Session (CTI)

Real-world data (RWD) is increasingly used in healthcare research, but only when it’s extracted and analyzed with appropriate methodological rigor. Too often, organizations assume data alone will generate insight. However, data alone cannot generate meaningful insights without appropriate study design and analytical approaches. Without the right expertise, study design, and methodology, even large or complex datasets can lead to a misinterpretation of findings.
This session will feature a discussion on how human expertise applied to RWD informs the intentional and methodical decision making required to generate meaningful and reliable insights. Drawing on experience in applying real-world data within healthcare research and regulatory contexts, Dr. Brown will discuss a practical framework for aligning the research question, the data source, and the analytical method. When these elements are matched to the intended use, real-world data can support credible evidence generation and healthcare decision-making.
Building on this framework, the session will highlight the opportunities, challenges, and key considerations involved in determining whether a dataset is truly fit for purpose within the context of the research question and intended use, along with common pitfalls that can undermine the trustworthiness of study findings. Attendees will gain practical considerations for selecting data sources more strategically and designing studies that are both credible and relevant for decision-making, supporting more reliable and appropriate use of real-world data.
Sponsored by TriNetX