分析患者在线互助如何形成非正式治疗方案，揭示其与临床指南的差异。
Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions
- 通过网络对话与临床访谈，研究患者如何共同构建治疗计划。
- 发现患者常偏离临床指南，因个人体验与社会支持影响决策。
- 评估大语言模型是否捕捉到这种非正式医疗知识，警示应用风险。
面对复杂且不确定的医疗状况（如癌症、心理健康问题、物质依赖康复），数百万人寻求在线同伴支持。本研究结合在线话语的内容分析与对临床医生及患者代表的民族志研究，探讨复杂疾病治疗计划的“社会建构”过程。具体以药物辅助康复治疗为例，将其与临床指南对比，揭示患者何时以及为何偏离指南。通过深度访谈临床专家，评估此类社会性构建治疗方案的影响与效果。最后，鉴于人工智能在患者沟通中的广泛应用，探究先进大语言模型（LLM）是否反映这类社会性治疗知识。采用创新的混合方法，本研究指出了面向患者的在线健康社区中沟通研究的关键方向。
原文摘要 · Abstract (English)
When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of patients seek online peer support. In this study, we leverage content analysis of online discourse and ethnographic studies with clinicians and patient representatives to characterize how treatment plans for complex conditions are "socially constructed." Specifically, we ground online conversation on medication-assisted recovery treatment to medication guidelines and subsequently surface when and why people deviate from the clinical guidelines. We characterize the implications and effectiveness of socially constructed treatment plans through in-depth interviews with clinical experts. Finally, given the enthusiasm around AI-powered solutions for patient communication, we investigate whether and how socially constructed treatment-related knowledge is reflected in a state-of-the-art large language model (LLM). Leveraging a novel mixed-method approach, this study highlights critical research directions for patient-centered communication in online health communities.
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