NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes

Junda Wang, Zonghai Yao, Zhichao Yang, Huixue Zhou, Rumeng Li, Xun Wang, Yucheng Xu, Hong Yu


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.
Anthology ID:
2024.findings-acl.901
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15183–15201
Language:
URL:
https://aclanthology.org/2024.findings-acl.901
DOI:
10.18653/v1/2024.findings-acl.901
Bibkey:
Cite (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. Association for Computational Linguistics.
Cite (Informal):
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (Wang et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-acl.901.pdf