@inproceedings{wang-etal-2024-notechat,
title = "{N}ote{C}hat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes",
author = "Wang, Junda and
Yao, Zonghai and
Yang, Zhichao and
Zhou, Huixue and
Li, Rumeng and
Wang, Xun and
Xu, Yucheng and
Yu, Hong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.901",
pages = "15183--15201",
abstract = "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.",
}
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%0 Conference Proceedings
%T NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes
%A Wang, Junda
%A Yao, Zonghai
%A Yang, Zhichao
%A Zhou, Huixue
%A Li, Rumeng
%A Wang, Xun
%A Xu, Yucheng
%A Yu, Hong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wang-etal-2024-notechat
%X 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.
%U https://aclanthology.org/2024.findings-acl.901
%P 15183-15201
Markdown (Informal)
[NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes](https://aclanthology.org/2024.findings-acl.901) (Wang et al., Findings 2024)
ACL
- Junda Wang, Zonghai Yao, Zhichao Yang, Huixue Zhou, Rumeng Li, Xun Wang, Yucheng Xu, and Hong Yu. 2024. NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes. In Findings of the Association for Computational Linguistics ACL 2024, pages 15183–15201, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.