AI-Assisted Health Information Seeking and Mothers’ Preparedness for Pediatric Emergencies: The Roles of Trust, Perceived Usefulness, and Information Verification

Danur Azissah R Sofais; Handi Rustandi; Dulce Elda Ximenes dos Reis; Firman Oswari

doi:10.70716/mohr.v3i3.607

Authors

Danur Azissah R Sofais Health Science Department, Universitas Dehasen, Bengkulu, Indonesia
Handi Rustandi Health Science Department, Universitas Dehasen, Bengkulu, Indonesia
Dulce Elda Ximenes dos Reis Public Health Department, Dili University, Dili, Timor Leste
Firman Oswari Health Science Department, Universitas Dehasen, Bengkulu, Indonesia

DOI:

https://doi.org/10.70716/mohr.v3i3.607

Keywords:

Artificial Intelligence, Health Information Seeking, Mothers, Pediatric Emergencies, Preparedness, Information Verification

Abstract

Artificial intelligence (AI) has increasingly become a source of health information for parents, yet its role in supporting preparedness for pediatric emergencies remains unclear. This study examined the relationships between dimensions of AI-assisted health information seeking and mothers’ preparedness for pediatric emergencies. A cross-sectional survey was conducted among 385 mothers with children aged 0–12 years in Indonesia. Data were collected through an online questionnaire measuring AI-assisted health information seeking, including frequency of use, trust in AI, perceived usefulness, and information verification, as well as preparedness for pediatric emergencies. Data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression in IBM SPSS Statistics version 27. Information verification showed the strongest positive association with preparedness, whereas frequency of AI use demonstrated only a weak correlation.. Multiple linear regression revealed that information verification was the strongest predictor of preparedness (β = 0.41, p < 0.001), followed by perceived usefulness (β = 0.18, p < 0.001) and trust in AI (β = 0.12, p = 0.004). Frequency of AI use was not significantly associated with preparedness (p = 0.102). The model explained 38% of the variance in preparedness scores. Mothers’ preparedness for pediatric emergencies is influenced more by critical engagement with AI-generated information than by the frequency of AI use. Strengthening AI literacy and information verification skills may enhance the safe and effective use of AI in child health decision-making.

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References

Alnajjar, H. A., Hantira, N. Y., Kassem, F. K., Ahmed, H. A., Fouda, M. E., & Elkaluby, E. A. (2025). Enhancing Mothers? Preparedness for Home Safety and Emergency Response: The Effect of Simulation-Based First Aid Training among Mothers of Under-Five Children [version 1; peer review: awaiting peer review] . F1000Research, 14(1223). https://doi.org/10.12688/f1000research.171977.1

Alwasedi, A. M., Al-Wathinani, A. M., Gómez-Salgado, J., Abahussain, M. A., Alnajada, A., & Goniewicz, K. (2025). Maternal knowledge of pediatric first aid in Riyadh: Addressing gaps for improved child safety and women’s health outcomes. Medicine, 104(7). https://journals.lww.com/md-journal/fulltext/2025/02140/maternal_knowledge_of_pediatric_first_aid_in.4.aspx

Bharel, M., Auerbach, J., Nguyen, V., & DeSalvo, K. B. (2024). Transforming Public Health Practice With Generative Artificial Intelligence. Health Affairs, 43(6), 776–782. https://doi.org/10.1377/hlthaff.2024.00050

Branda, F., Stella, M., Ceccarelli, C., Cabitza, F., Ceccarelli, G., Maruotti, A., Ciccozzi, M., & Scarpa, F. (2025). The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review. In Future Internet (Vol. 17, Issue 4, p. 145). https://doi.org/10.3390/fi17040145

Chong, S.-L., Goh, M. S. L., Ong, G. Y.-K., Acworth, J., Sultana, R., Yao, S. H. W., Ng, K. C., Scholefield, B., Aickin, R., Maconochie, I., Atkins, D., Couto, T. B., Guerguerian, A.-M., Kleinman, M., Kloeck, D., Nadkarni, V., Nuthall, G., Reis, A., Rodriguez-Nunez, A., … Morley, P. (2022). Do paediatric early warning systems reduce mortality and critical deterioration events among children? A systematic review and meta-analysis. Resuscitation Plus, 11, 100262. https://doi.org/https://doi.org/10.1016/j.resplu.2022.100262

Demblon, M.-C., Bicknell, C., & Aufegger, L. (2023). Systematic review of the development and effectiveness of digital health information interventions, compared with usual care, in supporting patient preparation for paediatric hospital care, and the impact on their health outcomes. Frontiers in Health Services, Volume 3-. https://doi.org/10.3389/frhs.2023.1103624

Fang, Z., Liu, Y., & Peng, B. (2024). Empowering older adults: bridging the digital divide in online health information seeking. Humanities and Social Sciences Communications, 11(1), 1748. https://doi.org/10.1057/s41599-024-04312-7

Gill, F. J., Cooper, A., Falconer, P., Stokes, S., & Leslie, G. D. (2022). Development of an evidence-based ESCALATION system for recognition and response to paediatric clinical deterioration. Australian Critical Care, 35(6), 668–676. https://doi.org/https://doi.org/10.1016/j.aucc.2021.09.004

