A Multiparty Collaboration to Engage Diverse Populations in Community-Centered Artificial Intelligence Research

Anna Devon-Sand MPH , Rory Sayres PhD , Yun Liu PhD , Patricia Strachan MSc , Margaret A. Smith MBA , Trinh Nguyen MA , Justin M. Ko MD , Steven Lin MD
{"title":"A Multiparty Collaboration to Engage Diverse Populations in Community-Centered Artificial Intelligence Research","authors":"Anna Devon-Sand MPH ,&nbsp;Rory Sayres PhD ,&nbsp;Yun Liu PhD ,&nbsp;Patricia Strachan MSc ,&nbsp;Margaret A. Smith MBA ,&nbsp;Trinh Nguyen MA ,&nbsp;Justin M. Ko MD ,&nbsp;Steven Lin MD","doi":"10.1016/j.mcpdig.2024.07.001","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI)-enabled technology has the potential to expand access to high-quality health information and health care services. Learning how diverse users interact with technology enables improvements to the AI model and the user interface, maximizing its potential benefit for a greater number of people. This narrative describes how technology developers, academic researchers, and representatives from a community-based organization collaborated to conduct a community-centered project on emerging health technologies. Our project team comprised representatives from Stanford Medicine, Google, and Santa Clara Family Health Plan’s Blanca Alvarado Community Resource Center. We aimed to understand the usability and acceptability of an AI-driven dermatology tool among East San Jose, California, community members. Specifically, our objectives were as follows: to test a model for cross-sector research of AI-based health technology; to determine the utility of the tool in an ethnically and age-diverse population; to obtain in-depth user experience feedback from participants recruited during community events; to offer free skin health consultations; and to provide resources for receiving follow-up care. We describe a collaborative approach in which each party contributed expertise: knowledge of the community from the community health partner, clinical expertise from the academic research institution, and software and AI expertise from the technology company. Through an iterative process, we identified important community needs, including technological, language, and privacy support. Our approach allowed us to recruit and engage a diverse cohort of participants, over 70% of whom preferred a language other than English. We distill learnings from planning and executing this case study that may help other collaborators bridge the gap between academia, industry, and community in AI health care innovation.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 463-469"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000713/pdfft?md5=7070082b704aa5765c6681bfe1a2ee2d&pid=1-s2.0-S2949761224000713-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mayo Clinic Proceedings. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949761224000713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Artificial intelligence (AI)-enabled technology has the potential to expand access to high-quality health information and health care services. Learning how diverse users interact with technology enables improvements to the AI model and the user interface, maximizing its potential benefit for a greater number of people. This narrative describes how technology developers, academic researchers, and representatives from a community-based organization collaborated to conduct a community-centered project on emerging health technologies. Our project team comprised representatives from Stanford Medicine, Google, and Santa Clara Family Health Plan’s Blanca Alvarado Community Resource Center. We aimed to understand the usability and acceptability of an AI-driven dermatology tool among East San Jose, California, community members. Specifically, our objectives were as follows: to test a model for cross-sector research of AI-based health technology; to determine the utility of the tool in an ethnically and age-diverse population; to obtain in-depth user experience feedback from participants recruited during community events; to offer free skin health consultations; and to provide resources for receiving follow-up care. We describe a collaborative approach in which each party contributed expertise: knowledge of the community from the community health partner, clinical expertise from the academic research institution, and software and AI expertise from the technology company. Through an iterative process, we identified important community needs, including technological, language, and privacy support. Our approach allowed us to recruit and engage a diverse cohort of participants, over 70% of whom preferred a language other than English. We distill learnings from planning and executing this case study that may help other collaborators bridge the gap between academia, industry, and community in AI health care innovation.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
多方合作,让不同人群参与以社区为中心的人工智能研究
人工智能(AI)技术有可能扩大高质量健康信息和医疗保健服务的获取范围。通过了解不同用户与技术的交互方式,可以改进人工智能模型和用户界面,使其为更多人带来最大的潜在益处。本文介绍了技术开发人员、学术研究人员和社区组织代表如何合作开展以社区为中心的新兴医疗技术项目。我们的项目团队由斯坦福医学院、谷歌和圣克拉拉家庭健康计划布兰卡-阿尔瓦拉多社区资源中心的代表组成。我们旨在了解加利福尼亚州东圣荷西社区成员对人工智能驱动的皮肤科工具的可用性和可接受性。具体来说,我们的目标如下:测试基于人工智能的健康技术的跨部门研究模式;确定该工具在种族和年龄多元化人群中的实用性;从社区活动中招募的参与者那里获得深入的用户体验反馈;提供免费皮肤健康咨询;以及提供接受后续护理的资源。我们介绍了一种合作方法,其中各方都贡献了自己的专业知识:社区卫生合作伙伴的社区知识、学术研究机构的临床专业知识以及技术公司的软件和人工智能专业知识。通过迭代过程,我们确定了重要的社区需求,包括技术、语言和隐私支持。我们的方法使我们能够招募和吸引多样化的参与者,其中超过 70% 的人喜欢英语以外的语言。我们从这一案例研究的规划和实施过程中总结出了一些经验,这些经验可以帮助其他合作者在人工智能医疗保健创新方面缩小学术界、产业界和社区之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
自引率
0.00%
发文量
0
审稿时长
47 days
期刊最新文献
Developing a Research Center for Artificial Intelligence in Medicine Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience Reviewers for Mayo Clinic Proceedings: Digital Health (2024) A Blueprint for Clinical-Driven Medical Device Development: The Feverkidstool Application to Identify Children With Serious Bacterial Infection Cost-Effectiveness of Artificial Intelligence-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction Trial
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1