在助产教育中使用生成人工智能的远程医疗模拟:以人为中心和文化响应性护理的实践。

IF 2.3
Hannah Cole McGrew, Kendra Faucett, Regina G Russell, Jo Ellen Holt, Jannyse Tapp, Julia Steed, Julia Phillippi
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引用次数: 0

摘要

国际助产士基本能力联合会和美国护士助产士基本助产实践核心能力学院包括安全入门级实践所需的基本技能,并为来自不同背景的个人提供以人为本的护理。然而,助产学学生与不同患者群体互动的机会可能有限,特别是在同质地区。教育项目很难从广泛的社会、文化、种族和宗教背景中招募标准化的患者。此外,助产学学生可能缺乏提供符合文化的护理的技能或经验,这可能会影响患者并加剧健康差距。本文报告了在线人工智能(AI)模拟平台的试点使用,为助产学学生准备以人为本的远程医疗,为文化和社会多样化,服务不足的患者提供服务。该平台使用生成技术生成具有详细历史的交互式化身,并允许虚拟患者和培训助产士之间进行自发和自适应的对话。评估了易用性、虚拟形象的保真度、融入不同文化元素的能力、学生学习、开发时间和成本。案例开发需要协作和迭代方法。与传统模拟类似,基于人工智能的模拟需要仔细的计划,预先和汇报讨论,以及持续的改进努力,以最大限度地提高学生的学习能力。基于生成人工智能的模拟可以为以患者为中心、文化上适当的远程医疗提供高效、灵活的准备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Telehealth Simulations with Generative Artificial Intelligence in Midwifery Education: Practice for Person-Centered and Culturally Responsive Care.

The International Confederation of Midwives Essential Competencies and the American College of Nurse-Midwives Core Competencies for Basic Midwifery Practice include essential skills needed for safe entry-level practice and provision of person-centered care to individuals from diverse backgrounds. However, opportunities for midwifery students to interact with diverse patient populations may be limited, especially in homogenous areas. Education programs struggle to recruit standardized patients from wide-ranging social, cultural, ethnic, and religious backgrounds. In addition, midwifery students may lack skills or experience in providing culturally responsive care, potentially affecting patients and exacerbating health disparities. This article reports on the pilot use of an online artificial intelligence (AI) simulation platform to prepare midwifery students for person-centered telehealth with culturally and socially diverse, underserved patients. The platform used generative technology to produce interactive avatars with detailed histories and allowed spontaneous and adaptive conversations between the virtual patient and midwife-in-training. Ease of use, avatar fidelity, ability to incorporate diverse cultural elements, student learning, time, and cost in the development were assessed. Case development requires collaboration and an iterative approach. Similar to traditional simulation, AI-based simulations require careful planning, pre- and debriefing discussions, and continuous improvement efforts for maximal student learning. Generative AI-based simulations can enable efficient, flexible preparation for patient-centered, culturally appropriate telehealth.

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