Catch the Patient, Not the AI: Collective Sensemaking in an Online Health Community

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Patients and caregivers increasingly use artificial intelligence (AI) tools to interpret medical reports, weigh care decisions, and seek emotional support. Yet most research treats patient-facing AI as a private exchange between a user and a system, leaving open how AI-related content is taken up once users carry it back into the peer communities where they already make sense of illness. This study examines collective sensemaking around AI in House086, China's largest online community for lymphoma patients and caregivers. We identified roughly 400 publicly accessible discussion threads (2014 to 2026) through AI-related keyword searches and manual screening, extracted them into structured case profiles using a schema-prompted large language model (Claude Sonnet 4.6, deployed via AWS Bedrock), and conducted mixed-method analysis. After quality control, the verified analytic sample comprised 337 post-ChatGPT records focused on widespread conversational-AI use. Members most often reported using AI for informational support, followed by second opinions and psychosocial support. Although members often introduced AI favorably, roughly one in six described feeling overwhelmed by AI output. When other members responded, they frequently engaged the poster's underlying medical or emotional intent while leaving the AI dimension unaddressed. The tendency to bypass AI persisted even in threads seeking triangulation between AI output and other information sources, and when posters shared overwhelmed or unfavorable experiences with AI. On the occasions when members did discuss the AI, they were more often cautious than endorsing. Rather than auditing AI output for factual accuracy, the community more often worked to deflate the false certainty it produced, placing a single AI answer back among multiple sources of judgment. This study argues that AI does not replace the interpretive work of online health communities, nor is it systematically audited by them. Instead, it shifts the locus of sensemaking downstream, so that the community continues to catch the person even when it does not catch the AI, and the interpretive authority that follows does not settle in any single place. We discuss implications for CSCW theory, online-community norms, and patient-facing AI design.

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