Effective health communication is often hindered by a "vocabulary gap" between language familiar to consumers and jargon used in medical practice and research. To present health information to consumers in a comprehensible fashion, we need to develop a mechanism to quantify health terms as being more likely or less likely to be understood by typical members of the lay public. Prior research has used approaches including syllable count, easy word list, and frequency count, all of which have significant limitations. In this article, we present a new method that predicts consumer familiarity using contextual information. The method was applied to a large query log data set and validated using results from two previously conducted consumer surveys. We measured the correlation between the survey result and the context-based prediction, syllable count, frequency count, and log normalized frequency count. The correlation coefficient between the context-based prediction and the survey result was 0.773 (p Ͻ 0.001), which was higher than the correlation coefficients between the survey result and the syllable count, frequency count, and log normalized frequency count (p Յ 0.012). The context-based approach provides a good alternative to the existing term familiarity assessment methods.  Research on consumer health vocabularies (CHV) is motivated by the increasing development, availability, and utilization of online health applications intended for laypersons. 1 Effective health information retrieval from consumer health applications, including the understanding of that information by laypersons, is often hindered by a "vocabulary gap" between language familiar to consumers and jargon used in medical practice and research. Thus, CHV research aims to bridge this lay-professional communication gap by identifying expressions and concepts actually used by laypersons (i.e., consumer-friendly health terms) and mapping them to domain-specific technical terms and concepts. Such mappings would allow computers to understand the vocabulary used by consumers and present health information in a consumer-friendly manner. It is necessary to develop a measure of consumer-friendliness or familiarity that estimates the likelihood of a term being understood by members of the lay public. Researchers in fields such as readability, literacy, and linguistics have found that, in the general domain, words with fewer letters and syllables tend to be more readable and comprehensible. 2 Previous readability studies 3 have included extensive lists of words familiar to grade-school children. In the area of health literacy, researchers have also identified types of terms that are difficult to understand and proposed short lists of alternative terms. 4, 5 Nevertheless, assessing and estimating lay familiarity with health-domain terms and concepts remains a challenge. For example, terms with the same number of syllables and/or letters (e.g., aspirin, Anbesol, and aplisol) vary greatly in difficulty; existing lists of "easy words" lack health terms; and existing lists of alternative health terms are not comprehensive. What is required, then, is a systematic and dynamic approach to identify lay expressions, map them to corresponding technical terms and concepts, and predict familiarity or user friendliness for the intended lay audience. Our first attempt at predicting the lay familiarity or consumer friendliness of health terminology quantitatively used term and word frequencies from several health-related text corpora. 6 Two exploratory studies suggested that this approach has some validity. 7, 8 However, these studies also indicated that not all variability in observed term difficulty could be explained by usage frequency. Therefore, we devised a new method that estimates term difficulty based on context, modeled on prior research that successfully exploited the contextual usage of words for indexing and retrieval. 9,10
REF improve on the previous methods using contextual information.
Estimating Consumer Familiarity with Health Terminology: A Context-based Approach
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine