Fast and Slow Thinking for Vascular Surgeons and Dual-Process Strategies for Improving Clinical Decision Making and Patient Care

Surgical dictum rests on the belief that a careful, conscientious, well-read surgeon may be insulated from error. The cognitive science of decision-making indicates otherwise. In this context, we bring Daniel Kahneman's dual-process framework to a vascular surgical audience and argue that many associated decision-making errors are not failures of knowledge or intelligence but rather predictable failures of cognition. Human judgment operates through two interacting systems. System 1 is the fast, automatic, pattern-based processing that underlies much of what we call surgical expertise. System 2 is the slow, effortful, rule-following analysis required for preoperative risk stratification, evidence-based guideline application, and complex or redo surgical anatomy. A well-trained System 1 is indispensable yet systematically vulnerable to biases such as Representativeness, Anchoring, Availability, Affect, and Confirmation, and we provide relevant surgical scenarios. We challenge the assumption that deliberate System 2 analysis reliably corrects these errors, examining its characteristic failures. For vascular surgeons, familiarity with this framework is a foundational step toward enhanced patient care and safety, more efficient health systems, impactful surgical training, and quality improvement. Diverse and complex surgical teams may provide appropriate external scaffolds to counteract bias. Artificial Intelligence (AI) has the potential to narrow the regulation gaps between System 1 and System 2, leading to improved surgical decision-making. However, a steadfast recognition that current research reporting standards may be insufficiently standardized and that large language and machine-learning models themselves exhibit human-like cognitive biases will be important for using AI to improve care for surgical patients and to optimize training paradigms for future surgeons.