2024-10-26 –, CLASS #2
Language: English
We provide prompts to extract values or short texts for body temperature, appetite and daily activities from automated transcripts of nurses’ words. However, because the conversation is intended to be as relaxed as possible, there are challenges in summarizing these dialogues into specific information that matches the care records. For example, some information in the transcript does not clearly convey certain information. In response to these challenges, we propose to use LangChain to process Large Language Models (LLMs) in extracting transcript information related to care records automatically. This study investigates the effectiveness of LLMs in automatically identifying and extracting relevant care record information from communication sessions through automated transcription, with the ultimate goal of simplifying the documentation process and improving the quality of services provided to the elderly population.
Researcher at Sozolab Kyutech, Kitakyushu, Japan
Lecturer at Universitas Malikussaleh, Aceh, Indonesia