Limited health literacy is a barrier to understanding health information. Simplifying text can reduce this barrier and possibly other known disparities in health. Unfortunately, few tools exist to simplify text with demonstrated impact on comprehension. By leveraging modern data sources integrated with natural language processing algorithms, we are developing the first semi-automated text simplification tool. We present two main contributions. First, we introduce our evidence-based development strategy for designing effective text simplification software and summarize initial, promising results. Second, we present a new study examining existing readability formulas, which are the most commonly used tools for text simplification in healthcare. We compare syllable count, the proxy for word difficulty used by most readability formulas, with our new metric ‘term familiarity’ and find 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621254 |
| Date | January 2016 |
| Creators | Kauchak, David, Leroy, Gondy |
| Contributors | Univ Arizona, Eller Coll Management, Management Informat Syst |
| Publisher | IEEE COMPUTER SOC |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2016 IEEE |
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