Department of Health Policy and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
General Intensive Care Unit, Rambam Medical Center, Haifa, Israel.
JMIR Hum Factors. 2024 Aug 1;11:e56924. doi: 10.2196/56924.
The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms.
The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system.
A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects.
Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05).
Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.
计算能力的指数级增长和信息的日益数字化极大地推动了机器学习（ML）研究领域的发展。然而，ML 算法通常被认为是“黑箱”，这引发了不信任。在医疗领域，错误可能导致致命后果，因此从业者可能特别不愿意信任 ML 算法。
本研究旨在探讨用户界面设计特征对重症监护医生对基于 ML 的临床决策支持系统的信任的影响。
共有 47 名来自重症监护专业的医生在基于 ML 的模拟系统中对 3 例菌血症患者进行了评估。根据信息相关性和交互性的组合，测试了模拟的三种条件。参与者对系统的信任通过他们对系统预测的一致性和实验后的问卷调查来评估。应用线性回归模型来衡量效果。
参与者对系统预测的一致性并未根据实验条件而有所不同。然而，在实验后的问卷调查中，更高的信息相关性评分和交互性评分与更高的感知信任系统相关（两者均 P<.001）。在用户界面上对 ML 算法的特征进行明确的可视化呈现导致参与者的信任度降低（P=.05）。
基于 ML 的临床决策支持系统的用户界面设计应考虑信息相关性和交互性特征，以增强重症监护医生的信任。本研究揭示了信息相关性、交互性和人机 ML 交互中的信任之间的联系，特别是在重症监护环境中。
