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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-1918.pdf