Advancing Patient Education in Idiopathic Intracranial Hypertension

Abstract

Background and Objectives: We evaluated the performance of 3 large language models (LLMs) in generating patient education materials (PEMs) and enhancing the readability of prewritten PEMs on idiopathic intracranial hypertension (IIH). Methods: ]." We compared generated and rewritten PEMs, along with the first 20 googled eligible PEMs on IIH, on readability (Simple Measure of Gobbledygook [SMOG] and Flesch-Kincaid Grade Level [FKGL]), quality (DISCERN and Patient Education Materials Assessment tool [PEMAT]), and accuracy (Likert misinformation scale). Results: < 0.001, DISCERN ≥4, Likert misinformation = 1). Discussion: In conclusion, LLMs, particularly ChatGPT-4, can produce high-quality, readable PEMs on IIH. They can also serve as supplementary tools to improve the readability of prewritten PEMs while maintaining quality and accuracy.

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