Translating Electronic Health Record Notes from English to Spanish: A Preliminary Study

Abstract

The Centers for Medicare & Medicaid Services Incentive Programs promote meaningful use of electronic health records (EHRs), which, among many benefits, allow patients to receive electronic copies of their EHRs and thereby empower them to take a more active role in their health. In the United States, however, 17% population is Hispanic, of which 50% has limited English language skills. To help this population take advantage of their EHRs, we are developing English-Spanish machine translation (MT) systems for EHRs. In this study, we first built an English-Spanish parallel corpus and trained NoteAid Spanish , a statistical MT (SMT) system. Google Translator and Microsoft Bing Translator are two baseline MT systems. In addition, we evaluated hybrid MT systems that first replace medical jargon in EHR notes with lay terms and then translate the notes with SMT systems. Evaluation on a small set of EHR notes, our results show that Google Translator outperformed NoteAid Spanish . The hybrid SMT systems first map medical jargon to lay language. This step improved the translation. A fully implemented hybrid MT system is available at http://www.clinicalnotesaid.org. The English-Spanish parallel-aligned MedlinePlus corpus is available upon request.

Clinical Natural Language Processing in languages other than English: opportunities and challenges

“…However, it was shown to be of little help to render medical record content more comprehensible to patients [ 155 ]. A systematic evaluation of machine translation tools showed that off-the-shelf tools were outperformed by customized systems [ 156 ]; however, this was not confirmed when using a smaller in-domain corpus [ 157 ]. Encouragingly, medical speech translation was shown to be feasible in a real clinical setting, if the system focused on narrowly-defined patient-clinician interactions [ 158 ].…”

A Paraphrase Generation System for EHR Question Answering

“…development dataset for natural language understanding task without evaluating the quality of the paraphrases. Such general-purpose machine translation systems lack the ability to capture the domain-specific nuances of biomedicine (Liu and Cai, 2015). This suggests the need for a question paraphrasing dataset targeted toward clinical domain.…”

“…Also, existing datasets for clinical paraphrasing consist of either short phrases (Hasan et al, 2016) or webpage title texts (Adduru et al, 2018), both of which are not suitable to build a paraphrase generator for QA. One can resort to using external tools such as Google Translate for generating question paraphrases (Neuraz et al, 2018), but these general-purpose tools are not tailored to the medical domain (Liu and Cai, 2015).…”

Is machine language translation a viable tool for health communication?

“…Evaluation of NoteAidspa 11 ;s1, W. Liu and Cai (2015) used BLEU score and a bilingual human expert to evaluate three systems: NoteAidspanish, Google Translate, and Bing Translator when translating electronic health records. BLEU is a method for automatic evaluation of machine translation quality (Papineni, Roukos, Ward, & Zhu, 2002).…”