ArticleJournal of biomedical informatics2012
A classification of errors in lay comprehension of medical documents.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
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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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Enhancing Patient Understanding of Laboratory Test Results: Systematic Review of Presentation Formats and Their Impact on Perception, Decision, Action, and Memory.Journal of medical Internet research · 2024Pooled it
- Interventions to increase patient portal use in vulnerable populations: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- A multi-metric evaluation of readability in psychiatric discharge summaries.BioData mining · 2026Article
- LLMonFHIR: A Physician-Validated, Large Language Model-Based Mobile Application for Querying Patient Electronic Health Data.JACC. Advances · 2025Article
- Health Information on Pre-Exposure Prophylaxis From Search Engines and Twitter: Readability Analysis.JMIR public health and surveillance · 2023Article
- Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal.Applied clinical informatics · 2023Article
- Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information.Journal of biomedical informatics · 2022Article
- User-Centered System Design for Communicating Clinical Laboratory Test Results: Design and Evaluation Study.JMIR human factors · 2021Article
- Towards more patient friendly clinical notes through language models and ontologies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Evaluation of lexical clarification by patients reading their clinical notes: a quasi-experimental interview study.BMC medical informatics and decision making · 2020Article
- Predicting Transition Words Between Sentence for English and Spanish Medical Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Assessing the Readability of Freely Available ICU Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- An unsupervised and customizable misspelling generator for mining noisy health-related text sources.Journal of biomedical informatics · 2018Article
- Methods for Patient-Centered Interface Design of Test Result Display in Online Portals.EGEMS (Washington, DC) · 2018Article
- A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews.Journal of medical Internet research · 2018Article
- Patient portals and health apps: Pitfalls, promises, and what one might learn from the other.Healthcare (Amsterdam, Netherlands) · 2017Article
- Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study.Journal of medical Internet research · 2017Article
- The Readability of Electronic Cigarette Health Information and Advice: A Quantitative Analysis of Web-Based Information.JMIR public health and surveillance · 2017Article
- Assessing the readability of ClinicalTrials.gov.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Effects on Text Simplification: Evaluation of Splitting Up Noun Phrases.Journal of health communication · 2016Review
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
Emphasis on participatory medicine requires that patients and consumers participate in tasks traditionally reserved for healthcare providers. This includes reading and comprehending medical documents, often but not necessarily in the context of interacting with Personal Health Records (PHRs). Research suggests that while giving patients access to medical documents has many benefits (e.g., improved patient-provider communication), lay people often have difficulty understanding medical information. Informatics can address the problem by developing tools that support comprehension; this requires in-depth understanding of the nature and causes of errors that lay people make when comprehending clinical documents. The objective of this study was to develop a classification scheme of comprehension errors, based on lay individuals' retellings of two documents containing clinical text: a description of a clinical trial and a typical office visit note. While not comprehensive, the scheme can serve as a foundation of further development of a taxonomy of patients' comprehension errors. Eighty participants, all healthy volunteers, read and retold two medical documents. A data-driven content analysis procedure was used to extract and classify retelling errors. The resulting hierarchical classification scheme contains nine categories and 23 subcategories. The most common error made by the participants involved incorrectly recalling brand names of medications. Other common errors included misunderstanding clinical concepts, misreporting the objective of a clinical research study and physician's findings during a patient's visit, and confusing and misspelling clinical terms. A combination of informatics support and health education is likely to improve the accuracy of lay comprehension of medical documents.
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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.