Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations

Nazmul Kazi, Indika Kahanda


Abstract
Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.
Anthology ID:
W19-1918
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
140–148
Language:
URL:
https://aclanthology.org/W19-1918
DOI:
10.18653/v1/W19-1918
Bibkey:
Cite (ACL):
Nazmul Kazi and Indika Kahanda. 2019. Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 140–148, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations (Kazi & Kahanda, ClinicalNLP 2019)
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PDF:
https://aclanthology.org/W19-1918.pdf