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As biopharmaceutical companies and regulatory bodies look toward adopting EHR-derived real-world data to complete studies more efficiently and affordably, one major concern that often arises is what has come to be known as “data missingness.”
This is an excerpt from a longer post that originally appeared on Going Digital: Behavioral Health Tech.
Real-world data (RWD) are becoming increasingly critical to clinical research. The FDA has put forth definitions surrounding RWD, as well as issued guidance around its use in research emphasizing the principle of data being “fit-for-purpose”—selecting the data needed to answer the question at hand. Meanwhile, stakeholders engaged in clinical development have increasingly recognized that RWD will enable them to conduct studies faster, at a lower cost, and often, with a more representative and diverse population.
However, not all RWD is fit-for-purpose—that is, captured and stored in such a way that the data is ready to address the question at hand. In order to move forward with using RWD in a way that is efficient and effective, we need to build a shared understanding of the different types of data within the broad umbrella that is RWD and make clear which type of RWD is fit-for-purpose for a specific question.
I recently led a team of researchers in a large-scale observational study, which was accepted and published by The Lancet Psychiatry. To our knowledge, our study was the first to study the impact of early clinical trajectory across multiple psychiatric diagnoses.
Our mission is to build the world’s richest real-world evidence platform for behavioral health to transform clinical research and care delivery. Our NeuroBlu Database delivers value to our customers by providing meaningful clinical information that helps to produce evidence that could not be easily generated otherwise.
This year at the American Society of Clinical Psychopharmacology’s annual conference, I had the privilege to organize and chair a panel discussion on challenges and opportunities in leveraging real-world evidence to inform the development of new treatments in behavioral health.
