The Investigation of the Application of RAG Technology in the Field of EHR

Abstract

Electronic Health Records (EHR) play a crucial role in contemporary medical information systems, capturing extensive data in multiple scenarios. This review explores the impact of Retrieval Augmented Generation (RAG) on managing EHR. By integrating retrieval and generation processes, RAG significantly enhances data handling, clinical decision support, and patient management. EHRs contain extensive data like patient histories and diagnostic information, which are becoming increasingly complex. The RAG improves the accuracy and richness of this data processing by using a retrieval module to extract relevant text fragments and a generation module to create precise outputs. Despite its potential, RAG faces challenges such as data inconsistency, privacy concerns, and the need for efficient training. Recent studies highlight the RAG’s effectiveness in summarizing clinical notes and enhancing prediction accuracy, suggesting a promising future in healthcare. However, ongoing research is necessary to optimize the RAG’s application and address these challenges, aiming to transform healthcare delivery effectively.