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.

中文翻译：

---

超越可读性指标以简化与健康相关的文本

有限的健康素养是理解健康信息的障碍。简化文本可以减少这一障碍，并可能解决其他已知的健康差异。不幸的是，很少有工具可以简化文本并对理解产生明显影响。通过利用与自然语言处理算法集成的现代数据源，作者开发了一种半自动文本简化工具。他们介绍了他们设计有效的文本简化软件的基于证据的开发策略，并总结了最初的、有希望的结果。他们还提出了一项新的研究，检查现有的可读性公式，这些公式是医疗保健中最常用的文本简化工具。他们将音节计数——大多数可读性公式使用的单词难度代理——与他们的新指标、术语熟悉度、并确定音节计数衡量的是单词看起来有多难，而不是它们的实际难度。相比之下，术语熟悉度可用于衡量实际难度。