Promoting patient health literacy in burn care through artificial intelligence language learning models: A study of text analysis and simplification

Abstract

Health literacy is essential in patient care, especially in burn treatment, where understanding care information can significantly influence recovery outcomes. Despite national guidelines recommending that patient education materials be written at a 6th- to 8th-grade reading level, many resources exceed this complexity, exacerbating poor health literacy and patient outcomes. This study investigates the effectiveness of artificial intelligence language learning models in simplifying patient-facing burn care information to adhere to these readability standards. Fifteen excerpts from academic institutions' burn care materials were evaluated for readability using a traditional readability calculator and four AI models: ChatGPT 4o, Microsoft Copilot, Gemini, and Meta AI. The traditional readability calculator provided a baseline score, which was compared to the scores from the AI models. Results indicated that ChatGPT 4o and Microsoft Copilot had readability scores that were comparable to the readability scores provided by the traditional calculator. Additionally, when tasked with simplifying the texts, Microsoft Copilot, Gemini, and Meta AI reduced the readability scores to within the desired 6th to 8th-grade level. These findings suggest that AI models, particularly Microsoft Copilot, Gemini, and Meta AI, can effectively simplify medical texts, making them more accessible to patients. However, clinician oversight is necessary to ensure the accuracy and appropriateness of the simplified materials, promoting better health literacy and patient outcomes in burn care.