Towards Better Health Conversations: The Benefits of Context-seeking
Year: 2026
Type: conference-paper
Abstract: Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inaccurate, biased or misleading. We present insights from 5 mixed-methods studies (total N=261), examining how people interact with LLMs for their own health questions. Qualitative studies revealed the importance of context-seeking in conversational AIs to elicit specific details a person may not volunteer or know to share. Context-seeking by LLMs was valued by participants, even if it meant deferring an answer for several turns. Incorporating these insights, we developed a “Wayfinding AI” to proactively solicit context. In two randomized, blinded studies, participants rated the Wayfinding AI as more helpful, relevant, and tailored to their concerns compared to a baseline AI. These results demonstrate the strong impact of proactive context-seeking on conversational dynamics, and suggest design patterns for conversational AI to help navigate health topics.
Authors: Rory Sayres, Yuexing Hao, Abbi Ward, Amy Wang, Beverly Freeman, Serena Zhan, Diego Ardila, Jimmy Li, I-Ching Lee, Anna Iurchenko, Siyi Kou, Kartikeya Badola, Jimmy Hu, Bhawesh Kumar, Keith Y Johnson, Supriya Vijay, Justin Krogue, Avinatan Hassidim, Yossi Matias, Dale Webster, Sunny Virmani, Yun Liu, Quang Duong, Mike Schaekermann
Institutions: Google (United States), Google (Israel)
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416057736