LLMs simplify diabetes information for better patient understanding
Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes
Artificial Intelligence
Summary
Medical information can be really hard for patients to understand, which makes it tougher for them to make good health choices. The authors created a system called MediClear that uses large language models to rewrite complicated diabetes info into simpler language. They tested it with real patients and found it made the info easier to read and users liked it. This shows LLMs could help make medical knowledge easier for everyone to understand.
What this means in practice
- •For patient education teams: Provide diabetes patients with easier-to-understand explanations by integrating MediClear’s LLM-based simplification system into educational materials.
- •For health content developers: Use MediClear’s retrieval and simplification methods to create accessible health articles and resources for distribution by healthcare organizations.
- •For customer support in health tech: Enhance chatbot or virtual assistant responses with MediClear’s simplified medical information for better patient communication.$Commercial implications: Enables health tech companies to sell improved patient support chatbots that deliver clear medical explanations adapted to user literacy levels.
Authors
Pallika Kafle, Yipeng Zhou, Guanfeng Liu, Quan Z. Sheng, Cheng-Hsin Hsu
Abstract
Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving patient comprehension, informed decision-making, and health outcomes. Recent advances in large language models (LLMs) provide a promising approach for simplifying complex medical information into patient-friendly language; however, their effectiveness in real-world patient education remains insufficiently explored through human evaluation. To investigate their practical effectiveness, this paper presents a case study on diabetes knowledge simplification through the implementation and evaluation of MediClear, an LLM-based medical knowledge simplification system enhanced with Retrieval-Augmented Generation (RAG). Public diabetes-related articles from Diabetes Australia, WHO, American Diabetes Association (ADA), NIDDK, and AIHW are indexed in the RAG knowledge base to retrieve clinically grounded information, which is then simplified by the LLM into accessible patient explanations. We evaluate the generated responses using standard readability metrics, including the Flesch-Kincaid Grade Level (FKGL), and conduct a human study involving 10 participants. Results show that MediClear consistently reduces the reading level of generated responses to the recommended patient literacy range while achieving high user satisfaction and willingness for future use. This case study demonstrates the potential of LLMs to improve the accessibility of medical knowledge for patient education.