AI-Augmented EHR Systems: Leveraging GRAG and NLP for Efficient Data Visualization and Entry

Abstract

Electronic Health Records (EHR) play a vital role in managing patient charts, encompassing both structured and unstructured data across various modalities. While EHR systems have already improved communication across medical facilities and addressed issues like poorly written notes, modern artificial intelligence techniques, especially Large Language Models (LLMs), offer even greater potential for enhancement. LLMs can process and integrate the multimodal nature of EHR data, enabling advanced applications in data management and analysis without sacrificing the foundational progress made by traditional systems. In this paper, we propose to develop and implement advanced techniques such as Graph Retrieval Augmented Generation (GRAG) and Natural Language Processing (NLP) to transform data visualization within EHR systems. Our approach aims to provide healthcare providers, such as nurses and doctors, with a more intuitive and efficient means of accessing patient information, which is essential for rapid decision-making. Additionally, we are exploring the use of Large Language Models (LLMs) to optimize data entry tasks, thereby reducing the administrative burden on healthcare providers. By leveraging these AI-driven methods, we seek to foster critical thinking in clinical settings by automating repetitive tasks and enhancing the visualization of complex patient data. This research underscores the potential of LLMs and other AI techniques to further enhance the functionality of EHRs, ultimately contributing to a more effective and streamlined healthcare delivery system.