Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists

Kleine, Anne-Kathrin ; Kokje, Eesha ; Lermer, Eva und Gaube, Susanne (2023): Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists. Cross-sectional Study. In: JMIR Human Factors, Bd. 10, e46859

Abstract

Background:

Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care.

Objective:

This study aimed to address this gap by examining the predictors of psychology students’ and early practitioners’ intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology.

Methods:

This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools’ functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions.

Results:

Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed.

Conclusions:

The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.

• Dokumententyp: Zeitschriftenartikel
• Keywords: artificial intelligence; mental health; clinical decision support systems; Unified Theory of Acceptance and Use of Technology; technology acceptance model
• Fakultät: Psychologie und Pädagogik > Department Psychologie > Wirtschafts- und Organisationspsychologie
• Themengebiete: 300 Sozialwissenschaften > 300 Sozialwissenschaft, Soziologie
• ISSN: 2292-9495
• Sprache: Englisch

Literaturliste

Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov 2020 Jan 27:1-14 [CrossRef]

Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017 Nov 01;19(11):e367 [https://www.jmir.org/2017/11/e367/] [CrossRef] [Medline]

Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LK. An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit). Int J Med Inform 2008 Jun;77(6):386-398 [CrossRef] [Medline] Garvey KV, Thomas Craig KJ, Russell R, Novak LL, Moore D, Miller BM. Considering clinician competencies for the implementation of artificial intelligence-based tools in health care: findings from a scoping review. JMIR Med Inform 2022 Nov 16;10(11):e37478 [https://medinform.jmir.org/2022/11/e37478/] [CrossRef] [Medline]

Shachak A, Kuziemsky C, Petersen C. Beyond TAM and UTAUT: future directions for HIT implementation research. J Biomed Inform 2019 Dec;100:103315 [https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(19)30234-5] [CrossRef] [Medline] Hsiao JL, Chen RF. Critical factors influencing physicians' intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model. BMC Med Inform Decis Mak 2016 Jan 16;16(1):3 [https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0241-3] [CrossRef] [Medline]

Kumar A, Mani V, Jain V, Gupta H, Venkatesh VG. Managing healthcare supply chain through artificial intelligence (AI): a study of critical success factors. Comput Ind Eng 2023 Jan;175:108815 [https://europepmc.org/abstract/MED/36405396] [CrossRef] [Medline]

Wiljer D, Salhia M, Dolatabadi E, Dhalla A, Gillan C, Al-Mouaswas D, et al. Accelerating the appropriate adoption of artificial intelligence in health care: protocol for a multistepped approach. JMIR Res Protoc 2021 Oct 06;10(10):e30940 [https://www.researchprotocols.org/2021/10/e30940/] [CrossRef] [Medline]

Camacho J, Zanoletti-Mannello M, Landis-Lewis Z, Kane-Gill SL, Boyce RD. A conceptual framework to study the implementation of clinical decision support systems (BEAR): literature review and concept mapping. J Med Internet Res 2020 Aug 06;22(8):e18388 [https://www.jmir.org/2020/8/e18388/] [CrossRef] [Medline]

Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989 Sep;13(3):319-340 [CrossRef]

Venkatesh V, Thong JY, Xu X. Unified theory of acceptance and use of technology: a synthesis and the road ahead. J Assoc Inf Syst 2016 May 1;17(5):328-376 [https://papers.ssrn.com/abstract=2800121] [CrossRef]

Arfi WB, Nasr IB, Kondrateva G, Hikkerova L. The role of trust in intention to use the IoT in eHealth: application of the modified UTAUT in a consumer context. Technol Forecast Soc Change 2021 Jun;167:120688 [CrossRef]

Fan W, Liu J, Zhu S, Pardalos PM. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann Oper Res 2018 Mar 19;294(1-2):567-592 [CrossRef]

Lin HC, Tu YF, Hwang GJ, Huang H. From precision education to precision medicine: factors affecting medical staff's intention to learn to use AI applications in hospitals. Educ Technol Soc 2021 Jan;24(1):123-137 [https://www.jstor.org/stable/26977862]

Zhai H, Yang X, Xue J, Lavender C, Ye T, Li JB, et al. Radiation oncologists' perceptions of adopting an artificial intelligence-assisted contouring technology: model development and questionnaire study. J Med Internet Res 2021 Sep 30;23(9):e27122 [https://www.jmir.org/2021/9/e27122/] [CrossRef] [Medline]

