Nadarzynski Tom, Miles Oliver, Cowie Aimee, Ridge Damien
The University of Westminster, London, UK.
University College London, London, UK.
Digit Health. 2019 Aug 21;5:2055207619871808. doi: 10.1177/2055207619871808. eCollection 2019 Jan-Dec.
Artificial intelligence (AI) is increasingly being used in healthcare. Here, AI-based chatbot systems can act as automated conversational agents, capable of promoting health, providing education, and potentially prompting behaviour change. Exploring the motivation to use health chatbots is required to predict uptake; however, few studies to date have explored their acceptability. This research aimed to explore participants' willingness to engage with AI-led health chatbots.
The study incorporated semi-structured interviews (N-29) which informed the development of an online survey (N-216) advertised via social media. Interviews were recorded, transcribed verbatim and analysed thematically. A survey of 24 items explored demographic and attitudinal variables, including acceptability and perceived utility. The quantitative data were analysed using binary regressions with a single categorical predictor.
Three broad themes: 'Understanding of chatbots', 'AI hesitancy' and 'Motivations for health chatbots' were identified, outlining concerns about accuracy, cyber-security, and the inability of AI-led services to empathise. The survey showed moderate acceptability (67%), correlated negatively with perceived poorer IT skills OR = 0.32 [CI:0.13-0.78] and dislike for talking to computers OR = 0.77 [CI:0.60-0.99] as well as positively correlated with perceived utility OR = 5.10 [CI:3.08-8.43], positive attitude OR = 2.71 [CI:1.77-4.16] and perceived trustworthiness OR = 1.92 [CI:1.13-3.25].
Most internet users would be receptive to using health chatbots, although hesitancy regarding this technology is likely to compromise engagement. Intervention designers focusing on AI-led health chatbots need to employ user-centred and theory-based approaches addressing patients' concerns and optimising user experience in order to achieve the best uptake and utilisation. Patients' perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots.
人工智能（AI）在医疗保健领域的应用日益广泛。在此，基于人工智能的聊天机器人系统可充当自动化对话代理，能够促进健康、提供教育并可能促使行为改变。为预测其应用情况，有必要探究使用健康聊天机器人的动机；然而，迄今为止，很少有研究探讨其可接受性。本研究旨在探究参与者与人工智能主导的健康聊天机器人互动的意愿。
该研究纳入了半结构化访谈（N = 29），这些访谈为通过社交媒体进行宣传的在线调查（N = 216）的开展提供了信息。访谈进行了录音，逐字转录并进行了主题分析。一项包含24个项目的调查探讨了人口统计学和态度变量，包括可接受性和感知效用。使用具有单个分类预测变量的二元回归分析定量数据。
确定了三个广泛的主题：“对聊天机器人的理解”、“对人工智能的犹豫”和“使用健康聊天机器人的动机”，概述了对准确性、网络安全以及人工智能主导服务缺乏同理心的担忧。调查显示出中等程度的可接受性（67%），与较差的信息技术技能感知呈负相关（OR = 0.32 [CI：0.13 - 0.78]）以及与不喜欢与计算机交谈呈负相关（OR = 0.77 [CI：0.60 - 0.99]），同时与感知效用呈正相关（OR = 5.10 [CI：3.08 - 8.4]））、积极态度呈正相关（OR = 2.71 [CI：1.77 - 4.16]）以及与感知可信度呈正相关（OR = 1.92 [CI：1.13 - 3.25]）。
大多数互联网用户愿意使用健康聊天机器人，尽管对这项技术的犹豫可能会影响参与度。专注于人工智能主导的健康聊天机器人的干预设计者需要采用以用户为中心且基于理论的方法，解决患者的担忧并优化用户体验，以实现最佳的应用和利用效果。在开发和评估健康聊天机器人的有效性时，需要考虑患者的观点、动机和能力。
