Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
By Tom Nadarzynski, Oliver Miles, A. Cowie, D. Ridge. Digital Health, 2019
660 citations recorded by the source corpus.
Background 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. Methods 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. Results 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 [CI95%:0.13–0.78] and dislike for talking to computers OR = 0.77 [CI95%:0.60–0.99] as well as positively correlated with perceived utility OR = 5.10 [CI95%:3.08–8.43], positive attitude OR = 2.71 [CI95%:1.77–4.16] and perceived trustworthiness OR = 1.92 [CI95%:1.13–3.25]. Conclusion 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.
Subfields: Applied Psychology, Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/0c432b171e52c86203843e200260f443a72ecaed
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Citations in the corpus
- Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020, methodology): “In the study by Nadarzynski et al [59] , users favored the conversational agent because of its ubiquity as a convenient smartphone app and its ability to perform remote services such as video consultation, potentially alleviating any inhibitions users may have around discussing sexual health in…”
- “Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model (2023, background): “[128] found that older adults were more likely to consider privacy concerns when evaluating the usefulness and appropriateness of health-related chatbots.”
- The Internet of Things: Impact and Implications for Health Care Delivery (2020, background): “Md, Babylon, and HealthTap, where a patient can input their symptoms and advice is generated instantly [72].”