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.

移动的健康应用程序（mHealth）和数字健康传感器与人工智能和机器相结合 学习（AI/ML）有可能成为突破性技术并支持整体的、预防性的 医疗模式。这些技术允许个人将日常生活活动与医疗保健相结合 这些药物可用于递送，并且对于患有1种或多种慢性疾病的患者尤其有希望。mHealth 技术是接触依靠移动的设备上网的人口的关键。他们也有 对于那些因费用而面临医疗保健障碍的人来说，减少卫生不公平的潜力， 地理、语言或系统性种族主义。然而，如果这些技术的发展没有适当的 如果将那些打算从中受益的人的价值观纳入其中， 通过放大医疗保健中现有的结构性假设， 不能使用，反应迟钝，或者不适合人们生活的社会文化背景。 开发移动健康和人工智能/机器学习技术的研究人员需要关于社区的强大证据库 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事项，以及 支持在整个研究过程中嵌入社区价值观。 该项目将建立在长期的关系，华盛顿大学翻译研究所 健康科学（Health Sciences，缩写为HMS）与整个华盛顿州的西班牙裔社区一起发展。我们将研究 关于mHealth和AI/ML的潜在利益和负担的伦理问题， 西班牙裔识别社区成员，并为mHealth和 AI/ML研究和应用开发。本补充文件汇集了CNAS的两个关键目标： (1)将社区利益相关者纳入转化研究的各个阶段，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。为了实现这些目标，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关键特征;（2）描述 华盛顿州的西班牙裔社区成员对使用移动医疗的看法， AI/ML;（3）生成原型翻译资源，以指导mHealth和AI/ML研究人员 开发社区响应技术。这种资源可以由未来的研究团队加以改进， 在临床和转化科学奖（CTSA）联盟内外，并可用于 指导患者人群和临床场景的未来工作。