title: Can multilingual machine translation help make medical record content more comprehensible to patients?
abstract: With the development of electronic personal health records, more patients are gaining access to their own medical records. However, comprehension of medical record content remains difficult for many patients. Because each record is unique, it is also prohibitively costly to employ human translators to solve this problem. In this study, we investigated whether multilingual machine translation could help make medical record content more comprehensible to patients who lack proficiency in the language of the records. We used a popular general-purpose machine translation tool called Babel Fish to translate 213 medical record sentences from English into Spanish, Chinese, Russian and Korean. We evaluated the comprehensibility and accuracy of the translation. The text characteristics of the incorrectly translated sentences were also analyzed. In each language, the majority of the translations were incomprehensible (76% to 92%) and/or incorrect (77% to 89%). The main causes of the translation are vocabulary difficulty and syntactical complexity. A general-purpose machine translation tool like the Babel Fish is not adequate for the translation of medical records; however, a machine translation tool can potentially be improved significantly, if it is trained to target certain narrow domains in medicine.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA. q.t.zeng@utah.edu
authors.forename: Qing
authors.initials: Q
authors.lastname: Zeng-Treitler
authors.forename: Hyeoneui
authors.initials: H
authors.lastname: Kim
authors.forename: Graciela
authors.initials: G
authors.lastname: Rosemblat
authors.forename: Alla
authors.initials: A
authors.lastname: Keselman
mesh_terms: D054626:Consumer Health Information / Q000379:methods*; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D016347:Medical Records Systems, Computerized; D019303:Multilingualism; D009323:Natural Language Processing; D010353:Patient Education as Topic / Q000379:methods*; D014175:Translating; D014481:United States; D014584:User-Computer Interface; D018875:Vocabulary, Controlled