Studying Challenges in Medical Conversation with Structured Annotation

Nan Wang, Yan Song, Fei Xia


Abstract
Medical conversation is a central part of medical care. Yet, the current state and quality of medical conversation is far from perfect. Therefore, a substantial amount of research has been done to obtain a better understanding of medical conversation and to address its practical challenges and dilemmas. In line with this stream of research, we have developed a multi-layer structure annotation scheme to analyze medical conversation, and are using the scheme to construct a corpus of naturally occurring medical conversation in Chinese pediatric primary care setting. Some of the preliminary findings are reported regarding 1) how a medical conversation starts, 2) where communication problems tend to occur, and 3) how physicians close a conversation. Challenges and opportunities for research on medical conversation with NLP techniques will be discussed.
Anthology ID:
2020.nlpmc-1.3
Volume:
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations
Month:
July
Year:
2020
Address:
Online
Editors:
Parminder Bhatia, Steven Lin, Rashmi Gangadharaiah, Byron Wallace, Izhak Shafran, Chaitanya Shivade, Nan Du, Mona Diab
Venue:
NLPMC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://aclanthology.org/2020.nlpmc-1.3
DOI:
10.18653/v1/2020.nlpmc-1.3
Bibkey:
Cite (ACL):
Nan Wang, Yan Song, and Fei Xia. 2020. Studying Challenges in Medical Conversation with Structured Annotation. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, pages 12–21, Online. Association for Computational Linguistics.
Cite (Informal):
Studying Challenges in Medical Conversation with Structured Annotation (Wang et al., NLPMC 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.nlpmc-1.3.pdf
Video:
 http://slideslive.com/38929889