Given the increasing availability of real-world health data, the development of credible analyses to help fill the knowledge gap between clinical trials and actual clinical practice is more important than ever. In this white paper, we discuss the expanding use of real-world evidence in healthcare decision-making, as well as the importance of clean and reliable data, robust disease identification algorithms, consistent patient attribution methodologies, and relevant outcomes metrics that can be generated from reliably reported data.
What is real-world evidence?
Real-world data (RWD) is data relating to patient health status or the delivery of healthcare collected during the course of clinical care and captured in a variety of data sources, such as administrative claims, electronic health records (EHRs), and product and disease registries. Real-world evidence (RWE) is generated through the analysis and/or synthesis of RWD to identify the effects of health interventions, such as benefits, risks, or resource uses that are not routinely collected during randomized control trials (RCTs).
To read the full white paper from Milliman, click here.
