用AI助手帮医生快速理解患者自动生成的健康数据。
Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction
- 用大模型自动生成健康数据摘要，辅助医生快速掌握信息
- 对话式交互让医生灵活分析多源健康数据，弥补专业差距
- 研究揭示了隐私与信任风险，为临床AI设计提供实证参考
越来越多患者通过可穿戴设备和智能手机生成大量个人健康与生活方式数据。尽管这些数据有望推动预防性医疗，但其规模庞大、类型多样，加之医护人员时间紧张和数据素养不足，导致难以融入临床实践。本研究以心血管疾病（CVD）风险降低为应用场景，通过混合方法学，让16名医护人员使用集成常见图表、大语言模型（LLM）生成摘要及对话接口的原型系统，评估其对患者生成健康数据（PGHD）的理解支持效果。结果表明，AI生成的摘要能提供快速概览，作为探索起点；对话交互则支持灵活分析，有效弥合数据素养差距。然而，医护人员也提出对透明度、隐私保护及过度依赖的担忧。本研究贡献了关于将AI驱动的摘要与对话功能整合进临床工作流以支持PGHD理解的实证发现与社会技术设计启示。
原文摘要 · Abstract (English)
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.
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