Kleine Anne-Kathrin, Kokje Eesha, Lermer Eva, Gaube Susanne
Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany.
Technical University of Applied Sciences Augsburg, Augsburg, Germany.
JMIR Hum Factors. 2023 Jul 12;10:e46859. doi: 10.2196/46859.
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.
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.
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.
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.
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.
尽管为开发便于临床护理使用的人工智能（AI）应用程序付出了越来越多的努力，但由于个人、组织和系统层面的障碍，其应用仍然有限。关于在精神卫生保健中使用AI系统的意愿的研究有限。
本研究旨在通过基于技术接受与使用统一理论，考察心理学专业学生和早期从业者使用两种特定的人工智能支持的精神卫生工具的意愿的预测因素，以填补这一空白。
这项横断面研究纳入了206名心理学专业学生和接受培训的心理治疗师，以考察他们使用两种人工智能支持的精神卫生保健工具的意愿的预测因素。第一种工具就心理治疗师对动机性访谈技巧的遵循情况提供反馈。第二种工具使用患者语音样本得出情绪分数，治疗师可将其用于治疗决策。在测量扩展的技术接受与使用统一理论的变量之前，向参与者展示了工具功能机制的图形描述。总共指定了2个结构方程模型（每种工具1个），其中包括预测工具使用意愿的直接路径和中介路径。
感知有用性和社会影响对使用反馈工具（P<.001）和治疗建议工具（感知有用性，P=.01；社会影响，P<.001）的意愿有积极影响。然而，信任与两种工具的使用意愿均无关。此外，考虑所有预测因素时，感知易用性与使用意愿无关（反馈工具），甚至呈负相关（治疗建议工具，P=.004）。此外，观察到认知技术准备情况与使用反馈工具的意愿之间存在正相关（P=.02），人工智能焦虑与使用反馈工具（P=.001）和治疗建议工具（P<.001）的意愿之间存在负相关。
研究结果揭示了精神卫生保健中人工智能技术应用的一般驱动因素和工具依赖驱动因素。未来的研究可以探索影响精神卫生保健中人工智能支持工具应用的技术和用户群体特征。
