Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union

Abstract

The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to inform decisions about treatment effectiveness and safety. First, we provide an overview of RWD and RWE. Data quality frameworks (DQFs) in the US and EU were examined, including their dimensions and subdimensions. There is some convergence of the conceptual DQFs on specific assessment criteria. Second, we describe a list of screening criteria for assessing the quality of RWD sources. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). In conclusion, this paper provides a perspective on the utilization of RWD and RWE in healthcare decision-making. It covers the types and uses of RWD, data quality frameworks (DQFs), regulatory landscapes, and the potential impact of RWE, as well as the challenges and opportunities for the greater leveraging of RWD to create credible RWE.

Keywords

• real-world data
• real-world evidence
• data types
• data discoverability
• data quality
• data privacy