The Halo Effect: Perceptions of Information Privacy Among Healthcare Chatbot Users

IF 4.3 2区 医学 Q1 GERIATRICS & GERONTOLOGY
Jessica R. Ellis, Natalia S. Dellavalle, Mika K. Hamer, Marlee Akerson, Matt Andazola, Annie A. Moore, Eric G. Campbell, Matthew DeCamp
{"title":"The Halo Effect: Perceptions of Information Privacy Among Healthcare Chatbot Users","authors":"Jessica R. Ellis,&nbsp;Natalia S. Dellavalle,&nbsp;Mika K. Hamer,&nbsp;Marlee Akerson,&nbsp;Matt Andazola,&nbsp;Annie A. Moore,&nbsp;Eric G. Campbell,&nbsp;Matthew DeCamp","doi":"10.1111/jgs.19393","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Patient-facing chatbots can be used for administrative tasks, personalized care reminders, and overcoming transportation or geographic barriers in healthcare. Although some data suggest older adults see privacy as an ethical barrier to adopting digital technologies, little is known about privacy concerns regarding information shared with novel patient-facing chatbots. We sought to examine attitudes toward privacy based on age or other sociodemographic characteristics.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We conducted a sequential mixed methods study among patient users of a large healthcare system chatbot. We purposively oversampled by race and ethnicity to survey 3089 patient chatbot users online using de novo and validated items. Next, we conducted semi-structured interviews with users (<i>n</i> = 46) purposively sampled based on diversity or select survey responses. We used multivariable logistic regression to analyze survey data and modified grounded theory to analyze interviews. We integrated data using simultaneous visualization and triangulation.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We received 617/3089 surveys (response rate, 20.0%). Overall, 370/597 (63.9%) expressed worry about the privacy of information shared with the chatbot. Logistic regression found that users ≥ 65 years were 26% points less likely to be worried about information privacy compared to those 18–34 years old (<i>p</i> &lt; 0.001). We found less worry among Black, non-Hispanic users and more worry among those with more than a four-year college degree [Correction added on 4 April 2025, after first online publication: The preceding sentence has been revised in this version.]. By integrating our survey and interview data, we observed that older adult users experienced a halo effect: they worried less because they saw the chatbot as associated with a trusted health system and experienced lower medical mistrust.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Contrary to some prior research, adults aged 65 and older expressed less concern about chatbot privacy than younger adults because of their trust in health care. To maintain this trust and build it among all users, health systems using patient-facing chatbots need to take active steps to maintain and communicate patient privacy protections.</p>\n </section>\n </div>","PeriodicalId":17240,"journal":{"name":"Journal of the American Geriatrics Society","volume":"73 5","pages":"1472-1483"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Geriatrics Society","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jgs.19393","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Patient-facing chatbots can be used for administrative tasks, personalized care reminders, and overcoming transportation or geographic barriers in healthcare. Although some data suggest older adults see privacy as an ethical barrier to adopting digital technologies, little is known about privacy concerns regarding information shared with novel patient-facing chatbots. We sought to examine attitudes toward privacy based on age or other sociodemographic characteristics.

Methods

We conducted a sequential mixed methods study among patient users of a large healthcare system chatbot. We purposively oversampled by race and ethnicity to survey 3089 patient chatbot users online using de novo and validated items. Next, we conducted semi-structured interviews with users (n = 46) purposively sampled based on diversity or select survey responses. We used multivariable logistic regression to analyze survey data and modified grounded theory to analyze interviews. We integrated data using simultaneous visualization and triangulation.

Results

We received 617/3089 surveys (response rate, 20.0%). Overall, 370/597 (63.9%) expressed worry about the privacy of information shared with the chatbot. Logistic regression found that users ≥ 65 years were 26% points less likely to be worried about information privacy compared to those 18–34 years old (p < 0.001). We found less worry among Black, non-Hispanic users and more worry among those with more than a four-year college degree [Correction added on 4 April 2025, after first online publication: The preceding sentence has been revised in this version.]. By integrating our survey and interview data, we observed that older adult users experienced a halo effect: they worried less because they saw the chatbot as associated with a trusted health system and experienced lower medical mistrust.

Conclusion

Contrary to some prior research, adults aged 65 and older expressed less concern about chatbot privacy than younger adults because of their trust in health care. To maintain this trust and build it among all users, health systems using patient-facing chatbots need to take active steps to maintain and communicate patient privacy protections.

光环效应:医疗聊天机器人用户对信息隐私的看法。
背景:面向患者的聊天机器人可用于管理任务、个性化护理提醒以及克服医疗保健中的交通或地理障碍。尽管一些数据表明,老年人将隐私视为采用数字技术的道德障碍,但人们对与新型面向患者的聊天机器人分享信息时的隐私担忧知之甚少。我们试图根据年龄或其他社会人口特征来研究对隐私的态度。方法:我们在大型医疗保健系统聊天机器人的患者用户中进行了顺序混合方法研究。我们有意按种族和民族进行抽样调查,使用从头开始和经过验证的项目在线调查3089名患者聊天机器人用户。接下来,我们对用户(n = 46)进行了半结构化访谈,根据多样性或选择的调查回答有目的地抽样。我们使用多变量逻辑回归分析调查数据,并修正扎根理论分析访谈。我们使用同时可视化和三角测量来整合数据。结果:共收到问卷617/3089份,回复率为20.0%。总体而言,597人中有370人(63.9%)对与聊天机器人共享的信息隐私表示担忧。逻辑回归发现,与18-34岁的用户相比,≥65岁的用户担心信息隐私的可能性低26% (p结论:与之前的一些研究相反,65岁及以上的成年人对聊天机器人隐私的担忧程度低于年轻人,因为他们信任医疗保健。为了维持这种信任并在所有用户之间建立这种信任,使用面向患者的聊天机器人的卫生系统需要采取积极措施来维护和沟通患者隐私保护。
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 求助全文
来源期刊
CiteScore
10.00
自引率
6.30%
发文量
504
审稿时长
3-6 weeks
期刊介绍: Journal of the American Geriatrics Society (JAGS) is the go-to journal for clinical aging research. We provide a diverse, interprofessional community of healthcare professionals with the latest insights on geriatrics education, clinical practice, and public policy—all supporting the high-quality, person-centered care essential to our well-being as we age. Since the publication of our first edition in 1953, JAGS has remained one of the oldest and most impactful journals dedicated exclusively to gerontology and geriatrics.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信