title: Improving perceived and actual text difficulty for health information consumers using semi-automated methods.
abstract: We are developing algorithms for semi-automated simplification of medical text. Based on lexical and grammatical corpus analysis, we identified a new metric, term familiarity, to help estimate text difficulty. We developed an algorithm that uses term familiarity to identify difficult text and select easier alternatives from lexical resources such as WordNet, UMLS and Wiktionary. Twelve sentences were simplified to measure perceived difficulty using a 5-point Likert scale. Two documents were simplified to measure actual difficulty by posing questions with and without the text present (information understanding and retention). We conducted a user study by inviting participants (N=84) via Amazon Mechanical Turk. There was a significant effect of simplification on perceived difficulty (p<.001). We also saw slightly improved understanding with better question-answering for simplified documents but the effect was not significant (p=.097). Our results show how term familiarity is a valuable component in simplifying text in an efficient and scalable manner.
authors.affiliation: Claremont Graduate University, Claremont, CA, USA.
authors.forename: Gondy
authors.initials: G
authors.lastname: Leroy
authors.forename: James E
authors.initials: JE
authors.lastname: Endicott
authors.forename: Obay
authors.initials: O
authors.lastname: Mouradi
authors.forename: David
authors.initials: D
authors.lastname: Kauchak
authors.forename: Melissa L
authors.initials: ML
authors.lastname: Just
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D032882:Comprehension; D054626:Consumer Health Information; D004522:Educational Status; D005260:Female; D057220:Health Literacy; D006801:Humans; D008297:Male; D010353:Patient Education as Topic; D017432:Unified Medical Language System