A LOW CODE APPROACH TO Q&A ON CARE RECORDS USING FLOWISE AI WITH LLM INTEGRATION AND RAG METHOD

Abstract

Care records are vital for monitoring patient conditions and supporting clinical decision-making, but their diverse formats—such as tables, narrative sentences, checklists, and fill-in-the-blank fields—present challenges for efficient information retrieval. Traditional retrieval methods are often time-consuming and error-prone, while automated systems struggle with contextual accuracy in complex medical language. This study proposes a low-code approach to develop a question-and-answer (Q&A) system for care records using Flowise AI integrated with Retrieval-Augmented Generation (RAG) methodology. By utilizing LangChain and OpenAI’s language models, Flowise AI provides a framework for constructing a Q&A system that retrieves information accurately across different documentation formats. The system employs components such as Recursive Character Text Splitter, PDF processing, OpenAI Embeddings, In-Memory Vector Store, and a Conversational Retrieval QA Chain, ensuring efficient retrieval with contextual relevance. Our results demonstrate high accuracy in aligning the Q&A responses with ground truth data, validating the system's effectiveness in healthcare documentation retrieval. This low-code solution not only enhances accessibility for non-technical users but also empowers healthcare professionals with a scalable tool for quick access to critical patient data. The findings underscore the potential of low-code AI systems like Flowise AI, utilizing RAG, to improve information retrieval in healthcare, supporting more accurate and timely clinical decisions.