urban settings (409/469, 87.2%), in primary care (243/313, 77.6%), and in the public sector (253/464, 54.5%). The telephone remained the most used communication channel (462/481, 96.1%), followed by email (290/481, 60.3%) and WhatsApp (139/481, 28.9%). Private-sector HCPs used digital platforms more frequently than public-sector HCPs, including email (136/206, 66% vs 135/247, 54.7%; P=.02), Instagram (23/206, 11.2% vs 5/247, 2%; P<.001), WhatsApp (105/206, 51% vs 26/247, 10.5%; P<.001), and Facebook (18/206, 8.7% vs 3/247, 1.2%; P<.001). Nearly all respondents (417/437, 95.4%) believed that parents were increasingly seeking health information online, yet a considerable proportion (158/458, 34.5%) reported that parents rarely consulted them about reliable sources. Overall, 85.4% (410/480) agreed that the internet and social media raise many questions among parents, while only 7.5% (35/465) believed that the information found is generally reliable. Nearly half of the participants (232/443, 48.5%) proactively suggested trustworthy digital resources, while 44.1% (211/443) did so only when asked. Younger respondents (P=.002), public-sector HCPs (P=.005), and primary care specialists (P=.004) were significantly more likely to offer this guidance, with scientific society resources being the most frequently recommended (402/430, 93.5%). We found that 78.6% (369/470) of participants were familiar with AI, with private-sector HCPs demonstrating greater knowledge than public-sector HCPs (P=.003). Overall, 59.6% (279/468) agreed that AI could significantly improve medicine, a view more commonly held by private-sector HCPs (P=.005). However, 94.8% (306/323) expressed ethical concerns, and 89.8% (422/470) wished to receive AI-related training. CONCLUSIONS: The survey highlights the increasing use of digital communication tools in pediatric practice, with private-sector HCPs leading adoption. While AI is viewed as promising, ethical dilemmas remain, underscoring the need for training. Limited confidence in online health information highlights the importance of strengthening digital literacy among both HCPs and parents to optimize patient care.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».