Improving Electronic Health Records with NLP and LLM-RAG: A Scalable AI Method for Processing Medical Data

<p><span>The rapid adoption of Artificial Intelligence (AI) has transformed Electronic Health Records (EHRs) for clinical decision-making, yet traditional systems suffer from poor contextual awareness, slow retrieval, and limited adaptability to real-time medical updates. To overcome these challenges, this study proposes an AI-powered healthcare assistant leveraging Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs). Unlike existing chatbots that face issues with factual consistency, outdated data, and inefficient information retrieval, the proposed system integrates Groq LLaMA 3.1 (LLM), Qdrant (vector database), Hugging Face E5-large-v2 (embeddings), Tavily API (real-time search), and Supabase (authentication & storage) to provide a comprehensive solution. Through semantic search, AI-driven summarization, and dynamic access to reliable sources, the assistant significantly improves response accuracy, document search efficiency, and adaptability to evolving medical guidelines. Experimental results highlight enhanced decision support, automation, and patient care, underscoring the potential of AI-driven EHR systems to improve interactivity, intelligence, and accessibility in healthcare while enabling better real-time clinical outcomes.</span></p>