[1Win4] Poster session 1

[1Win4-98] Leveraging Large Language Models for Automatic Prefilling of Clinical Forms

〇Daiki Mori¹, Fernando Wong², Verónica Iturra², Rika Sato¹, Luis Loyola² (1. DeNA Co., Ltd., 2. Allm Inc.)

Keywords:LLM, Named Entity Extraction, Healthcare

The challenges of working with unstructured data in healthcare, such as free text and audio, hinder efficient clinical data management. This paper proposes a solution using large language models (LLMs) to extract data from these sources and automate the population of clinical forms. This approach aims to alleviate the documentation burden on medical personnel, improve data accuracy, and ultimately enhance patient care by allowing clinicians to focus on their primary responsibilities, highlighting the potential of LLMs for improving clinical workflows and the effectiveness of healthcare documentation.