TY  - JOUR
AU  - Katzburg, Omer
AU  - Roimi, Michael
AU  - Frenkel, Amit
AU  - Ilan, Roy
AU  - Bitan, Yuval
PY  - 2024
DA  - 2024/8/1
TI  - The Impact of Information Relevancy and Interactivity on Intensivists’ Trust in a Machine Learning–Based Bacteremia Prediction System: Simulation Study
JO  - JMIR Hum Factors
SP  - e56924
VL  - 11
KW  - user-interface design
KW  - user-interface designs
KW  - user interface
KW  - human-automation interaction
KW  - human-automation interactions
KW  - trust in automation
KW  - automation
KW  - human-computer interaction
KW  - human-computer interactions
KW  - human-ML
KW  - human-ML interaction
KW  - human-ML interactions
KW  - decision making
KW  - decision support system
KW  - clinical decision support
KW  - decision support
KW  - decision support systems
KW  - machine learning
KW  - ML
KW  - artificial intelligence
KW  - AI
KW  - machine learning algorithm
KW  - machine learning algorithms
KW  - digitization
KW  - digitization of information
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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). Conclusions: 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. 
SN  - 2292-9495
UR  - https://humanfactors.jmir.org/2024/1/e56924
UR  - https://doi.org/10.2196/56924
DO  - 10.2196/56924
ID  - info:doi/10.2196/56924
ER  -