2018
DOI: 10.2196/jmir.8669
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A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

Abstract: BackgroundMany health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes.ObjectiveThe aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by… Show more

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Cited by 43 publications
(27 citation statements)
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References 42 publications
(48 reference statements)
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“…The vocabulary was also evaluated for quality, coverage, and usefulness of the simplification, for which the proposed resource outperforms all existing written resources. In another work, researchers propose to link medical terms to lay definitions 17 . The proposed system NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions.…”
Section: Resultsmentioning
confidence: 99%
“…The vocabulary was also evaluated for quality, coverage, and usefulness of the simplification, for which the proposed resource outperforms all existing written resources. In another work, researchers propose to link medical terms to lay definitions 17 . The proposed system NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions.…”
Section: Resultsmentioning
confidence: 99%
“…Voice control was part of the initial design plan for the ALS-SimVR due to an indication from other health care settings [ 46 ]; however, implementing it is not without significant challenges. In addition to the computational and algorithmic challenges, there is the issue of a hugely diverse cohort of individuals, all of whom often use different language to convey the same information using expressions drawn from a diverse and specific lexicon [ 47 ].…”
Section: Discussionmentioning
confidence: 99%
“…Text-to-speech engines are promising and can support patient with reading difficulties [27]. Developers can be supported by automated algorithms that link medical jargon to lay language [55].…”
Section: Discussionmentioning
confidence: 99%