Reviewing the Promise and Use of Real-World Data

Real-world data (RWD) and the real-world evidence (RWE) generated is used more and more in research and healthcare decision-making. A newly-published article in BMC Medical Research Methodology serves as an RWD primer, covering the different types of RWD, where it comes from, analysis methods, and how insights derived from it can be implemented.

According to authors Fang Liu and Panagiotakos Demosthenes, “First, RWD are observational as opposed to data gathered in a controlled setting. Second, many types of RWD are unstructured (e.g., texts, imaging, networks) and at times inconsistent due to entry variations across providers and health systems. Third, RWD may be generated in a high-frequency manner (e.g., measurements at the millisecond level from wearables), resulting in voluminous and dynamic data. Fourth, RWD may be incomplete and lack key endpoints for an analysis given that the original collection is not for such a purpose. For example, claims data usually do not have clinical endpoints; registry data have limited follow-ups.”

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(Source: BMC Medical, November 5^th, 2022)