Real-world data: a brief review of the methods, applications, challenges and opportunities

BMC Med Res Methodol. 2022 Nov 5;22(1):287. doi: 10.1186/s12874-022-01768-6.


Background: The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments.

Methods: We provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic.

Results and conclusions: Real-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.

Keywords: Artificial intelligence; Causal inference; Electronic health records; Machine learning; Real-world data (RWD); Real-world evidence (RWE).

Publication types

  • Review

MeSH terms

  • Delivery of Health Care* / methods
  • Humans
  • Information Storage and Retrieval*