The role of large language models in improving the readability of orthopaedic spine patient educational material.

introductionPatient education is crucial for informed decision-making. Current educational materials are often written at a higher grade level than the American Medical Association (AMA)-recommended sixth-grade level. Few studies have assessed the readability of orthopaedic materials such as American Academy of Orthopaedic Surgeons (AAOS) OrthoInfo articles, and no studies have suggested efficient methods to improve readability. This study assessed the readability of OrthoInfo spine articles and investigated the ability of large language models (LLMs) to improve readability.

methodsA cross-sectional study analyzed 19 OrthoInfo articles using validated readability metrics (Flesch-Kincaid Grade Level and Reading Ease). Articles were simplified iteratively in three steps using ChatGPT, Gemini, and CoPilot. LLMs were prompted to summarize text, followed by two clarification prompts simulating patient inquiries. Word count, readability, and accuracy were assessed at each step. Accuracy was rated by two independent reviewers using a three-point scale (3 = fully accurate, 2 = minor inaccuracies, 1 = major inaccuracies). Statistical analysis included one-way and two-way ANOVA, followed by Tukey post-hoc tests for pairwise comparisons.

resultsBaseline readability exceeded AMA recommendations, with a mean Flesch-Kincaid Grade Level of 9.5 and a Reading Ease score of 51.1. LLM summaries provided statistically significant improvement in readability, with the greatest improvements in the first iteration. All three LLMs performed similarly, though ChatGPT achieved statistically significant improvements in Reading Ease scores. Gemini incorporated appropriate disclaimers most consistently. Accuracy remained stable throughout, with no evidence of hallucination or compromise in content quality or medical relevance. DISCUSSION: LLMs effectively simplify orthopaedic educational content by reducing grade levels, enhancing readability, and maintaining acceptable accuracy. Readability improvements were most significant in initial simplification steps, with all models performing consistently. These findings support the integration of LLMs into patient education workflows, offering a scalable strategy to improve health literacy, enhance patient comprehension, and promote more equitable access to medical information across diverse populations.