Turner Anne M, Choi Yong K, Dew Kristin, Tsai Ming-Tse, Bosold Alyssa L, Wu Shuyang, Smith Donahue, Meischke Hendrika
School of Public Health, Department of Health Services, University of Washington, Seattle, WA, United States.
School of Medicine, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
JMIR Public Health Surveill. 2019 Jan 28;5(1):e11171. doi: 10.2196/11171.
In the United States, language barriers pose challenges to communication in emergency response and impact emergency care delivery and quality for individuals who are limited English proficient (LEP). There is a growing interest among Emergency Medical Services (EMS) personnel in using automated translation tools to improve communications with LEP individuals in the field. However, little is known about whether automated translation software can be used successfully in EMS settings to improve communication with LEP individuals.
The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP (henceforth referred to as LEP participants) to evaluate the potential of two automated translation technologies in improving emergency communication.
We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking (Mandarin) LEP participants using two automated language translation tools: an EMS domain-specific fixed-sentence translation tool (QuickSpeak) and a statistical machine translation tool (Google Translate). At the end of the sessions, we gathered feedback from both groups through a postsession questionnaire. EMS participants also completed the System Usability Scale (SUS).
We conducted a total of 5 group sessions (3 Chinese and 2 Spanish) with 12 Chinese-speaking LEP participants, 14 Spanish-speaking LEP participants, and 17 EMS personnel. Overall, communications between EMS and LEP participants remained limited, even with the use of the two translation tools. QuickSpeak had higher mean SUS scores than Google Translate (65.3 vs 48.4; P=.04). Although both tools were deemed less than satisfactory, LEP participants showed preference toward the domain-specific system with fixed questions (QuickSpeak) over the free-text translation tool (Google Translate) in terms of understanding the EMS personnel's questions (Chinese 11/12, 92% vs 3/12, 25%; Spanish 12/14, 86% vs 4/14, 29%). While both EMS and LEP participants appreciated the flexibility of the free-text tool, multiple translation errors and difficulty responding to questions limited its usefulness.
Technologies are emerging that have the potential to assist with language translation in emergency response; however, improvements in accuracy and usability are needed before these technologies can be used safely in the field.
在美国，语言障碍给应急响应中的沟通带来挑战，影响了英语水平有限（LEP）人群的紧急医疗服务提供及质量。紧急医疗服务（EMS）人员越来越有兴趣使用自动翻译工具来改善与现场LEP人群的沟通。然而，对于自动翻译软件能否在EMS环境中成功用于改善与LEP人群的沟通，人们了解甚少。
本研究的目的是通过基于场景的方法，与EMS提供者以及自认为是LEP的非英语母语使用者（以下简称LEP参与者）合作，评估两种自动翻译技术在改善紧急沟通方面的潜力。
我们开发了模拟紧急场景，并使用两种自动语言翻译工具，在与EMS人员以及说西班牙语和中文（普通话）的LEP参与者的模拟会议中进行演练：一种是特定于EMS领域的固定语句翻译工具（QuickSpeak）和一种统计机器翻译工具（谷歌翻译）。在会议结束时，我们通过会后问卷收集了两组的反馈。EMS参与者还完成了系统可用性量表（SUS）。
我们总共进行了5组会议（3组中文和2组西班牙语），有12名说中文的LEP参与者、14名说西班牙语的LEP参与者和17名EMS人员。总体而言，即使使用了这两种翻译工具，EMS与LEP参与者之间的沟通仍然有限。QuickSpeak的平均SUS得分高于谷歌翻译（65.3对48.4；P = 0.04）。虽然两种工具都被认为不太令人满意，但在理解EMS人员的问题方面，LEP参与者表现出对带有固定问题的特定领域系统（QuickSpeak）的偏好超过自由文本翻译工具（谷歌翻译）（中文11/12，92%对3/12，25%；西班牙语12/14，86%对4/14，29%）。虽然EMS和LEP参与者都欣赏自由文本工具的灵活性，但多个翻译错误和回答问题的困难限制了其有用性。
正在出现有潜力协助应急响应中语言翻译的技术；然而，在这些技术能够在现场安全使用之前，需要提高准确性和可用性。
