Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose \textbf{ShortageSim}, addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84\%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.

A new report from the Milken Center for Public Health suggests "panic buying" of medications by patients and providers caused drug shortages. TRIPLE THREAT – Amid COVID, flu and RSV, households and hospitals stockpiled meds, says new research. MORE MPOX – CDC warns of new case clusters in Chicago. UNREALISTIC IDEALS – Here's how AI defines the "perfect body." A new study by The Bulimia Project, a Brooklyn, New York-based website that publishes content and research related to eating disorders, investigated how AI perceived the "ideal" body based on social media data.

Discussions about the application of artificial intelligence (AI) in healthcare often span multiple areas, most commonly about making more accurate diagnoses, identifying at-risk populations, and better understanding how individual patients will respond to medicines and treatment protocols. To date, there has been relatively little discussion about practical applications of AI to improve medication management across the care continuum, an area this article will address. What's the first thing that comes to mind when someone mentions prescription drugs in the United States? In poll after poll, the high and rising costs of medications are American voters' top healthcare-related issue. This concern is well founded.