Estimating Consumer Familiarity with Health Terminology: A Context-based Approach
- aDecision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- bLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
- cAquilent, Inc., Laurel, MD
- Correspondence: Qing Zeng-Treitler, PhD, Decision Systems Group, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 900 Commonwealth Avenue, 3rd Floor, Boston, MA 02215 (e-mail: <qzeng{at}dsg.bwh.harvard.edu>)
- Received 14 August 2007
- Accepted 8 February 2008
Abstract
Objectives 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.
Design 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.
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).
Conclusions The context-based approach provides a good alternative to the existing term familiarity assessment methods.
Footnotes
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This work is supported by National Institutes of Health grants R01 LM07222 and R01 DK75837, and by the Intramural Research Program of the National Institutes of Health, National Library of Medicine.
