Toward Extracting Care Records from Transcriptions of Visiting Nurses Using Large Language Models

抄録

In this paper, we provide prompts to extract values or short texts for body temperature, appetite, daily activities, and mental state 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 using Large Language Models (LLMs) to extract transcript information related to care records automatically. This paper 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. As a result, the prompts we generate for compact output types, such as body temperature and appetite, produce outputs that closely match the ground truth data. In thirteen documents tested with this prompt, eight of them both body temperature and appetite, produced information that was in line with ground truth.