Tran AQ, Nguyen LH, Nguyen HS, Nguyen CT, Vu LG, Zhang M, et al. Determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians. Front Public Health 2021 Nov 26;9:755644 [https://europepmc.org/abstract/MED/34900904] [CrossRef] [Medline]

Gado S, Kempen R, Lingelbach K, Bipp T. Artificial intelligence in psychology: how can we enable psychology students to accept and use artificial intelligence? Psychol Learn Teach 2021 Aug 12;21(1):37-56 [CrossRef]

Cummins R, Ewbank MP, Martin A, Tablan V, Catarino A, Blackwell AD. TIM: a tool for gaining insights into psychotherapy. In: Proceedings of the World Wide Web Conference. 2019 Presented at: WWW '19: The Web Conference; May 13-17, 2019; San Francisco, CA, USA URL: https://doi.org/10.1145/3308558.3314128 [CrossRef]

Hirsch T, Soma C, Merced K, Kuo P, Dembe A, Caperton DD, et al. "It's hard to argue with a computer:" investigating psychotherapists' attitudes towards automated evaluation. DIS (Des Interact Syst Conf) 2018 Jun;2018:559-571 [https://europepmc.org/abstract/MED/30027158] [CrossRef] [Medline]

Tanana MJ, Soma CS, Srikumar V, Atkins DC, Imel ZE. Development and evaluation of ClientBot: patient-like conversational agent to train basic counseling skills. J Med Internet Res 2019 Jul 15;21(7):e12529 [https://www.jmir.org/2019/7/e12529/] [CrossRef] [Medline]

Imel ZE, Pace BT, Soma CS, Tanana M, Hirsch T, Gibson J, et al. Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. Psychotherapy (Chic) 2019 Jun;56(2):318-328 [CrossRef] [Medline]

Huang Z, Epps J, Joachim D, Chen M. Depression detection from short utterances via diverse smartphones in natural environmental conditions. In: Proceedings of the Interspeech 2018. 2018 Presented at: Interspeech 2018; Sep 2-6, 2018; Hyderabad, India URL: https://doi.org/10.21437/Interspeech.2018-1743 [CrossRef]

Rønnestad MH, Ladany N. The impact of psychotherapy training: introduction to the special section. Psychother Res 2006 May;16(3):261-267 [CrossRef]

ieso online therapy homepage. ieso. URL: https://www.iesohealth.com/why-typed-therapy [accessed 2023-05-05]

Calati R, Courtet P. Is psychotherapy effective for reducing suicide attempt and non-suicidal self-injury rates? Meta-analysis and meta-regression of literature data. J Psychiatr Res 2016 Aug;79:8-20 [CrossRef] [Medline]

Jan A, Meng H, Gaus YF, Zhang F. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Trans Cogn Dev Syst 2018 Sep;10(3):668-680 [CrossRef]

Karam ZN, Provost EM, Singh S, Montgomery J, Archer C, Harrington G, et al. Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. Proc IEEE Int Conf Acoust Speech Signal Process 2014 May;2014:4858-4862 [https://europepmc.org/abstract/MED/27630535] [CrossRef] [Medline]

Huang Z, Epps J, Joachim D, Sethu V. Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection. IEEE J Sel Top Signal Process 2020 Feb;14(2):435-448 [CrossRef]

Sokero TP, Melartin TK, Rytsälä HJ, Leskelä US, Lestelä-Mielonen PS, Isometsä ET. Prospective study of risk factors for attempted suicide among patients with DSM-IV major depressive disorder. Br J Psychiatry 2005 Apr 02;186(4):314-318 [CrossRef] [Medline]

Sonde health homepage. Sonde Health. URL: https://www.sondehealth.com [accessed 2023-05-05]

Venkatesh V. Adoption and use of AI tools: a research agenda grounded in UTAUT. Ann Oper Res 2021 Jan 19;308(1-2):641-652 [CrossRef]

Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q 2003 Sep;27(3):425-478 [CrossRef] Student mental health AI tools. Open Source Fremwork. URL: https://osf.io/fqdzb [accessed 2023-06-26]

Owusu MK, Owusu A, Fiorgbor ET, Atakora J. Career aspiration of students: the influence of peers, teachers and parents. J Educ Soc Behav Sci 2021 Apr 29;34(2):67-79 [CrossRef]

