Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.

翻译：个体正日益生成大量个人健康与生活方式数据，例如通过可穿戴设备和智能手机。尽管此类数据可能改变预防性医疗，但其融入临床实践却受限于数据规模、异质性以及医疗专业人员面临的时间压力和数据素养限制。本研究探讨大型语言模型如何通过自动摘要和自然语言数据探索来支持患者生成健康数据的感知理解。以心血管疾病风险降低为应用场景，16名医疗专业人员在混合方法研究中通过集成常规图表、LLM生成摘要和对话界面的原型系统审阅了多模态患者生成健康数据。研究发现：AI摘要能提供快速概览并锚定探索方向，而对话交互则支持灵活分析并弥合数据素养差距。然而，医疗专业人员对透明度、隐私和过度依赖问题表示担忧。我们通过实证研究提出社会技术设计启示，为将AI驱动的摘要生成与对话系统整合至临床工作流以支持患者生成健康数据感知提供参考。