Department of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States.
Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.
Appl Clin Inform. 2023 Mar;14(2):374-391. doi: 10.1055/a-2035-5342. Epub 2023 Feb 14.
Patient and provider-facing screening tools for social determinants of health have been explored in a variety of contexts; however, effective screening and resource referral remain challenging, and less is known about how patients perceive chatbots as potential social needs screening tools. We investigated patient perceptions of a chatbot for social needs screening using three implementation outcome measures: acceptability, feasibility, and appropriateness.
We implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews.
The screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants ( = 350) rated the chatbot as an acceptable, feasible, and appropriate way of screening. Through interviews ( = 22), participants explained that the chatbot was a responsive, private, easy to use, efficient, and comfortable channel to report social needs in the ED, but wanted more information on data use and more support in accessing resources.
In this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.
在各种情况下都探索了针对健康社会决定因素的面向患者和提供者的筛查工具;然而,有效的筛查和资源转介仍然具有挑战性,对于患者如何看待聊天机器人作为潜在的社会需求筛查工具,人们了解得较少。我们使用三个实施结果衡量标准(可接受性、可行性和适当性)调查了患者对社会需求筛查聊天机器人的看法。
我们在一家大型公立医院急诊室实施了社会需求筛查聊天机器人,并使用同期三角测量来评估聊天机器人用于筛查的看法。共有 350 名 ED 访客完成了社会需求筛查,并对实施结果衡量标准对聊天机器人进行了评分,22 名参与者进行了后续电话访谈。
筛查参与者的年龄从 18 岁到 90 岁不等,在种族/民族、教育和保险状况方面具有多样性。参与者(n=350)认为聊天机器人是一种可接受、可行和适当的筛查方式。通过访谈(n=22),参与者解释说,聊天机器人是一种响应迅速、私密、易于使用、高效和舒适的报告急诊室社会需求的渠道,但希望获得更多关于数据使用的信息,并在获取资源方面获得更多支持。
在这项研究中,我们在真实环境中部署了用于社会需求筛查的聊天机器人,发现患者认为聊天机器人是一种可接受、可行和适当的社会需求筛查方式。研究结果表明,聊天机器人是一种很有前途的社会需求筛查方式,可以成功吸引大量不同的 ED 患者群体。这很重要,因为这表明聊天机器人可以促进一个最终将患者与社会需求护理联系起来的筛查过程,从而改善弱势群体患者的健康和福祉。