J Korean Med Sci.  2019 Jan;34(4):e28. 10.3346/jkms.2019.34.e28.

Proceed with Caution When Using Real World Data and Real World Evidence

Affiliations
  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • 2Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • 3Division of Biomedical Informatics, Systems Biomedical Informatics Research Center, Seoul National University College of Medicine, Seoul, Korea. juhan@snu.ac.kr

Abstract

Clinical studies can be conducted to gather real world evidence (RWE) not available from randomized controlled trials, providing new information and knowledge. Although the concept of RWE emerged relatively recently, numerous clinical studies are utilizing it. However, many researchers are engaging in trial and error that may not overcome the various biases that occur in electronic medical record (EMR)-based RWE studies. While RWE can reflect the real world, there are still limitations to its acceptance. There are many hurdles in using RWE and solutions must be explored. Results based on RWE may be overestimated and it can be difficult to derive good quality results. This paper discusses data quality management, direct chart review, sample size, study design, and the interpretation of EMR-based RWE. More specifically, this paper shares the experience of the various hurdles that occur when conducting RWE studies and discusses the easy-to-false errors. RWE is still in the developmental stage and numerous aspects of RWE use remain unclear. Nonetheless, despite its many limitations, increasing use of RWE is still anticipated. This will require continued experience and effort in using RWE, as well as upgrading RWE research through the accumulation of information on such experiences and efforts.

Keyword

Real World Data; Real World Evidence; Electronic Medical Record; Randomized Control Trial; Clinical Research; Cohort Study

MeSH Terms

Bias (Epidemiology)
Cohort Studies
Data Accuracy
Electronic Health Records
Sample Size
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