ArticleJournal of medical Internet research2017
Text Simplification Using Consumer Health Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Psychological Impact of AI-Simplified Brain MRI Reports: A Randomized Trial of Patient Understanding, Anxiety, and Health Literacy.Journal of clinical medicine · 2026Article
- Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.JMIR formative research · 2025Article
- Article
- Generating colloquial radiology reports with large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Exploring therapeutic communication in managing chronic non-communicable diseases: a mixed-method study in Ghana.Archives of public health = Archives belges de sante publique · 2024Article
- Retrieval augmentation of large language models for lay language generation.Journal of biomedical informatics · 2024Article
- Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.Journal of digital imaging · 2023Article
- MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2022Article
- A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Communicating with patients in the age of online portals-challenges and opportunities on the horizon for radiologists.Insights into imaging · 2022Article
- Full Radiology Report through Patient Web Portal: A Literature Review.International journal of environmental research and public health · 2020Review
- Factors Influencing Motivation and Engagement in Mobile Health Among Patients With Sickle Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements.JMIR human factors · 2020Article
- Patient-centered web-based information on oral lichen planus: Quality and readability.Medicina oral, patologia oral y cirugia bucal · 2019Article
- Mining and standardizing chinese consumer health terms.BMC medical informatics and decision making · 2018Article
- 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
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports for patients. The open-access, collaborative consumer health vocabulary (CHV) is a terminology system created for such purposes and can be the basis of lexical simplification processes for clinical notes.
objectiveThe aim of this study was to examine the comprehensibility and suitability of CHV in simplifying radiology reports for consumers. This was done by characterizing the content coverage and the lexical similarity between the terms in the reports and the CHV-preferred terms.
methodsThe overall procedure was divided into the following two main stages: (1) translation and (2) evaluation. The translation process involved using MetaMap to link terms in the reports to CHV concepts. This is followed by replacing the terms with CHV-preferred terms using the concept names and sources table (MRCONSO) in the Unified Medical Language System (UMLS) Metathesaurus. In the second stage, medical terms in the reports and general terms that are used to describe medical phenomena were selected and evaluated by comparing the words in the original reports with the translated ones. The evaluation includes measuring the content coverage, investigating lexical similarity, and finding trends in missing concepts.
resultsOf the 792 terms selected from the radiology reports, 695 of them could be mapped directly to CHV concepts, indicating a content coverage of 88.5%. A total of 51 of the concepts (53%, 51/97) that could not be mapped are names of human anatomical structures and regions, followed by 28 anatomical descriptions and pathological variations (29%, 28/97). In addition, 12 radiology techniques and projections represented 12% of the unmapped concepts, whereas the remaining six concepts (6%, 12/97) were physiological descriptions. The rate of lexical similarity between the CHV-preferred terms and the terms in the radiology reports was approximately 72.6%.
conclusionsThe CHV covered a high percentage of concepts found in the radiology reports, but unmapped concepts are associated with areas that are commonly found in radiology reporting. CHV terms also showed a high percentage of lexical similarity with terms in the reports, which contain a myriad of medical jargon. This suggests that many CHV terms might not be suitable for lay consumers who would not be facile with radiology-specific vocabulary. Therefore, further patient-centered content changes are needed of the CHV to increase its usefulness and facilitate its integration into consumer-oriented applications.
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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.