Retrieval-Augmented Generation Based Nurse Observation Extraction
This addresses the burden on nurses in the medical field by automating observation extraction, but it is incremental as it applies an existing RAG method to a specific medical task.
The paper tackled the problem of automatically extracting clinical observations from nurse dictations to reduce nurse workload, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.
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.