"I just assumed that it would translate": examining MT risk awareness among healthcare staff with abbreviations as a use case

2026-10-01

AI Summary

This study addresses patient safety risks arising from machine translation (MT) errors when processing medical abbreviations, aiming to evaluate the limited awareness among UK healthcare professionals regarding such mistranslation hazards. Drawing on a clinical corpus and simulated high-risk scenarios generated via Google Translate, the research employs semi-structured interviews and cross-lingual qualitative content analysis. Findings reveal that healthcare professionals generally lack vigilance toward the potential risks posed by MT. By bridging the empirical gap in MT risk awareness within clinical documentation, this work exposes cognitive blind spots in human–machine collaboration under high-stakes conditions. Ultimately, it provides critical evidence to inform the standardized clinical deployment of MT technologies and to safeguard patient safety.

Abstract

In the UK, public healthcare staff report turning to machine translation (MT) - predominantly Google Translate (GT) - to communicate with patients across language barriers. Though intended to support their duty of care, potentially uninformed reliance on MT in such contexts could have serious consequences for patient safety. Research nonetheless remains limited on staff awareness of the possible risks posed by higher-stakes MT use in general and with patient medical records in particular, most existing literature instead examining its use in interpersonal situations or with patient-oriented documentation. Moreover, medical abbreviations are well-documented as increasing patient risk even monolingually, with outcomes from their misuse and/or misinterpretation ranging from temporary harm to the death of the patient. Abbreviations were therefore selected as a use case for identifying the potential risks posed by their translation with MT. Contextualised French and Spanish data examples drawn from authoritative clinical corpora and translated via GT were presented during semi-structured interviews to 21 healthcare staff participants in diverse roles and specialties. The results were then subject to qualitative analysis and cross-analysis.

Problem

• Machine Translation
• Healthcare
• Risk Awareness
• Medical Abbreviations
• Patient Safety

Innovation

• Machine Translation
• Medical Abbreviations
• Risk Awareness
• Healthcare
• Qualitative Analysis