用大模型打造术后胃肠癌远程监护系统，提升患者管理效率
RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care
- 基于大模型构建对话式患者助手与临床仪表盘
- 通过多轮访谈设计出六项临床整合策略
- 适合医疗AI落地、远程护理研究者参考
胃肠道（GI）癌症占全球癌症死亡人数的35%以上，手术是主要治疗手段，但术后并发症难以预测且可能危及生命。本文探索大语言模型（LLMs）在远程患者监测（RPM）系统中的临床集成潜力，设计并实现名为RECOVER的LLM驱动型术后胃肠道癌症监护系统。为充分融入临床需求，研究团队通过七次参与式设计工作坊与五名医护人员、五名癌症患者访谈，提炼出六项关键设计策略，将临床指南与信息需求融入系统。系统包含面向患者的对话式代理和供医护人员使用的交互式仪表盘，支持高效术后监测。以四名医护人员和五名患者为试点，验证了设计策略的可行性，识别出核心设计要素，提出负责任AI实践建议，并为未来基于大模型的远程监护系统提供发展方向。
原文摘要 · Abstract (English)
Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related deaths worldwide, but postoperative complications are unpredictable and can be life-threatening. In this paper, we investigate how recent advancements in large language models (LLMs) can benefit remote patient monitoring (RPM) systems through clinical integration by designing RECOVER, an LLM-powered RPM system for postoperative GI cancer care. To closely engage stakeholders in the design process, we first conducted seven participatory design sessions with five clinical staff and interviewed five cancer patients to derive six major design strategies for integrating clinical guidelines and information needs into LLM-based RPM systems. We then designed and implemented RECOVER, which features an LLM-powered conversational agent for cancer patients and an interactive dashboard for clinical staff to enable efficient postoperative RPM. Finally, we used RECOVER as a pilot system to assess the implementation of our design strategies with four clinical staff and five patients, providing design implications by identifying crucial design elements, offering insights on responsible AI, and outlining opportunities for future LLM-powered RPM systems.
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