Problem
Research questions and friction points this paper is trying to address.
AI overreliance
clinical decision-making
decision-support systems
cognitive engagement
friction
Friction in AI-Assisted Clinical Decision-Making: A Case Study on The Role of Questions and 'What-if' Scenarios
🤖 AI Summary
This study addresses the problem of clinicians’ overreliance on AI-driven decision support systems (DSS) and their diminished cognitive engagement by introducing two friction mechanisms—data-driven questioning and “what-if” counterfactual analysis—and empirically evaluating their impact on clinician reflection and decision-making in authentic clinical tasks. Leveraging a prototype DSS replicating real-world clinical workflows, the research integrates in-situ interviews and qualitative feedback from seven domain experts, analyzed through human-computer interaction and cognitive science frameworks. This work presents the first comparative assessment of these friction mechanisms in a real-world setting, demonstrating that “what-if” analysis enhances care quality, while data-driven questioning effectively directs clinicians’ attention to critical information. The findings further propose a novel perspective: leveraging AI-induced friction as a pedagogical tool for training novice clinicians.
📝 Abstract
Clinical decision-making is augmented by decision-support systems (DSSs). To counter overreliance on DSSs, several methods have been proposed that create friction in order to promote cognitive engagement and reflection. In this paper, we investigate how two such forms of friction, namely data-driven questions and `what-if' analysis, are perceived by medical experts. For a real-world decision task, we replicated a DSS used in clinical practice and gathered clinicians' feedback on a prototype through in-situ interviews (n=7). Our findings suggest that while the questions were perceived as unhelpful for reflective thinking, they could serve as reminders to consider relevant information. Furthermore, inspecting `what-if' hypotheticals was found useful for potentially improving patient care. Clinicians saw our prototype as a promising training tool for novice clinicians. From the clinicians' feedback, we make recommendations for designing friction in work practices. Our work contributes to human-AI interaction research, which aims to encourage reflection to mitigate AI overreliance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
friction
what-if analysis
AI-assisted clinical decision-making
cognitive engagement
decision-support systems
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