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

Abstract

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.

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

“…A variety of study designs were used in the included studies, comprising 20 case studies [ 44 , 48 , 51 , 61 - 63 , 66 , 69 , 71 , 73 - 79 , 82 , 84 , 85 , 89 ], 4 surveys [ 55 , 56 , 59 , 65 ], 3 observational studies [ 53 , 86 , 87 ], 11 randomized controlled trials [ 46 , 49 , 50 , 57 , 64 , 67 , 72 , 80 , 81 , 83 , 88 ], 3 diagnostic accuracy studies [ 58 , 60 , 68 ], 3 controlled before and after studies [ 30 , 45 , 70 ], 2 ongoing trials [ 51 , 54 ], and 1 pilot study [ 47 ] ( Figure 3 ).…”

Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice

“…An automated chatbot platform might be less desirable for those already attending the clinic; however, it could enable clinicians to triage patients waiting and might improve the overall service delivery. Previous studies identified hesitancy towards AI-led chatbots due to concerns about confidentiality and cyber security as well as the lack of empathy and trust in the autonomous decisions made by computers based on predefined algorithms 16. Another explanation is a typical reluctance towards novel technologies, outlined in the Diffusion of Innovation theory,17 which indicates that the adaptation of new digital solutions is dependent on individual decision-making processes, their promotion, time since the introduction and social systems.…”

Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

“…AI chatbots can be deployed in the form of mobile apps on smartphones, thus making programs available 24/7. AI chatbots have been rapidly transforming multiple fields, including business [ 17 ], governance [ 18 ], education [ 19 ], and health care [ 16 , 20 ]. As the top platforms supporting chatbot development, Amazon Alexa had more than 100,000 programs and Facebook Messenger had more than 300,000 active chatbots as of 2019, many of which are for health care and wellbeing.…”