Understanding the factors that influence users’ perceptions of healthcare chatbots is crucial for ensuring effective adoption and optimizing chatbots’ role in health service delivery. In this study, we conducted an online survey to identify specific beliefs and perspectives associated with users’ willingness to use healthcare chatbots and its perceived usefulness for health self-management. The results suggest that a willingness to use healthcare chatbots was positively associated with experiencing less tension during interactions with chatbots compared to interactions with clinician (p < .001), using chatbots to prepare questions before medical appointments (p = .003), and a willingness to recommend them to others (p < .001). Views on health self-management using healthcare chatbots were also positively related to prior AI experience (p < .001), data accuracy (p = .025), time efficiency (p = .022), and post-appointment use (p < .001). These insights provide a nuanced understanding of user needs, enabling future chatbot designs to better address expectations and enhance user experience, satisfaction, and sustainable adoption.
AkaikeH. (1998). Information theory and an extension of the maximum likelihood principle. In ParzenE.TanabeK.KitagawaG. (Eds.), Selected papers of Hirotugu Akaike (pp. 199–213). Springer New York. https://doi.org/10.1007/978-1-4612-1694-0_15
2.
AlkurdiD.RasouliS.TalamontiW. J.NasirM.AlsaidA. (2024). From categorical to dimensional: A multifaceted approach to emotions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 1519–1525https://doi.org/10.1177/10711813241262446
3.
AlmalkiM. (2021). Exploring the influential factors of consumers’ willingness toward using COVID-19 related chatbots: An empirical study. Medical Archives, 75(1), 50. https://doi.org/10.5455/medarh.2021.75.50-55
4.
BaldaufM.FröehlichP.EndlR. (2020). Trust me, I’m a doctor – User perceptions of AI-driven apps for mobile health diagnosis. In Proceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia (pp. 167–178). ACM. https://doi.org/10.1145/3428361.3428362
5.
BiroJ.LinderC.NeyensD. (2023). The effects of a health care chatbot’s complexity and persona on user trust, perceived usability, and effectiveness: Mixed methods study. JMIR Human Factors, 10, e41017. https://doi.org/10.2196/41017
Brucker-KleyE.KleinbergerU.KellerT.ChristenJ.Keller-SennA.KoppitzA. (2021). Identifying research gaps: A review of virtual patient education and self-management. Technology and Health Care, 29(6), 1057–1069. https://doi.org/10.3233/THC-202665
8.
ChangI.-C.ShihY.-S.KuoK.-M. (2022). Why would you use medical chatbots? Interview and survey. International Journal of Medical Informatics, 165, 104827. https://doi.org/10.1016/j.ijmedinf.2022.104827
9.
ChewH. S. J.AchananuparpP. (2022). Perceptions and needs of artificial intelligence in health care to increase adoption: Scoping review. Journal of Medical Internet Research, 24(1), e32939. https://doi.org/10.2196/32939
10.
FieldA.FieldZ.MilesJ. (2012). Discovering statistics using R. Sage.
11.
GriffinA. C.KhairatS.BaileyS. C.ChungA. E. (2023). A chatbot for hypertension self-management support: User-centered design, development, and usability testing. JAMIA Open, 6(3), ooad073. https://doi.org/10.1093/jamiaopen/ooad073
12.
HaoyueL. L.ChoH. (2025). Factors influencing intention to engage in human–chatbot interaction: Examining user perceptions and context culture orientation. Universal Access in the Information Society, 24(1), 607–620. https://doi.org/10.1007/s10209-023-01087-7
13.
HoA.HancockJ.MinerA. S. (2018). Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. Journal of Communication, 68(4), 712–733. https://doi.org/10.1093/joc/jqy026
14.
KadariyaD.VenkataramananR.YipH. Y.KalraM.ThirunarayananK.ShethA. (2019). kBot: Knowledge-enabled personalized chatbot for asthma self-management. In 2019 IEEE international conference on smart computing (SMARTCOMP) (pp. 138–143). IEEE. https://doi.org/10.1109/SMARTCOMP.2019.00043
15.
KimM.OhJ.KimD.ShinJ.LeeD. (2024). Understanding user preferences in developing a mental healthcare AI chatbot: A conjoint analysis approach. International Journal of Human–Computer Interaction, 41(8), 4813–4821. https://doi.org/10.1080/10447318.2024.2353450
16.
