SAGE: System for Accessible Guided Exploration of Health Information

Sabriya Maryam Alam, Haodi Zou, Reya Vir, Niloufar Salehi

Track Selection: Track 1: Developing LLM-powered tools for positive outcomes

Keywords: Human-Computer Interaction, Human-Centered AI, Intelligent User Interfaces, Planning and Decision Support for Human-Machine Teams, Knowledge Representation and Reasoning, Applications, Information Extraction, Question Answering, Patient Education, Health Informatics

TL;DR: SAGE fills patients’ knowledge gaps using LLMs and Knowledge Graphs to organize health information for accessible navigation, recommend insightful questions, and deliver clear, accurate answers tailored to patients’ individual health literacy levels.

Abstract: The Center for Disease Control estimates that six in ten adults in the United States currently live with a chronic disease such as cancer, heart disease, or diabetes. Yet most patients lack sufficient access to comprehensible information and guidance for effective self-management of chronic conditions and remain unaware of gaps in their knowledge. To address this challenge, we introduce SAGE, a System for Accessible Guided Exploration of healthcare information. SAGE is an information system that leverages Large Language Models (LLMs) to help patients identify and fill gaps in their understanding through automated organization of healthcare information, generation of guiding questions, and retrieval of reliable and accurate answers to patient queries. While LLMs may be a powerful intervention for these tasks, they pose risks and lack reliability in such high-stakes settings. One approach to address these limitations is to augment LLMs with Knowledge Graphs (KGs) containing well-structured and pre-verified health information. Thus, SAGE demonstrates how LLMs and KGs can complement each other to aid in the construction and retrieval of structured knowledge. By integrating the flexibility and natural language capabilities of LLMs with the reliability of KGs, SAGE seeks to create a collaborative system that promotes knowledge discovery for informed decision-making and effective self-management of chronic conditions.

Submission Number: 12