Understanding AI’s Impact on Clinical Decision-Making: A Comparative Study of Simple and Complex Primary Care Scenarios

Abstract

Clinical decision-making is a complex cognitive process shaped by multiple factors, including cognitive biases, clinical context, and the integration of healthcare technologies. This thesis investigates how the introduction of artificial intelligence (AI)-enabled decision support tools influences clinical reasoning processes in primary care settings. Using Cognitive Work Analysis (CWA), Decision Ladder (DL) frameworks, and content analysis methods, this study qualitatively examines clinician decision-making behaviors across traditional electronic medical record (EMR) environments and AI-supported scenarios. Fourteen clinicians from Ontario, Canada, participated in scenario-driven sessions involving routine (uncomplicated urinary tract infections) and complex (mental health distress) cases. Analysis revealed distinct cognitive shortcuts, shifts, and reliance patterns influenced by AI. Specifically, AI systems reinforced heuristic-driven decisions for routine cases but introduced additional cognitive demands in complex scenarios due to information integration requirements. Visual emphasis in the DLs highlighted AI-driven cognitive shortcuts and behavior modifications. Limitations include scenario-driven constraints and a small, region-specific sample with similar EMR and AI experiences. Future research should explore mid-complexity scenarios, incorporate diverse clinician populations, and evaluate long-term effects of AI integration on clinical reasoning. This work contributes to understanding the nuanced interplay between cognitive processes and AI technology, informing user-centered design strategies for healthcare decision support systems.