Overcoming Medical Overuse with AI Assistance: An Experimental Investigation

This study explores the potential of Artificial Intelligence (AI) in mitigating medical overuse, specifically analyzing physicians' decision-making processes in a controlled experimental environment. Medical overuse, defined as unnecessary interventions that may cause harm to patients and increase healthcare costs, is a significant problem in both developed and developing healthcare systems. By integrating AI tools that provide evidence-based recommendations, this study aims to understand the extent to which AI can influence physician behavior and reduce overtreatment.

This experimental design consists of a lab-in-the-field setting within a medical school, and we used a two-by-three factorial design to manipulate monetary incentives and the availability of AI assistance between different groups of medical students. The study collects data on the potential causes of overtreatment and tests defensive medical behaviors. We built on previous experiments by conducting a field experiment with medical students, excluding the effects of physician self-learning as well as ordinal effects, examining AI-assisted consultations as a between-subject variable, and exploring the effects of medical students' overuse rates and clinical decision-making accuracy in familiar and unfamiliar consultation scenarios. Key findings of the study include the frequency of overtreatment, diagnostic accuracy, defensive medical behaviors, and adoption of AI recommendations.

The findings are expected to provide a broader contribution based on previous research, providing policymakers and healthcare administrators with more nuanced insights about the value of integrating AI technology in healthcare settings to potentially improve healthcare efficiency and patient safety.