Guo, Shuangyan, Song, Yang, Chen, Guanyun, Han, Hongxin, Wu, Hong, & Ma, Jingdong. (2025). Promoting trust and intention to adopt health information generated by ChatGPT among healthcare customers: An empirical study. DIGITAL HEALTH, 11, 20552076251374120. https://doi.org/10.1177/20552076251374121

Kasthuri, V. S., Glueck, J., Pham, H., Daher, M., Balmaceno-Criss, M., McDonald, C. L., Diebo, B. G., & Daniels, A. H. (2024). Assessing the accuracy and reliability of AI-generated responses to patient questions regarding spine surgery. JBJS, 106(12), 1136–1142. https://doi.org/10.2106/JBJS.23.00914

Kolivand, P., Azari, S., Bakhtiari, A., Namdar, P., Ayyoubzadeh, S. M., Rajaie, S., & Ramezani, M. (2025). AI applications in disaster governance with health approach: A scoping review. Archives of Public Health, 83(1), 218. https://doi.org/10.1186/s13690-025-01712-2

Lermann Henestrosa, A., & Kimmerle, J. (2025). “Always check important information!” - The role of disclaimers in the perception of AI-generated content. Computers in Human Behavior: Artificial Humans, 4, 100142. https://doi.org/https://doi.org/10.1016/j.chbah.2025.100142

Li, F., & Yang, Y. (2024). Impact of artificial intelligence–generated content labels on perceived accuracy, message credibility, and sharing intentions for misinformation: Web-based, randomized, controlled experiment. JMIR Formative Research, 8(1), e60024.

Melhem, S., Kayyali, R., Nabhani-Gebara, S., Almomani, H., Elian, M., Alabbadi, I., Almousa, R., Almousa, A., & Alrashdan, Y. (2025). The Role of Digital Literacy, Health Literacy, and Information-Seeking Behaviour in Cancer Care: Empowering Survivors through Knowledge and Action (J. Fusi, G. Scarfò, & F. Franzoni (eds.)). IntechOpen. https://doi.org/10.5772/intechopen.1009800

Newgard, C. D., Lin, A., Malveau, S., Cook, J. N. B., Smith, M., Kuppermann, N., Remick, K. E., Gausche-Hill, M., Goldhaber-Fiebert, J., Burd, R. S., Hewes, H. A., Salvi, A., Xin, H., Ames, S. G., Jenkins, P. C., Marin, J., Hansen, M., Glass, N. E., Nathens, A. B., … Group, P. R. S. (2023). Emergency Department Pediatric Readiness and Short-term and Long-term Mortality Among Children Receiving Emergency Care. JAMA Network Open, 6(1), e2250941–e2250941. https://doi.org/10.1001/jamanetworkopen.2022.50941

Onyejesi, C. D., Alsabri, M., Del Castillo Miranda, J. C., Aziz, M. M., Ram, M. D., Abady, E. M., & Abdelbar, S. M. (2025). Pediatric emergency disaster preparedness: a narrative review of global disparities, challenges, and policy solutions. International Journal of Emergency Medicine, 18(1), 91. https://doi.org/10.1186/s12245-025-00856-w

Pennestrì, F., Cabitza, F., Picerno, N., & Banfi, G. (2025). Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper. BMC Medical Informatics and Decision Making, 25(1), 56. https://doi.org/10.1186/s12911-025-02883-2

Rabbani, S. A., El-Tanani, M., Sharma, S., Rabbani, S. S., El-Tanani, Y., Kumar, R., & Saini, M. (2025). Generative Artificial Intelligence in Healthcare: Applications, Implementation Challenges, and Future Directions. In BioMedInformatics (Vol. 5, Issue 3, p. 37). https://doi.org/10.3390/biomedinformatics5030037

Raghunath, A., Maharjan, P., Toprakbasti, Z., & Anderson, R. (2025). “Being Hopeful is Mandatory”: Characterizing Gaps Between the Imaginaries and Realities of AI-enabled Healthcare in Nepal. Proc. ACM Hum.-Comput. Interact., 9(7). https://doi.org/10.1145/3757526

Sánchez-García, P., Diez-Gracia, A., Mayorga, I. R., & Jerónimo, P. (2025). Media Self-Regulation in the Use of AI: Limitation of Multimodal Generative Content and Ethical Commitments to Transparency and Verification. In Journalism and Media (Vol. 6, Issue 1, p. 29). https://doi.org/10.3390/journalmedia6010029

Svestkova, Adela, Huang, Yi, & Smahel, David. (2025). Factors that influence trust and willingness to use generative AI for health information: A cross-sectional study. DIGITAL HEALTH, 11, 20552076251360972. https://doi.org/10.1177/20552076251360973

Whiles, B. B., Bird, V. G., Canales, B. K., DiBianco, J. M., & Terry, R. S. (2023). Caution! AI Bot Has Entered the Patient Chat: ChatGPT Has Limitations in Providing Accurate Urologic Healthcare Advice. Urology, 180, 278–284. https://doi.org/https://doi.org/10.1016/j.urology.2023.07.010