Applying Retrieval-Augmented Generation on Open LLMs for a Medical Chatbot Supporting Hypertensive Patients

Disease management, especially for chronic conditions or the elderly, involves continuous monitoring, lifestyle adjustments, and frequent healthcare interactions, necessitating effective home-care ICT solutions. To address these needs, chatbot technology has emerged as a promising tool for supporting patients in managing their health autonomously. In this context, chatbots must provide timely and accurate information and continuous empathetic support to maintain patient engagement. Additionally, data privacy concerns necessitate avoiding third-party Natural Language Processing and Generation services. To meet these needs, in this paper we propose the development of a chatbot to support patients in managing chronic conditions, focusing on hypertension. Particularly, we utilise open-source large language models to avoid proprietary systems due to privacy requirements. Given that their performance, based on state-of-the-art metrics, do not compete third-party services, we incorporate retrieval augmented generation (RAG) techniques, building a knowledge base with input from medical professionals to enhance model performance. We evaluated seven open-source models, including two specifically trained in the medical domain. Our results indicate that RAG significantly improves performance, surpassing that of specialised medical-domain models without RAG. This approach offers a promising solution for managing chronic conditions independently and securely.