Publications

Detailed Information

AscleAI: A LLM-based Clinical Note Management System for Enhancing Clinician Productivity

Cited 0 time in Web of Science Cited 0 time in Scopus
Authors

Han, Jiyeon; Park, Jimin; Huh, Jinyoung; Oh, Uran; Do, Jaeyoung; Kim, Daehee

Issue Date
2024
Publisher
Association for Computing Machinery
Citation
Conference on Human Factors in Computing Systems - Proceedings
Abstract
While clinical notes are essential to the field of healthcare, they pose several challenges for clinicians since it is difficult to write down medical information, review prior notes, and extract the desired information at the same time while examining a patient. Thus, we designed a system that can automatically generate clinical notes from dialogues between patients and clinicians and provide specific information upon clinicians' query using a Large Language Model (LLM) both in real-time. To explore how this system can be used to support clinicians in practice, we conducted an interview with six clinicians followed by a design probe study with the current version of our system for feedback. Findings suggest that our system has the potential to enable clinicians to write and access clinical notes and examine the patients simultaneously with reduced cognitive loads and increased efficiency and accuracy.
URI
https://hdl.handle.net/10371/204612
DOI
https://doi.org/10.1145/3613905.3650784
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area AI 애플리케이션을 위한 알고리즘-시스템 공동 설계, AI-powered Big Data Management, Generative AI, Large Language Model, ML, 고성능 대규모 AI 데이터 분석 및 처리, 모달 AI

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share