Interpretation Performance in Chinese and Japanese Medical Consultation Scenarios

Background： Language barriers in healthcare have a significant impact on patient safety, health outcomes, and the quality of healthcare services. With the increase in global migration, more patients are facing language challenges in foreign healthcare systems. Literature shows that providing professional interpretation services significantly improves patient satisfaction and communication quality. However, due to the high usage of ad-hoc interpreters and the shortage of professional interpreters, clinical communication quality has declined.

Study Design： This is a one-year, single-center, prospective observational study. Methods： The study will be conducted in the cardiology and pulmonology outpatient clinics at Fu Jen University Hospital. A total of 20 cardiopulmonary disease patients will be enrolled, withtheir consultation sessions recorded and transcribed into Chinese. The study will compare the three tools' performance in terms of semantic accuracy, error types, and severity, and analyze their feasibility in clinical practice. Further analysis will involve satisfaction surveys from both professional interpreters and non-native speakers living in Taiwan. The translation results will be evaluated for accuracy and error rates by 8 professional medical interpreters, with a satisfaction survey completed by a total of 14 non-experts. The evaluation tool will be based on the assessment rubrics from the National Accreditation Authority for Translators and Interpreters.

Effect： It is expected that ChatGPT and UD Talk will show better translation accuracy and interpretation quality compared to GT. UD Talk is anticipated to perform better than the other two tools in real-time interpretation. The study results will provide valuable insights for future medical interpretation training and clinical applications.