Human-AI-Interaction Group, Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich, Germany.
Department of Liberal Arts and Sciences, Technical University of Applied Sciences Augsburg, Augsburg, Germany.
J Med Internet Res. 2024 Aug 5;26:e57224. doi: 10.2196/57224.
Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited.
This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence.
The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs.
The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy.
This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.
人工智能支持的临床决策支持系统（AI-CDSS）有潜力改善医疗保健结果，但它们在医疗保健从业者中的采用仍然有限。
本荟萃分析根据统一接受和使用技术理论（UTAUT）确定影响医疗保健从业者使用 AI-CDSS 意愿的预测因素。根据现有实证证据，还检查了其他预测因素。
使用电子数据库、前向搜索、会议计划和个人通信进行文献检索，产生了 7731 个结果，其中 17 项（0.22%）研究符合纳入标准。使用随机效应荟萃分析、相对权重分析以及荟萃分析调节和中介分析来检验相关预测变量与使用 AI-CDSS 意愿之间的关系。
荟萃分析结果支持将 UTAUT 应用于使用 AI-CDSS 的意图背景。结果表明，绩效期望（r=0.66）、努力期望（r=0.55）、社会影响（r=0.66）和便利条件（r=0.66）与使用 AI-CDSS 的意愿呈正相关，与 UTAUT 的预测一致。荟萃分析还确定了积极的态度（r=0.63）、信任（r=0.73）、焦虑（r=-0.41）、感知风险（r=-0.21）和创新性（r=0.54）作为其他相关预测因素。信任是整体上最具影响力的预测因素。调节分析的结果表明，随着年龄的增长，社会影响与使用意愿之间的关系变得较弱。此外，对于诊断 AI-CDSS，努力期望与使用意愿之间的关系强于将诊断和治疗建议相结合的设备。最后，便利条件与使用意愿之间的关系通过绩效和努力期望得到中介。
本荟萃分析有助于根据扩展的 UTAUT 模型理解使用 AI-CDSS 的意愿预测因素。需要更多的研究来证实所确定的关系，并通过确定相关调节因素来解释观察到的效应大小变化。研究结果对设计和实施医疗保健从业者培训计划具有重要意义，以减轻 AI-CDSS 在实践中的采用。
