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Using Voice-to-Voice Machine Translation to Overcome Language Barriers in Clinical Communication: An Exploratory
Patricia Hudelson1,2, François Chappuis3,4
1Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland. Patricia.Hudelson@hug.ch.
Journal of General Internal Medicine
|February 12, 2024
Summary
Machine translation (MT) apps can aid healthcare communication when interpreters are unavailable. While useful, MT requires careful use to manage risks and ensure effective patient-provider dialogue.
Area of Science:
- Medical Informatics
- Health Communication
- Clinical Practice
Background:
- Machine translation (MT) apps are increasingly used by healthcare professionals to bridge language gaps.
- The growing accuracy and accessibility of MT necessitate guidance on its safe and effective use in clinical settings.
Purpose of the Study:
- To explore factors influencing communication when using voice-to-voice MT in healthcare.
- To assess the feasibility and challenges of integrating MT into routine patient encounters.
Main Methods:
- Healthcare professionals used voice-to-voice MT apps during patient consultations.
- Feedback was collected from both professionals and patients, with a subset of consultations observed.
Main Results:
- Consultation goals were achieved in 82.7% of cases, but satisfaction with MT communication was lower (53.8%).
- Patients generally found MT communication easy (86%), and willingness to use MT in the future was high among both groups.
- Factors like language (European languages performed better) and specific speech practices impacted communication effectiveness.
Conclusions:
- Voice-to-voice MT may be a viable option in certain clinical scenarios, but professional interpreters remain optimal.
- Healthcare institutions must address potential MT errors and establish conditions for safe communication.
- Further research in real-world settings is crucial for developing guidelines and training for MT use in clinical communication.
