Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study

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 i...

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Main Authors: Tom Nadarzynski, Oliver Miles, Aimee Cowie, Damien Ridge
Format: Article
Language:English
Published: SAGE Publishing 2019-08-01
Series:Digital Health
Online Access:https://doi.org/10.1177/2055207619871808
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spelling doaj-3400d43285dd4d26a8116081312abc0d2020-11-25T03:19:00ZengSAGE PublishingDigital Health2055-20762019-08-01510.1177/2055207619871808Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods studyTom NadarzynskiOliver MilesAimee CowieDamien RidgeBackground 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.https://doi.org/10.1177/2055207619871808
collection DOAJ
language English
format Article
sources DOAJ
author Tom Nadarzynski
Oliver Miles
Aimee Cowie
Damien Ridge
spellingShingle Tom Nadarzynski
Oliver Miles
Aimee Cowie
Damien Ridge
Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
Digital Health
author_facet Tom Nadarzynski
Oliver Miles
Aimee Cowie
Damien Ridge
author_sort Tom Nadarzynski
title Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
title_short Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
title_full Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
title_fullStr Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
title_full_unstemmed Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study
title_sort acceptability of artificial intelligence (ai)-led chatbot services in healthcare: a mixed-methods study
publisher SAGE Publishing
series Digital Health
issn 2055-2076
publishDate 2019-08-01
description 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.
url https://doi.org/10.1177/2055207619871808
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