The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.
AI
ML
artificial intelligence
automation
clinical decision support
decision making
decision support
decision support system
decision support systems
digitization
digitization of information
human-ML
human-ML interaction
human-ML interactions
human-automation interaction
human-automation interactions
human-computer interaction
human-computer interactions
machine learning
machine learning algorithm
machine learning algorithms
trust in automation
user interface
user-interface design
user-interface designs
Journal
JMIR human factors
ISSN: 2292-9495
Titre abrégé: JMIR Hum Factors
Pays: Canada
ID NLM: 101666561
Informations de publication
Date de publication:
01 Aug 2024
01 Aug 2024
Historique:
received:
30
01
2024
revised:
09
05
2024
accepted:
24
05
2024
medline:
2
8
2024
pubmed:
2
8
2024
entrez:
2
8
2024
Statut:
epublish
Résumé
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.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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.
Identifiants
pubmed: 39092520
pii: v11i1e56924
doi: 10.2196/56924
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e56924Informations de copyright
© Omer Katzburg, Michael Roimi, Amit Frenkel, Roy Ilan, Yuval Bitan. Originally published in JMIR Human Factors (https://humanfactors.jmir.org).