Assessing the Quality and Readability of Online Patient Information: ENT UK Patient Information e-Leaflets versus Responses by a Generative Artificial Intelligence

Abstract

BACKGROUND: The evolution of artificial intelligence has introduced new ways to disseminate health information, including natural language processing models like ChatGPT. However, the quality and readability of such digitally generated information remains understudied. This study is the first to compare the quality and readability of digitally generated health information against leaflets produced by professionals. METHODOLOGY: Patient information leaflets from five ENT UK leaflets and their corresponding ChatGPT responses were extracted from the Internet. Assessors with various degrees of medical knowledge evaluated the content using the Ensuring Quality Information for Patients (EQIP) tool and readability tools including the Flesch-Kincaid Grade Level (FKGL). Statistical analysis was performed to identify differences between leaflets, assessors, and sources of information. RESULTS: ENT UK leaflets were of moderate quality, scoring a median EQIP of 23. Statistically significant differences in overall EQIP score were identified between ENT UK leaflets, but ChatGPT responses were of uniform quality. Nonspecialist doctors rated the highest EQIP scores, while medical students scored the lowest. The mean readability of ENT UK leaflets was higher than ChatGPT responses. The information metrics of ENT UK leaflets were moderate and varied between topics. Equivalent ChatGPT information provided comparable content quality, but with reduced readability. CONCLUSION: ChatGPT patient information and professionally produced leaflets had comparable content, but large language model content required a higher reading age. With the increasing use of online health resources, this study highlights the need for a balanced approach that considers both the quality and readability of patient education materials.

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