Enhancing Data Quality and Real-World Evidence in Clinical Research

In this video interview clip, Cameron Breze, product manager, Inovalon, and Sujay Jadhav, CEO of Verana Health, discuss the importance of high-quality data and the role of real-world evidence and synthetic control arms in clinical research. They emphasize how inaccurate or incomplete data can compromise machine learning models and study outcomes. The conversation also explores how leveraging large, longitudinal real-world datasets can enhance efficiency and reduce the cost of traditional patient recruitment.

Breze: Quality data is something that is top of mind for everyone in the in the data technology space. Many people like to use the age-old adage of garbage in garbage out when it comes to machine learning models and a lot of the data modeling that's available where if you're feeding it with inaccurate information, you're going to get a result that really can't be transposed and moved and translated into anything that's really applicable and that ends up being a waste of institutional resources and not the best way to serve patients.

Jadhav: Historically, recruiting patients where you're not providing them the treatment, as you can imagine, is very, very expensive and very time consuming overall. I think that's the area where you look at synthetic control arms, and leveraging real-world data can help be a good proxy, or can help augment and allow you to compare and contrast to the treatment arm. The good thing with real-world data and leveraging it for these type of control arms is that the volume is very, very large, and the quality and the longitudinality of the data is pretty good as well.