ArticleJournal of medical Internet research2017
Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Large Language Model Simplification of Open Access Pediatric Strabismus Literature: Cross-Sectional Validation of Readability and Clinical Fidelity.JMIR formative research · 2026Article
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- Navigating Home Care Packages: A Content and Usability Analysis of Consumer-Facing Information on Aged Care Provider Websites.Australasian journal on ageing · 2026Article
- Automated Approaches of Text Simplification of Patient Education Materials: Scoping Review.Journal of medical Internet research · 2026Article
- Comparing the readability of online patient education materials for LASIK and cataract surgery.International ophthalmology · 2026Article
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Assessing the Efficacy of ChatGPT Prompting Strategies in Enhancing Thyroid Cancer Patient Education: A Prospective Study.Journal of medical systems · 2025Article
- Jargon and Readability in Plain Language Summaries of Health Research: Cross-Sectional Observational Study.Journal of medical Internet research · 2025Observational
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- Improving the Quality and Utility of Electronic Health Record Data through Ontologies.Standards (Basel, Switzerland) · 2023Article
- Article
- Online Health Information for Penile Prosthesis Implants Lacks Quality and Is Unreadable to the Average US Patient.Cureus · 2023Article
- Evaluation of an online text simplification editor using manual and automated metrics for perceived and actual text difficulty.JAMIA open · 2022Article
- Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study.JMIR medical informatics · 2021Article
- An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study.Journal of medical Internet research · 2020Article
- Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study.Journal of communication in healthcare · 2020Article
- Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms.Journal of medical Internet research · 2019Article
- ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.Journal of medical Internet research · 2018Article
- Assessing the Readability of Medical Documents: A Ranking Approach.JMIR medical informatics · 2018Article
- Integrating Patient-Centered Electronic Health Record Communication Training into Resident Onboarding: Curriculum Development and Post-Implementation Survey Among Housestaff.JMIR medical education · 2018Article
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2 authors.
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Abstract
backgroundElectronic health records (EHRs) are a rich resource for developing applications to engage patients and foster patient activation, thus holding a strong potential to enhance patient-centered care. Studies have shown that providing patients with access to their own EHR notes may improve the understanding of their own clinical conditions and treatments, leading to improved health care outcomes. However, the highly technical language in EHR notes impedes patients' comprehension. Numerous studies have evaluated the difficulty of health-related text using readability formulas such as Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning-Fog Index (GFI). They conclude that the materials are often written at a grade level higher than common recommendations.
objectiveThe objective of our study was to explore the relationship between the aforementioned readability formulas and the laypeople's perceived difficulty on 2 genres of text: general health information and EHR notes. We also validated the formulas' appropriateness and generalizability on predicting difficulty levels of highly complex technical documents.
methodsWe collected 140 Wikipedia articles on diabetes and 242 EHR notes with diabetes International Classification of Diseases, Ninth Revision code. We recruited 15 Amazon Mechanical Turk (AMT) users to rate difficulty levels of the documents. Correlations between laypeople's perceived difficulty levels and readability formula scores were measured, and their difference was tested. We also compared word usage and the impact of medical concepts of the 2 genres of text.
resultsThe distributions of both readability formulas' scores (P<.001) and laypeople's perceptions (P=.002) on the 2 genres were different. Correlations of readability predictions and laypeople's perceptions were weak. Furthermore, despite being graded at similar levels, documents of different genres were still perceived with different difficulty (P<.001). Word usage in the 2 related genres still differed significantly (P<.001).
conclusionsOur findings suggested that the readability formulas' predictions did not align with perceived difficulty in either text genre. The widely used readability formulas were highly correlated with each other but did not show adequate correlation with readers' perceived difficulty. Therefore, they were not appropriate to assess the readability of EHR notes.
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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.