Clinical Data Collection: Best Practices And Latest Trends

Collecting clinical trial data can prove to be a demanding endeavor, but it is arguably the most crucial part of the study process. And to do it well, clinical data managers must master every aspect of the data collection and management journey, including determining what type of data to collect, deciding how to collect it, and determining how to store and analyze it.

To start, we discuss the role of clinical data manager (CDM) and how it’s evolving as AI/ML technologies continue advances in data analysis and as CDMs become increasingly responsible for tangential areas like technology selection and project management.

Then, we touch on each of these clinical data collection and analysis topics — from identifying the right data provider to understanding the role of data collection in DCTs to leveraging real-world data (RWD) for optimal trial performance. We explore the use of sensors as data collection devices and understand how changes to data collection processes can reduce bias in precision medicine.

With the advent of decentralized clinical trials and the increased fascination and exploration with technology-assisted processes bringing new data — and more of it — the importance of intentional data collection cannot be overstated.