Using Language Models for Extracting Insights from Medical Records

This research article presents a study on the application of Large Language Models (LLMs) in extracting insights from patient medical records, focusing on the challenges posed by unstructured medical data. The study outlines a proposed automated question-answering system designed to enhance patient care by improving the retrieval and interpretation of medical records formatted in FHIR. It details the use of the Langchain agent framework and Llama-Index for effective reasoning and decision-making, achieving an accuracy of 87.98% in generating responses without extensive model training. The methodology includes data extraction, transformation, and indexing processes, emphasizing the importance of converting complex hierarchical data into a more accessible natural language format. The article also discusses the integration of various technologies to streamline information retrieval and improve the chatbot's performance in healthcare settings. Overall, the research aims to contribute to advancements in conversational AI within the healthcare domain.