Retrieval-Augmented Generation Based Nurse Observation Extraction

Abstract

Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.

Figures (6)

• Figure 1: Full illustration of Retrieval-Augmented Generation (RAG) Based Nurse Observation Extraction Pipeline
• Figure 2: Prompt for nurse dictation segmentation
• Figure 3: Prompt for description generation
• Figure 4: Prompt for segment generation for train dataset
• Figure 5: Segment Example for Train Dataset.