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Improving perceived and actual text difficulty for health information consumers using semi-automated methods
Gondy Leroy1, James E Endicott, Obay Mouradi
1Claremont Graduate University, Claremont, CA, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 11, 2013
Summary
We developed a new metric, term familiarity, to simplify medical text. This approach significantly reduced perceived text difficulty, aiding comprehension and retention.
Area of Science:
- Natural Language Processing
- Medical Informatics
- Computational Linguistics
Background:
- Medical texts present comprehension challenges for many individuals.
- Automated methods for simplifying medical language are needed to improve accessibility.
Purpose of the Study:
- To develop and evaluate an algorithm for semi-automated simplification of medical text.
- To introduce and validate the 'term familiarity' metric for estimating text difficulty.
Main Methods:
- Lexical and grammatical corpus analysis to identify text difficulty.
- Algorithm development using term familiarity to find simpler word alternatives from resources like WordNet, UMLS, and Wiktionary.
- User study (N=84) assessing perceived difficulty (Likert scale) and actual understanding (question-answering) of simplified medical text.
Main Results:
- Simplification significantly reduced perceived text difficulty (p<.001).
- A trend towards improved information understanding and retention was observed in simplified documents, though not statistically significant (p=.097).
Conclusions:
- Term familiarity is a valuable metric for efficient and scalable medical text simplification.
- Semi-automated simplification shows promise in enhancing medical text accessibility and user comprehension.
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