Abstract
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.
Abstract (translated)
个人越来越多地通过可穿戴设备和智能手机等途径生成大量的健康和个人生活方式数据。尽管这种数据能够改变预防性护理，但由于其规模、异质性和医疗保健专业人员（HCPs）的时间压力及数据素养等因素，将其整合到临床实践中面临许多挑战。我们探讨了大型语言模型（LLMs）如何通过自动生成摘要和自然语言数据分析来支持对患者生成的健康数据（PGHD）的理解。 以心血管疾病（CVD）风险降低为例，我们在一项混合方法研究中让16名HCP使用一个原型工具审查多模态PGHD。该原型集成了常见图表、LLM生成的摘要以及对话式界面。研究结果表明，AI生成的摘要提供了快速概览，并为探索奠定了基础；而对话式的交互支持了灵活的分析，并弥补了数据素养差距。然而，HCP们也对透明度、隐私和过度依赖提出了担忧。 我们的研究贡献了将AI驱动的总结和对话整合到临床工作流程中以支持PGHD理解的经验见解和社会技术设计启示。
URL
https://arxiv.org/abs/2602.05687
