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：我们介绍了NoteChat，这是一种新颖的协同多智能体框架，利用大型语言模型（LLMs）生成患者-医生对话。NoteChat体现了这样一个原则：通过结构化的角色扮演和策略性提示，一组特定角色的LLMs能够更有效地执行其指定角色。这些角色扮演LLMs之间的协同作用产生了连贯且高效的对话生成。在MTS-dialogue（一个用于患者-医生对话-笔记对的基准数据集）上的评估表明，使用NoteChat增强的合成患者-医生对话进行训练的模型，在生成临床笔记方面优于其他最先进的模型。我们全面的自动和人工评估表明，在基于临床笔记生成优质合成患者-医生对话方面，NoteChat显著超越了ChatGPT和GPT-4等最先进模型，领域专家评估的领先幅度高达22.78%。NoteChat有潜力直接吸引患者参与并辅助临床文档工作，而后者是导致医生职业倦怠的主要原因之一。