Moving beyond readability metrics for health-related text simplification

Limited health literacy is a barrier to understanding health information. Simplifying text can reduce this barrier and possibly address other known health disparities. Unfortunately, few tools exist to simplify text with a demonstrated impact on comprehension. By leveraging modern data sources integrated with natural language processing algorithms, the authors have developed a semi-automated text-simplification tool. They introduce their evidence-based development strategy for designing effective text-simplification software and summarize initial, promising results. They also present a new study examining existing readability formulas, which are the most commonly used tools for text simplification in healthcare. They compare syllable count - the proxy for word difficulty used by most readability formulas - with their new metric, term familiarity, and determine that syllable count measures how difficult words appear to be, but not their actual difficulty. In contrast, term familiarity can be used to measure actual difficulty.