Balancing Health Information-Seeking through Retrieval-Augmented Generation-Based LLM Chatbot

Authors

Gargi Nandy, University of Nebraska at OmahaFollow
Srishti Gupta, University of Nebraska at Omaha
Farhad Mohammad Afzali, University of Nebraska at OmahaFollow
Eric Peeples, University of Nebraska Medical CenterFollow
Betsy Pilon, HOPE for HIEFollow
Chun-Hua Tsai, University of Nebraska at OmahaFollow

Author ORCID Identifier

Tsai - https://orcid.org/0000-0001-9188-0362 Nandy - https://orcid.org/0000-0002-1681-2551  Gupta - https://orcid.org/0000-0002-2354-0507  Afzali - https://orcid.org/0000-0001-7457-9148 Peeples - https://orcid.org/0000-0002-2892-2678

Document Type

Conference Proceeding

Publication Date

6-12-2025

Publication Title

UMAP Adjunct '25: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalizati

First Page

249

Last Page

254

DOI

https://doi.org/10.1145/3708319.3733709

Abstract

Family caregivers play a vital role in supporting children with chronic health conditions, such as neonates diagnosed with hypoxic-ischemic encephalopathy (HIE). However, navigating complex medical information can be overwhelming due to the quantity and quality of available literature. This study leverages Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) to develop a chatbot that integrates peer-reviewed scientific literature and provides personalized, simplified summaries for caregivers. A user study involving six caregivers and five healthcare providers demonstrated the chatbot’s ability to enhance clarity, improve comprehension, and deliver essential medical information concisely. Our findings highlight the potential of RAG-based LLMs to enhance caregivers’ health literacy and support their information-seeking behavior, while also underscoring the importance of thoughtfully navigating the differing expectations of caregivers and healthcare providers regarding the type, depth, and presentation of medical information.

Comments

PDF passed Adobe accessibility checker prior to upload.

Recommended Citation

Gargi Nandy, Srishti Gupta, Farhad Mohammad Afzali, Eric Peeples, Betsy Pilon, and Chun-Hua Tsai. 2025. Balancing Health Information-Seeking through Retrieval-Augmented Generation-Based LLM Chatbot. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '25). Association for Computing Machinery, New York, NY, USA, 249–254. https://doi.org/10.1145/3708319.3733709

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.