We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle that an ensemble of role-specific LLMs, through structured role-play and strategic prompting, can perform their assigned roles more effectively. The synergy among these role-playing LLMs results in a cohesive and efficient dialogue generation. Evaluation on MTS-dialogue, a benchmark dataset for patient-physician dialogues-note pairs, shows that models trained with the augmented synthetic patient-physician dialogues by NoteChat outperforms other state-of-the-art models for generating clinical notes. Our comprehensive automatic and human evaluation demonstrates that NoteChat substantially surpasses state-of-the-art models like ChatGPT and GPT-4 up to 22.78% by domain experts in generating superior synthetic patient-physician dialogues based on clinical notes. NoteChat has the potential to engage patients directly and help clinical documentation, a leading cause of physician burnout.

翻译：我们提出NoteChat，一种新颖的协作式多智能体框架，利用大语言模型生成医患对话。NoteChat体现了一个原则：通过结构化角色扮演和策略性提示，一组角色特定的大语言模型能够更有效地执行其分配的角色。这些角色扮演大语言模型之间的协同作用产生连贯且高效的对话生成。在医患对话-笔记对基准数据集MTS-dialogue上的评估表明，使用NoteChat增强的合成医患对话训练的模型在生成临床笔记方面优于其他最先进模型。我们的综合自动化和人工评估显示，NoteChat在基于临床笔记生成优质合成医患对话方面，显著超越ChatGPT和GPT-4等最先进模型达22.78%（由领域专家评估）。NoteChat具有直接与患者互动并辅助临床文档撰写的潜力，而临床文档撰写是导致医生职业倦怠的主要原因之一。