Overcoming Medical Overuse with AI Assistance: An Experimental Investigation

Abstract

This study examines the role of Artificial Intelligence (AI) in reducing medical overtreatment, a critical healthcare challenge that increases costs and patient risks. We conducted two lab-in-the-field experiments at a medical school and a hospital tested a novel medical prescription task under three incentive schemes: Flat (constant pay), Progressive (pay increases with treatment quantity), and Regressive (penalties for overtreatment). Our findings demonstrate that AI assistance significantly reduced overtreatment, achieving reductions of up to 62% (medical students) and 82% (physicians) under specific schemes. The accuracy rate of diagnosis improved with AI across all treatments, with gains between 14% and 37%. AI adoption was notable, with half of medical students and one-third of physicians incorporating AI suggestions. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI’s potential to mitigate non-monetary incentives and enhance social welfare. These results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.

Note:
Funding Information: Lijia Wei acknowledges support from National Science Foundation China (72173093), and the Center for Behavioral and Experimental Research (CBER) at Wuhan University. Lian Xue acknowledges support from the Research Funds for Youth Academic Team in Humanities and Social Sciences of Wuhan University (413000425).

Declaration of Interests: The authors declare no conflict of interest.

Ethics Approval Statement: This study was approved by the ethics committee at Wuhan University (Center for Behavioral and Experimental Research). All the participants involved in the study have given their consents to take part in the study upon enrollment.