Real world data (RWD) and real world evidence (RWE) have been increasingly used in health care decision-making since the passage of the 21st Century Cures Act on December 9, 2016, which requires the FDA to develop a framework and guidance for evaluating RWD and RWE to support approvals of new drugs or devices, or new indications for previously approved drugs, and to support post-approval studies for monitoring safety and adverse events for regulatory decision-making. Whereas pharmaceutical companies use RWD and RWE to support clinical development activities and to provide evidence to inform health technology assessment (HTA) decisions, the healthcare community uses RWD and RWE to develop guidelines and decisions to support medical practice and to assess treatment patterns, costs and outcomes of interventions. Although high-speed computing tools and machine learning algorithms have been conveniently applied to RWD, there are still substantial challenges in deriving RWE from RWD and using the RWE in healthcare decision-making. We discuss how recent advances in statistical science can be combined with domain knowledge to address these challenges.