AI chatbots in the PICU: parental enthusiasm contrasts with socioeconomic usage disparities

Parents of children admitted to the PICU face an overwhelming informational landscape, necessitating accessible, patient-specific information. Large Language Models (LLMs) powering AI chatbots offer a promising solution for simplifying complex medical information. We aimed to characterize parental online health information-seeking (OHIS) behaviors and attitudes toward AI chatbots by conducting a cross-sectional survey of 139 English-speaking parents of children admitted to a large academic PICU between April-August 2024. We assessed OHIS behaviors, knowledge of and experience with AI chatbots, and attitudes regarding their potential healthcare utility. Most parents (87%) engaged in OHIS using search engines (86%). Parents with higher income and education sought information more frequently (OR 3.3, 95% CI 1.8-6.2; OR 2.9, 95% CI 1.5-5.7, respectively); those with higher education were less satisfied with online resources (OR 0.5, 95% CI 0.25-0.97). Parents expressed openness toward AI chatbots in healthcare applications (median 4/6). Significant socioeconomic disparities in current AI chatbot use favored male (OR 2.5, 95% CI 1.1-6.0) and higher income (OR 3.8, 95% CI 1.1-12.7) parents. Parents of critically ill children show high OHIS behaviors and positive attitudes toward AI chatbots. Addressing significant socioeconomic disparities in AI chatbot use is crucial for developing equitable implementation strategies in the PICU.

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Publication Details

Journal
Informatics for Health and Social Care
Published
2025-11-24
DOI
https://doi.org/10.1080/17538157.2025.2589195
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

AI chatbots in the PICU: parental enthusiasm contrasts with socioeconomic usage disparities

Sanjiv Mehta, Ryan Brandon Hunter, Sriya Kakarla, S. M. Mushiur Rahman et al.
Informatics for Health and Social Care
Artificial Intelligence in Healthcare and Education
article

AI chatbots in the PICU: parental enthusiasm contrasts with socioeconomic usage disparities

Sanjiv Mehta, Ryan Brandon Hunter, Sriya Kakarla, S. M. Mushiur Rahman, Shelley Kumar
article en

Abstract

Parents of children admitted to the PICU face an overwhelming informational landscape, necessitating accessible, patient-specific information. Large Language Models (LLMs) powering AI chatbots offer a promising solution for simplifying complex medical information. We aimed to characterize parental online health information-seeking (OHIS) behaviors and attitudes toward AI chatbots by conducting a cross-sectional survey of 139 English-speaking parents of children admitted to a large academic PICU between April-August 2024. We assessed OHIS behaviors, knowledge of and experience with AI chatbots, and attitudes regarding their potential healthcare utility. Most parents (87%) engaged in OHIS using search engines (86%). Parents with higher income and education sought information more frequently (OR 3.3, 95% CI 1.8-6.2; OR 2.9, 95% CI 1.5-5.7, respectively); those with higher education were less satisfied with online resources (OR 0.5, 95% CI 0.25-0.97). Parents expressed openness toward AI chatbots in healthcare applications (median 4/6). Significant socioeconomic disparities in current AI chatbot use favored male (OR 2.5, 95% CI 1.1-6.0) and higher income (OR 3.8, 95% CI 1.1-12.7) parents. Parents of critically ill children show high OHIS behaviors and positive attitudes toward AI chatbots. Addressing significant socioeconomic disparities in AI chatbot use is crucial for developing equitable implementation strategies in the PICU.

Informatics for Health and Social CareVol. 51(1)
Children's Hospital of Philadelphia (US), McGovern Institute for Brain Research (US), Baylor College of Medicine (US), California University of Pennsylvania (US), Texas Children's Hospital (US), The University of Texas Health Science Center (US), University of Pennsylvania (US)
Openalex Percentile: Top 70%
Artificial Intelligence in Healthcare and Education
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