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 language | English |
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Title of host publication | CHI 2024 - Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Sytems |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9798400703317 |
DOIs | |
State | Published - 11 May 2024 |
Event | 2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024 - Hybrid, Honolulu, United States Duration: 11 May 2024 → 16 May 2024 |
Publication series
Name | Conference on Human Factors in Computing Systems - Proceedings |
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Conference
Conference | 2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024 |
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Country/Territory | United States |
City | Hybrid, Honolulu |
Period | 11/05/24 → 16/05/24 |
Bibliographical note
Publisher Copyright:© 2024 Association for Computing Machinery. All rights reserved.
Keywords
- Large language model
- clinical note
- design probe
- interview