This episode explores the systems and methods used for tracking a drug's real-world performance after it has been approved and is available to patients. We discuss key data sources such as registries, electronic health records, and insurance claims databases. The episode highlights the importance of post-marketing surveillance (PMS) in understanding how a drug behaves in a diverse population, outside the controlled environment of clinical trials.

We delve into various analytical approaches used to gather and analyze post-market data, including data mining and signal detection techniques. The conversation emphasizes how this continuous monitoring helps to identify rare side effects, long-term impacts, and variations in drug effectiveness across different patient groups. We also briefly tease the use of Artifical Intelligence and machine learning.