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Beyond AI Adoption: Patient Safety Behavior and Clinical Reasoning in AI-Supported Clinical Clerkship Education - A Mixed Methods Study
ABSTRACT
Background:
Artificial intelligence (AI) is increasingly used during medical training, but research has focused largely on its adoption, perceptions, and educational use. Less is known about how AI-supported learning relates to clinical reasoning and safety-related behavior during clinical clerkship.
Objective:
This study examined factors associated with clinical reasoning, clinical decision confidence, and self-reported patient safety behavior in AI-supported clinical clerkship and explored selected findings within the clinical learning context.
Methods:
A sequential explanatory mixed-methods study was conducted across five private medical schools in Indonesia. Survey data from 217 clinical clerkship students were analyzed using partial least squares structural equation modeling. Semi-structured interviews were subsequently conducted with 5 students, 3 clinical supervisors, and 3 medical directors and analyzed using reflexive thematic analysis.
Results:
Ten of 12 hypothesized associations were supported. Clinical reasoning showed the largest direct association with self-reported patient safety behavior (β=.512; P<.001), followed by clinical decision confidence (β=.292; P<.001). AI over-reliance moderated the association between clinical decision confidence and patient safety behavior (β=.161; P=.012), but not the association between clinical reasoning and patient safety behavior (β=−.082; P=.119). Interviews described clinical reasoning as involving verification of AI-generated information, consideration of patient context and uncertainty, and consultation with supervisors. Participants also described the importance of supervision, patient safety learning, and institutional guidance for AI use.
Conclusions:
In AI-supported clinical clerkship, self-reported patient safety behavior was associated with clinical reasoning and clinical decision confidence. The findings extend the focus beyond AI adoption by showing how AI use is situated within clinical reasoning, supervision, and the educational conditions of clinical training.
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