title: A classification of errors in lay comprehension of medical documents.
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.
authors.affiliation: Division of Specialized Information Services, National Library of Medicine, Bethesda, MD 20892-5467, USA. keselmana@mail.nih.gov
authors.forename: Alla
authors.initials: A
authors.lastname: Keselman
authors.forename: Catherine Arnott
authors.initials: CA
authors.lastname: Smith
mesh_terms: D000328:Adult; D000368:Aged; D032882:Comprehension; D005260:Female; D055991:Health Records, Personal; D006801:Humans; D008297:Male; D019300:Medical Errors / Q000517:prevention & control* / Q000706:statistics & numerical data; D008875:Middle Aged; D009819:Office Visits