Aafjes-van Doorn KA, Kamsteeg C, Bate J, Aafjes M. A scoping review of machine learning in psychotherapy research. Psychother Res 2021 Jan 29;31(1):92-116 [CrossRef] [Medline]

Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021 Jun 18;20(2):154-170 [https://europepmc.org/abstract/MED/34002503] [CrossRef] [Medline]

Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019 Oct 29;17(1):195 [https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1426-2] [CrossRef] [Medline]

Seufert S, Guggemos J, Sailer M. Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: the current situation and emerging trends. Comput Human Behav 2021 Feb;115:106552 [https://europepmc.org/abstract/MED/32921901] [CrossRef] [Medline]

Oppenheimer DM, Meyvis T, Davidenko N. Instructional manipulation checks: detecting satisficing to increase statistical power. J Exp Social Psychol 2009 Jul;45(4):867-872 [CrossRef]

Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study. BMC Med Educ 2021 Feb 18;21(1):112 [https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-021-02546-6] [CrossRef] [Medline]

Dimitrova M. Of discovery and dread: the importance of work challenges for international business travelers' thriving and global role turnover intentions. J Organ Behav 2020 Feb 03;41(4):369-383 [CrossRef]

Venkatesh V, Thong JY, Chan FK, Hu PJ, Brown SA. Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf Syst J 2011 Nov;21(6):527-555 [CrossRef]

Chai CS, Wang X, Xu C. An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics 2020 Nov 23;8(11):2089 [CrossRef]

Fietta V, Zecchinato F, Stasi BD, Polato M, Monaro M. Dissociation between users’ explicit and implicit attitudes toward artificial intelligence: an experimental study. IEEE Trans Human Mach Syst 2022 Jun;52(3):481-489 [CrossRef] Liang Y, Lee SA. Fear of autonomous robots and artificial intelligence: evidence from national representative data with probability sampling. Int J Soc Robot 2017 Mar 8;9(3):379-384 [CrossRef]

Sindermann C, Sha P, Zhou M, Wernicke J, Schmitt HS, Li M, et al. Assessing the attitude towards artificial intelligence: introduction of a short measure in German, Chinese, and English language. Künstl Intell 2020 Sep 23;35(1):109-118 [CrossRef]

Brady GM, Truxillo DM, Bauer TN, Jones MP. The development and validation of the Privacy and Data Security Concerns Scale (PDSCS). Int J Select Assess 2020 Sep 29;29(1):100-113 [CrossRef]

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2022. Rosseel Y. lavaan: an R package for structural equation modeling. J Stat Soft 2012;48(2):1-36 [CrossRef] Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling Multidiscipl J 1999 Jan;6(1):1-55 [CrossRef]

Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociol Methods Res 2016 Jun 29;21(2):230-258 [CrossRef]

Scharf F, Pförtner J, Nestler S. Can ridge and elastic net structural equation modeling be used to stabilize parameter estimates when latent factors are correlated? Struct Equ Model Multidiscipl J 2021 Jun 15;28(6):928-940 [CrossRef]

Hurst JL, Good LK. Generation Y and career choice: the impact of retail career perceptions, expectations and entitlement perceptions. Career Dev Int 2009;14(6):570-593 [CrossRef]

Luyckx K, Klimstra TA, Duriez B, Van Petegem S, Beyers W. Personal identity processes from adolescence through the late 20s: age trends, functionality, and depressive symptoms. Soc Dev 2013 Jun 04;22(4):701-721 [CrossRef]

Lin HC, Tu YF, Hwang GJ, Huang H. From precision education to precision medicine. Educ Technol Soci 2021 Jan;24(1):123-137 [https://www.jstor.org/stable/26977862]

Kwak Y, Ahn JW, Seo YH. Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions. BMC Nurs 2022 Sep 30;21(1):267 [https://bmcnurs.biomedcentral.com/articles/10.1186/s12912-022-01048-0] [CrossRef] [Medline]

Allen D, Karanasios S, Slavova M. Working with activity theory: context, technology, and information behavior. J Am Soc Inf Sci 2011 Feb 18;62(4):776-788 [CrossRef]

DeSanctis G, Poole MS. Capturing the complexity in advanced technology use: adaptive structuration theory. Organ Sci 1994 May;5(2):121-147 [CrossRef]