Development of Methods to Assess Readability in Health and Medical Information: A Scoping Review

This study aimed to extract characteristics of studies involving development of readability measures for health and medical information (HMI) to explore the common themes (strategies) within such developments. Four databases were searched following PRISMA guidelines. The data analysis included statistical summary, thematic analysis, and idea webbing. A total of 1,129 articles were identified in the initial search, resulting in 14 articles included. More than half of the articles (n = 8) were published between 2006 and 2015, with declining recent developments. Four main themes of development strategies were revealed: Language Features (n = 10), Machine Learning (ML, n = 6), Natural Language Processing (NLP, n = 5), and Human Annotations (n = 5). ML and NLP techniques were popularly used and oftentimes tested by professional and layperson judgement. However, despite these recent developments, none have been widely adopted. With further advancement of Artificial Intelligence and Large Language Models, opportunities exist to develop usable and reliable readability measures for HMI.