KoscelnyS. N.NeyensD. M. (2024). The effect of healthcare chatbots’ information presentation styles on user acceptance in a knowledge seeking task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 493–498. https://doi.org/10.1177/10711813241263509
17.
KoscelnyS. N.SadralashrafiS.NeyensD. M. (2025). Generative AI responses are a dime a dozen; Making them count is the challenge—Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models. Applied Ergonomics, 128, 104515. https://doi.org/10.1016/j.apergo.2025.104515
18.
LaranjoL.DunnA. G.TongH. L.KocaballiA. B.ChenJ.BashirR.SurianD.GallegoB.MagrabiF.LauA. Y. S.CoieraE. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072
19.
LiD.HuY.PfaffH.WangL.DengL.LuC.XiaS.ChengS.ZhuX.WuX. (2020). Determinants of patients’ intention to use the online inquiry services provided by internet hospitals: empirical evidence from China. Journal of Medical Internet Research, 22(10), e22716. https://doi.org/10.2196/22716
20.
LuL.CaiR.GursoyD. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
21.
MartinengoL.LumE.CarJ. (2022). Evaluation of chatbot-delivered interventions for self-management of depression: Content analysis. Journal of Affective Disorders, 319, 598–607. https://doi.org/10.1016/j.jad.2022.09.028
22.
MassaP.De Souza FerrazD. A.MagnoL.SilvaA. P.GrecoM.DouradoI.GrangeiroA. (2023). A transgender chatbot (Amanda Selfie) to create pre-exposure prophylaxis demand among adolescents in Brazil: Assessment of acceptability, functionality, usability, and results. Journal of Medical Internet Research, 25, e41881. https://doi.org/10.2196/41881
23.
MeshramS.NaikN.VrM.MoreT.KharcheS. (2021). Conversational AI: Chatbots. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE. https://doi.org/10.1109/CONIT51480.2021.9498508
24.
MilesO.WestR.NadarzynskiT. (2021). Health chatbots acceptability moderated by perceived stigma and severity: A cross-sectional survey. Digital Health, 7, 20552076211063012. https://doi.org/10.1177/20552076211063012
25.
NadarzynskiT.MilesO.CowieA.RidgeD. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health, 5, 205520761987180. https://doi.org/10.1177/2055207619871808
26.
PalanicaA.FlaschnerP.ThommandramA.LiM.FossatY. (2019). Physicians’ perceptions of chatbots in health care: Cross-sectional web-based survey. Journal of Medical Internet Research, 21(4), e12887. https://doi.org/10.2196/12887
27.
Tudor CarL.DhinagaranD. A.KyawB. M.KowatschT.JotyS.ThengY.-L.AtunR. (2020). Conversational agents in health care: scoping review and conceptual analysis. Journal of Medical Internet Research, 22(8), e17158. https://doi.org/10.2196/17158
28.
VenkateshV.ThongJ. Y. L.XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
29.
WangX.LuoR.LiuY.ChenP.TaoY.HeY. (2023). Revealing the complexity of users’ intention to adopt healthcare chatbots: A mixed-method analysis of antecedent condition configurations. Information Processing & Management, 60(5), 103444. https://doi.org/10.1016/j.ipm.2023.103444
30.
WuP. F.SummersC.PanesarA.KauraA.ZhangL. (2024). AI Hesitancy and acceptability—perceptions of AI chatbots for chronic health management and long COVID support: Survey study. JMIR Human Factors, 11, e51086–e51086. https://doi.org/10.2196/51086
31.
WutzM.HermesM.WinterV.Köberlein-NeuJ. (2023). Factors influencing the acceptability, acceptance, and adoption of conversational agents in health care: Integrative review. Journal of Medical Internet Research, 25, e46548. https://doi.org/10.2196/46548
32.
YinR.NeyensD. M. (2024). Examining how information presentation methods and a chatbot impact the use and effectiveness of electronic health record patient portals: An exploratory study. Patient Education and Counseling, 119, 108055. https://doi.org/10.1016/j.pec.2023.108055
33.
ZhangZ.CitardiD.WangD.GencY.ShanJ.FanX. (2021). Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data. Health Informatics Journal, 27(2), 14604582211011215. https://doi.org/10.1177/14604582211011215
