The journey of a new drug from lab to patient is incredibly long, expensive, and fraught with risk. A significant part of that challenge lies in clinical trials: identifying the right patient populations, recruiting them efficiently, and monitoring the drug’s safety and effectiveness in a real-world setting.
For years, the process has relied on manual site-by-site feasibility assessments and slow, cumbersome recruitment. But in 2024, the convergence of Artificial Intelligence and rich, real-world data from routine clinical care is fundamentally changing the paradigm. At IOMED, we are seeing this transformation firsthand as our pharmaceutical partners use our federated data network to accelerate and de-risk their clinical development pipelines.
From Feasibility to Recruitment
Traditionally, a pharmaceutical company planning a new trial would have to survey dozens of potential hospital sites, a process that could take months just to determine if a sufficient number of eligible patients even existed.
Today, they can get that answer in days. Using our platform, they can run complex feasibility queries across our entire network of hospitals. They can model different inclusion/exclusion criteria and see, in near real-time, how those changes impact the size of the available patient pool. This allows them to design more effective, realistic trial protocols from the outset.
Once the trial is designed, the same data can be used to accelerate recruitment. Our hospital partners can use the same queries to identify potentially eligible patients within their own EHR systems, dramatically speeding up the process of finding and enrolling participants.
The Future: Post-Market Surveillance and Beyond
The role of real-world data doesn’t end when a drug is approved. Monitoring a drug’s long-term safety and effectiveness across a broad population is a critical, and often challenging, part of the lifecycle.
A live, federated network of hospital data provides an ideal platform for this kind of post-market surveillance. We can help our partners monitor for rare side effects, compare the effectiveness of their drug to other treatments in a real-world setting, and gather the evidence needed for label expansions or new indications.
Embedding real-world data and AI into the clinical development pipeline is rapidly moving from a “nice-to-have” experiment to a competitive necessity. It’s leading to smarter trial designs, faster recruitment, and ultimately, a more efficient path from promising compound to life-changing treatment.