Real World Evidence Study: A New Path Towards Drug Safety

  • Rahul V Jodh Sudhakarrao Naik Institute of Pharmacy, Pusad (MS) India
Keywords: Real-world Data, Real World Evidence, Health Care, Clinical trial, Safety, effectiveness


Real-world data (RWD) allows researchers to recognize the effectiveness of treatments in everyday situations. This data type captures factors that cannot be measured in clinical settings. Real-world data can be obtained from observational studies, clinical trials, or surveys conducted in real-world settings. 

The design of real-world evidence (REW) studies is crucial to developing new health technologies. Planning for real-world evidence studies allows for seamless integration of health economics and outcomes research. Accurate World Data provides an alternative source of clinical evidence. It can also improve drug development by identifying unmet medical needs. However, real-world data is not a perfect substitute for controlled clinical trials. Unlike traditional RCTs, real-world evidence studies look at treatments in standard settings. They are based on data gleaned from electronic health records (EHR) and patient registries. These data are de-identified, which makes them an excellent source for RWE studies. T real-world data represents ninety-five percent of all patient data compared to clinical trials. Real-world studies can accompany clinical trials by providing additional insights on medication use. Real-world evidence studies can overcome some of the limitations associated with clinical trials. Real-world data can be collected from a diverse cross-section of society and be more helpful in determining whether a new treatment will work in the real world.

Real-world evidence can be invaluable in many ways, not the least of which is helping researchers develop new medicines. These data come from studies of existing drugs in the real world, not controlled trials. Although real-world evidence may be noisier, it can also inform future trials. The FDA acknowledges the value of RWE and has established criteria for assessing its quality. High-quality, real-world data must be transparent, unbiased, and codified to industry standards. It is a need for time to explore the real-world data and use it to find a new treatment.