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First published February 28, 2008 as JAMIA PrePrint; doi:10.1197/jamia.M2592
Journal of the American Medical Informatics Association 2008;15(3):349-356
© 2008 American Medical Informatics Association

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Submitted on August 14, 2007
Accepted on February 8, 2008

Estimating Consumer Familiarity with Health Terminology: A Context-Based Approach

Qing Zeng-Treitler PhD1*, Sergey Goryachev MS1, Tony Tse PhD2, Alla Keselman3, and Aziz Boxwala MD, PhD4

Affiliation of the authors: 1 DSG, Brigham and Women's Hospital, Harvard Medical School, Boston, MA ; 2 LHNCBC, National Library of Medicine, NIH, DHHS, Bethesda, MD; 3 LHNCBC, National Library of Medicine, NIH, DHHS, Bethesda, MD; Aquilent, Inc., Laurel, MD ; 4 DSG, Brigham and Women's Hospital, Harvard Medical School, Boston, MA

* To whom correspondence should be addressed.

Objective Effective health communication is often hindered by a "vocabulary gap" between language familiar to consumers and jargon used in medical practice and research. In order 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 employed approaches including syllable count, easy word list, and frequency count, all of which have significant limitations.

Design In this paper, we present a new method which 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.

Measurements We measured the correlation between the survey result and the context-based prediction, syllable count, frequency count, and log normalized frequency count.

Results The correlation coefficient between the context-based prediction and the survey result was 0.773 (p<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≤0.012).

Conclusion The context-based approach provides a good alternative to the existing term familiarity assessment methods.






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