Department of Otolaryngology, Mass Eye and Ear, Harvard Medical School, Boston, MA, United States.
Brown University, Providence, RI, United States.
Front Public Health. 2024 Feb 22;12:1337395. doi: 10.3389/fpubh.2024.1337395. eCollection 2024.
Online medical education often faces challenges related to communication and comprehension barriers, particularly when the instructional language differs from the healthcare providers' and caregivers' native languages. Our study addresses these challenges within pediatric healthcare by employing generative language models to produce a linguistically tailored, multilingual curriculum that covers the topics of team training, surgical procedures, perioperative care, patient journeys, and educational resources for healthcare providers and caregivers.
An interdisciplinary group formulated a video curriculum in English, addressing the nuanced challenges of pediatric healthcare. Subsequently, it was translated into Spanish, primarily emphasizing Latin American demographics, utilizing OpenAI's GPT-4. Videos were enriched with synthetic voice profiles of native speakers to uphold the consistency of the narrative.
We created a collection of 45 multilingual video modules, each ranging from 3 to 8 min in length and covering essential topics such as teamwork, how to improve interpersonal communication, "How I Do It" surgical procedures, as well as focused topics in anesthesia, intensive care unit care, ward nursing, and transitions from hospital to home. Through AI-driven translation, this comprehensive collection ensures global accessibility and offers healthcare professionals and caregivers a linguistically inclusive resource for elevating standards of pediatric care worldwide.
This development of multilingual educational content marks a progressive step toward global standardization of pediatric care. By utilizing advanced language models for translation, we ensure that the curriculum is inclusive and accessible. This initiative aligns well with the World Health Organization's Digital Health Guidelines, advocating for digitally enabled healthcare education.
在线医学教育常常面临沟通和理解障碍的挑战，尤其是当教学语言与医疗保健提供者和照护者的母语不同时。我们的研究通过使用生成式语言模型为儿科医疗保健提供了一个语言定制的多语言课程，涵盖了团队培训、手术程序、围手术期护理、患者旅程以及医疗保健提供者和照护者的教育资源等主题。
一个跨学科小组用英语制定了一个视频课程，针对儿科医疗保健的细微挑战。随后，它被翻译成西班牙语，主要强调拉丁美洲的人口统计学，利用 OpenAI 的 GPT-4。视频中加入了母语人士的合成语音档案，以保持叙述的一致性。
我们创建了一个包含 45 个多语言视频模块的集合，每个模块的长度从 3 到 8 分钟不等，涵盖了团队合作、如何改善人际沟通、“我是怎么做的”手术程序以及麻醉、重症监护病房护理、病房护理以及从医院到家庭的过渡等重点主题。通过人工智能驱动的翻译，这个全面的集合确保了全球的可访问性，并为医疗保健专业人员和照护者提供了一个语言包容性的资源，以提高全球儿科护理的标准。
多语言教育内容的开发标志着儿科护理全球标准化的一个进步步骤。通过使用先进的语言模型进行翻译，我们确保课程具有包容性和可访问性。这一举措符合世界卫生组织的数字健康指南，倡导数字化赋能的医疗保健教育。
