Using Large Language Models to Address Health Literacy in mHealth

Abstract

The innate complexity of medical topics often makes it challenging to produce educational content for the public. Although there are resources available to help authors appraise the complexity of their content, there are woefully few resources available to help authors reduce that complexity after it occurs. In this case study, we evaluate using ChatGPT to reduce the complex language used in health-related educational materials. ChatGPT adapted content from the SmartSHOTS mobile application, which is geared toward caregivers of children aged 0 to 24 months. SmartSHOTS helps reduce barriers and improve adherence to vaccination schedules. ChatGPT reduced complex sentence structure and rewrote content to align with a third-grade reading level. Furthermore, using ChatGPT to edit content already written removes the potential for unnoticed, artificial intelligence-produced inaccuracies. As an editorial tool, ChatGPT was effective, efficient, and free to use. This article discusses the potential of ChatGPT as an effective, time-efficient, and open-source method for editing health-related educational materials to reflect a comprehendible reading level.

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