Enhancing the Readability of Online Patient Education Materials Using Large Language Models: Cross-Sectional Study

作者：John J. Will, Mahin Gupta, Jonah Zaretsky, Aliesha Dowlath, Paul A Testa, Jonah Feldman · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/69955 · 被引用次数：97 · 研究领域：Health Literacy and Information Accessibility、Artificial Intelligence in Healthcare and Education、Text Readability and Simplification

BACKGROUND: Online accessible patient education materials (PEMs) are essential for patient empowerment. However, studies have shown that these materials often exceed the recommended sixth-grade reading level, making them difficult for many patients to understand. Large language models (LLMs) have the potential to simplify PEMs into more readable educational content. OBJECTIVE: We sought to evaluate whether 3 LLMs (ChatGPT [OpenAI], Gemini [Google], and Claude [Anthropic PBC]) can optimize the readability of PEMs to the recommended reading level without compromising accuracy. METHODS: This cross-sectional study used 60 randomly selected PEMs available online from 3 websites. We prompted LLMs to simplify the reading level of online PEMs. The primary outcome was the readability of the original online PEMs compared with the LLM-simplified versions. Readability scores were calculated using 4 validated indices Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook Index. Accuracy and understandability were also assessed as balancing measures, with understandability measured using the Patient Education Materials Assessment Tool-Understandability (PEMAT-U). RESULTS: The original readability scores for the American Heart Association (AHA), American Cancer Society (ACS), and American Stroke Association (ASA) websites were above the recommended sixth-grade level, with mean grade level scores of 10.7,10.0, and 9.6, respectively. After optim...