Acceptability of artificial intelligence (AI)-led chatbot services in healthcare

A mixed-methods study

Bibliographic Data

ID23345877
AuthorsTom Nadarzynski (0000-0001-7010-5308, University of Westminster, corresponding author), Oliver Miles (0000-0002-3214-2244, University College London), Aimee Cowie (University of Southampton), Damien Ridge (0000-0001-9245-5958, University of Westminster)
Year2019
Volume5
Pages2055207619871808-2055207619871808
Publication date2019-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueDIGITAL HEALTH (JOURNAL)
Journal identifiersISSN: 2055-2076 • E-ISSN: 2055-2076
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/2055207619871808
PMID31467682
OpenAlexW2969468266
LanguageEN
Citations received71
References cited28

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 [CI 95% :0.13–0.78] and dislike for talking to computers OR = 0.77 [CI 95% :0.60–0.99] as well as positively correlated with perceived utility OR = 5.10 [CI 95% :3.08–8.43], positive attitude OR = 2.71 [CI 95% :1.77–4.16] and perceived trustworthiness OR = 1.92 [CI 95% :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.

Applications of artificial intelligence · Chatbot · Health care · Medical education · Social media · Trustworthiness · World Wide Web · AI in Service Interactions · Applied Psychology · Artificial Intelligence · Artificial Intelligence in Healthcare and Education · Computer Science · Digital Mental Health Interventions · Medicine · Psychology · Social Psychology