MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses Using Large Language Models

Abstract

Providing contextual and comprehensive medical information tailored to individual patients is critical for enabling effective care in the healthcare domain. However, existing approaches often struggle to deliver personalized responses due to the distributed nature of medical data across multiple sources such as patient records, medical literature, and online resources. To address this challenge, we present MedInsight, a multi-source context augmentation framework that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to augment patient-specific information from medical transcripts with trusted knowledge from textbooks and web resources to generate personalized and contextually relevant responses. Our framework consists of three phases: patient context retrieval, medical knowledge retrieval, and response generation. By augmenting patient context with relevant external knowledge, MedInsight generates contextually relevant responses, empowering patients and caregivers with actionable insights. Experiments on the MTSamples dataset validate MedInsight’s effectiveness in generating contextually appropriate medical responses, using a comprehensive set of metrics including RAGAs, TruLens, ROUGE, and BertScore. Additionally, qualitative evaluations by Subject Matter Experts (SMEs) further confirm the relevance and factual correctness of the generated responses.