ArticleJournal of medical Internet research2013
User evaluation of the effects of a text simplification algorithm using term familiarity on perception, understanding, learning, and information retention.
Article in Journal of medical Internet research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges and Opportunities for Professional Medical Publications Writers to Contribute to Plain Language Summaries (PLS) in an AI/ML Environment - A Consumer Health Informatics Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Pooled it
- A multi-metric evaluation of readability in psychiatric discharge summaries.BioData mining · 2026Article
- Parallel Corpus Analysis of Text and Audio Comprehension to Evaluate Readability Formula Effectiveness: Quantitative Analysis.Journal of medical Internet research · 2025Article
- Text and Audio Simplification: Human vs. ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations.Journal of medical Internet research · 2023Article
- A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Article
- An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.Journal of medical Internet research · 2020Article
- Crowdsourcing for Food Purchase Receipt Annotation via Amazon Mechanical Turk: A Feasibility Study.Journal of medical Internet research · 2019Article
- Predicting Transition Words Between Sentence for English and Spanish Medical Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish.Journal of medical Internet research · 2018Article
- Readability of written medicine information materials in Arabic language: expert and consumer evaluation.BMC health services research · 2018Article
- Measuring Text Difficulty Using Parse-Tree Frequency.Journal of the Association for Information Science and Technology · 2017Article
- NegAIT: A new parser for medical text simplification using morphological, sentential and double negation.Journal of biomedical informatics · 2017Article
- The Role of Surface, Semantic and Grammatical Features on Simplification of Spanish Medical Texts: A User Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Dimensions of Deaf/Hard-of-Hearing and Hearing Adolescents' Health Literacy and Health Knowledge.Journal of health communication · 2016Article
- Effects on Text Simplification: Evaluation of Splitting Up Noun Phrases.Journal of health communication · 2016Review
- Assessing Pictograph Recognition: A Comparison of Crowdsourcing and Traditional Survey Approaches.Journal of medical Internet research · 2015Article
- Effects of individual health topic familiarity on activity patterns during health information searches.JMIR medical informatics · 2015Article
- Patient perspectives on online health information and communication with doctors: a qualitative study of patients 50 years old and over.Journal of medical Internet research · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAdequate health literacy is important for people to maintain good health and manage diseases and injuries. Educational text, either retrieved from the Internet or provided by a doctor's office, is a popular method to communicate health-related information. Unfortunately, it is difficult to write text that is easy to understand, and existing approaches, mostly the application of readability formulas, have not convincingly been shown to reduce the difficulty of text.
objectiveTo develop an evidence-based writer support tool to improve perceived and actual text difficulty. To this end, we are developing and testing algorithms that automatically identify difficult sections in text and provide appropriate, easier alternatives; algorithms that effectively reduce text difficulty will be included in the support tool. This work describes the user evaluation with an independent writer of an automated simplification algorithm using term familiarity.
methodsTerm familiarity indicates how easy words are for readers and is estimated using term frequencies in the Google Web Corpus. Unfamiliar words are algorithmically identified and tagged for potential replacement. Easier alternatives consisting of synonyms, hypernyms, definitions, and semantic types are extracted from WordNet, the Unified Medical Language System (UMLS), and Wiktionary and ranked for a writer to choose from to simplify the text. We conducted a controlled user study with a representative writer who used our simplification algorithm to simplify texts. We tested the impact with representative consumers. The key independent variable of our study is lexical simplification, and we measured its effect on both perceived and actual text difficulty. Participants were recruited from Amazon's Mechanical Turk website. Perceived difficulty was measured with 1 metric, a 5-point Likert scale. Actual difficulty was measured with 3 metrics: 5 multiple-choice questions alongside each text to measure understanding, 7 multiple-choice questions without the text for learning, and 2 free recall questions for information retention.
resultsNinety-nine participants completed the study. We found strong beneficial effects on both perceived and actual difficulty. After simplification, the text was perceived as simpler (P<.001) with simplified text scoring 2.3 and original text 3.2 on the 5-point Likert scale (score 1: easiest). It also led to better understanding of the text (P<.001) with 11% more correct answers with simplified text (63% correct) compared to the original (52% correct). There was more learning with 18% more correct answers after reading simplified text compared to 9% more correct answers after reading the original text (P=.003). There was no significant effect on free recall.
conclusionsTerm familiarity is a valuable feature in simplifying text. Although the topic of the text influences the effect size, the results were convincing and consistent.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.