Retrieval-Augmented Generation Based Nurse Observation Extraction

arXiv cs.CL / 3/30/2026

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Key Points

The paper proposes an automated pipeline that extracts clinical observations from nurse dictations to reduce nursing workload, leveraging recent advances in large language models (LLMs).
To improve accuracy, it uses a Retrieval-Augmented Generation (RAG) approach rather than relying only on the LLM’s internal knowledge.
The method is evaluated on the MEDIQA-SYNUR test dataset and reports an F1-score of 0.796.
The work positions nurse note/observation extraction as a practical medical NLP use case enabled by RAG-based LLM systems.

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.