ArticleIT professional
Moving Beyond Readability Metrics for Health-Related Text Simplification.
Article in IT professional. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines 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
- Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review.JCO clinical cancer informatics · 2018Pooled it
- Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models.European radiology · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Readability Analysis of Commonly Cited Patient-Reported Outcome Measures in the Orthopaedic Joint Literature.Arthroplasty today · 2026Article
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Functional endoscopic sinus surgery: assessing the readability and quality of online information.Annals of the Royal College of Surgeons of England · 2023Article
- 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
- Are Mobile Applications in Laryngology Designed for All Patients?The Laryngoscope · 2023Review
- A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Evaluation of an online text simplification editor using manual and automated metrics for perceived and actual text difficulty.JAMIA open · 2022Article
- An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.Journal of medical Internet research · 2020Article
- What Happened to Me while I Was in the Hospital? Challenges and Opportunities for Generating Patient-Friendly Hospitalization Summaries.Journal of healthcare informatics research · 2019Article
- Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.Yearbook of medical informatics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Limited health literacy is a barrier to understanding health information. Simplifying text can reduce this barrier and possibly other known disparities in health. Unfortunately, few tools exist to simplify text with demonstrated impact on comprehension. By leveraging modern data sources integrated with natural language processing algorithms, we are developing the first semi-automated text simplification tool. We present two main contributions. First, we introduce our evidence-based development strategy for designing effective text simplification software and summarize initial, promising results. Second, we present a new study examining existing readability formulas, which are the most commonly used tools for text simplification in healthcare. We compare syllable count, the proxy for word difficulty used by most readability formulas, with our new metric 'term familiarity' and find that syllable count measures how difficult words 'appear' to be, but not their actual difficulty. In contrast, term familiarity can be used to measure actual difficulty.
Indexed as
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What OpenQuestion holds
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.