Developing Community-Responsive mHealth and AI/ML: Understanding Perspectives of Hispanic Community Members in Washington State

开发社区响应型移动医疗和人工智能/机器学习：了解华盛顿州西班牙裔社区成员的观点

• 批准号: 10598360
• 负责人: Mary L. Disis
• 金额: $ 30.42万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598360
• 关键词: Artificial Intelligence; Attention; Chronic; Clinical; Clinical and Translational Science Awards; Communities; Community Developments; Coupled; Development; Discrimination; Education; Ensure; Ethics; Event; Face; Feedback; Focus Groups; Fostering; Future; Geography; Goals; Health; Health Personnel; Health Technology; Healthcare; Hispanic; Individual; Informatics; Infrastructure; Institutes; Institutional Racism; Internet; Language; Life; Machine Learning; Methods; Mobile Health Application; Participant; Patients; Persons; Population; Positioning Attribute; Predictive Analytics; Preventive; Process; Registries; Research; Research Personnel; Resources; Risk; Rural; Symptoms; Technology; Time; Translational Research; Universities; Visual Aid; Washington; Work; barrier to care; base; community center; community engaged research; community engagement; community setting; cost; digital health; evidence base; experience; handheld mobile device; health application; health care availability; health care delivery; health care model; health communication; health disparity; health equity; health inequalities; hispanic community; improved; mHealth; marginalized community; member; mortality; multidisciplinary; patient population; prototype; recruit; rural area; sensor; social culture; technology development; tool; translational health science; translational scientist; urban area; wearable sensor technology

Mobile health applications (mHealth) and digital health sensors coupled with artificial intelligence and machine learning (AI/ML) have the potential to become breakthrough technologies and support a holistic, preventive model of healthcare. These technologies allow individuals to integrate their daily life activities with healthcare delivery and are especially promising for patients living with 1 or more chronic conditions. mHealth technologies are key to reaching populations who rely on mobile devices for internet access. They also have the potential to reduce health inequities for those who face barriers to healthcare access because of cost, geography, language, or systemic racism. However, if these technologies are developed without appropriately incorporating the values of those intended to benefit from them, they risk exacerbating rather than alleviating health disparities by amplifying existing structural assumptions in healthcare, resulting in technologies that are unusable, unresponsive, or otherwise not appropriate for the sociocultural context in which people live. Researchers developing mHealth and AI/ML technologies need a robust evidence base about community values to inform technology development, including which health conditions are highest priority and tools to support embedding community values throughout research. This project will build on longstanding relationships that the University of Washington Institute of Translational Health Sciences (ITHS) has developed with Hispanic communities across Washington State. We will examine ethical questions about the potential benefits and burdens of mHealth and AI/ML from the perspective of Hispanic-identifying community members and produce a prototype translational resource for mHealth and AI/ML research and application development. This supplement brings together 2 key goals for ITHS: (1) integrating community stakeholders at all stages of translational research, and (2) developing accessible methods, tools, and education for informatics and machine learning. To advance these goals, we aim to: (1) develop community-facing materials conveying key features of potential mHealth applications; (2) describe perspectives of Hispanic-identifying community members in Washington State about the use of mHealth and AI/ML; and (3) produce a prototype translational resource to guide mHealth and AI/ML researchers in development of community-responsive technologies. This resource can be refined by future research teams, both within the Clinical and Translational Science Award (CTSA) consortium and beyond, and can be used to guide future work across patient populations and clinical scenarios.

移动健康应用程序（mHealth）和数字健康传感器与人工智能和机器相结合 学习（AI/ML）有潜力成为突破性技术，并支持全面的、预防性的 医疗保健模式。这些技术使个人能够将日常生活活动与医疗保健相结合 分娩，对于患有一种或多种慢性病的患者尤其有希望。移动医疗 技术是接触依赖移动设备访问互联网的人群的关键。他们还有 对于那些因费用而面临医疗保健障碍的人来说，有可能减少健康不平等， 地理、语言或系统性种族主义。然而，如果这些技术的开发没有得到适当的 纳入那些打算从中受益的人的价值观，它们可能会加剧而不是缓解 通过放大医疗保健领域现有的结构性假设来缩小健康差距，从而产生可 无法使用、反应迟钝或不适合人们所生活的社会文化背景。 开发移动医疗和人工智能/机器学习技术的研究人员需要关于社区的强大证据基础 为技术发展提供信息的价值观，包括哪些健康状况是最优先考虑的以及促进技术发展的工具 支持在整个研究中嵌入社区价值观。 该项目将建立在与华盛顿大学转化研究所的长期合作关系的基础上 健康科学 (ITHS) 与华盛顿州的西班牙裔社区一起发展。我们将检查 从以下角度探讨移动医疗和人工智能/机器学习的潜在利益和负担的伦理问题 确定西班牙裔社区成员，并为移动医疗和移动医疗制作原型翻译资源 AI/ML 研究和应用开发。本增刊汇集了 ITHS 的 2 个关键目标： (1) 在转化研究的各个阶段整合社区利益相关者，以及 (2) 开发可访问的 信息学和机器学习的方法、工具和教育。为了推进这些目标，我们的目标是： (1) 开发面向社区的材料，传达潜在移动医疗应用的主要特征； (2) 描述 华盛顿州西班牙裔社区成员对移动医疗和移动医疗使用的看法 人工智能/机器学习； (3) 制作原型翻译资源来指导移动医疗和人工智能/机器学习研究人员 开发社区响应技术。未来的研究团队可以完善​​该资源， 临床和转化科学奖 (CTSA) 联盟内外，可用于 指导未来跨患者群体和临床场景的工作。