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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400703317
DOIs
StatePublished - 11 May 2024
Event2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024 - Hybrid, Honolulu, United States
Duration: 11 May 202416 May 2024

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024
Country/TerritoryUnited States
CityHybrid, Honolulu
Period11/05/2416/05/24

Bibliographical note

Publisher Copyright:
© 2024 Association for Computing Machinery. All rights reserved.

Keywords

  • clinical note
  • design probe
  • interview
  • Large language model

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