Leveraging AI-Driven Simulation Training to Mitigate Defensive Medicine in Clinical Practice

Abstract

Defensive medicine remains widespread and expensive across global healthcare systems. The evolving healthcare landscape and defensive medicine practices are undergoing significant transformations with groundbreaking implementations of artificial intelligence (AI). Defensive medicine involves ordering tests, procedures, treatment, visits, or avoiding high-risk patients to reduce legal exposure and vulnerability, which increases healthcare costs, compromises quality standards, increases administrative processes, and lowers patient care. Even in the aftermath of tort reform and various legislative initiatives, several physicians continue to partake in defensive medicine practices, driven by fear of lawsuits and professional scrutiny. While emerging AI technologies encompass possibilities and obstacles that can complicate clinical decision-making practices, leveraging data-driven stimulated-based training and evidence-based practices may present promising outcomes that boosts clinical confidence, reduces fear, improves patient engagement, optimizes care delivery, and enables physicians to implement real-world clinical scenarios in a safe and controlled environment to minimize healthcare errors amd expenses, making it possible to focus more on patient-centered care instead of healthcare burdens. This article focuses on the practicality of how AI-powered simulation-based training may provide opportunities for personalized feedback, adjustment of settings in real-time based on performance measures, and ascertain defensive tendencies by analyzing patterns, including pinpointing early detections of high-risk situations that could result in medical and legal ramifications.