![]() 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. In what follows, we provide a brief review on the type and sources of RWD (Section 2) and the common models and approaches to utilize and analyze RWD (Section 3), and discuss the challenges and opportunities of using RWD for evidence-based decision making (Section 4). For example, during the COVID-19 pandemic, RWD are used to generate or aid the generation of real-world evidence (RWE) on the effectiveness of COVID-19 vaccination, to model localized COVID-19 control strategies, to characterize COVID-19 and flu using data from smartphones and wearables, to study behavioral and mental health changes in relation to the lockdown of public life, and to assist in decision and policy making, among others. ![]() The increasing accessibility of RWD and the fast development of artificial intelligence (AI) and machine learning (ML) techniques, together with rising costs and recognized limitations of traditional trials, has spurred great interest in the use of RWD to enhance the efficiency of clinical research and discoveries and bridge the evidence gap between clinical research and practice. The wide usage of the internet, social media, wearable devices and mobile devices, claims and billing activities, (disease) registries, electronic health records (EHRs), product and disease registries, e-health services, and other technology-driven services, together with increased capacity in data storage, have led to the rapid generation and availability of digital RWD. Per the definition by the US FDA, real-world data (RWD) in the medical and healthcare field “are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”. 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. 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. ![]() 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. 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. 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.
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