Real-world research refers to studies in which the research data come from real healthcare settings, reflecting the actual diagnostic and treatment processes and the health status of patients under real conditions. The data sources for real-world research are very broad, and can be massive amounts of data generated by patients in a variety of channels, including outpatient visits, hospitalizations, tests, surgeries, pharmacies, wearables, social media, and so on. The types of data can be research data, such as patient surveys based on specific research purposes, patient registry studies, electronic medical records, and intervention studies (e.g., effectiveness randomized controlled trials) based on real healthcare conditions, or non-research data, such as data that are routinely monitored, recorded, and stored by a wide range of institutions (e.g., hospitals, healthcare insurance, civil affairs, and public ****health departments). It can also be non-research data, such as data from various organizations (e.g., hospitals, health insurance departments, civil affairs departments, and public health departments) that are routinely monitored, recorded, and stored as health-related data, such as hospital electronic medical records, health insurance claims databases, public health surveys and public health surveillance (e.g., monitoring of adverse drug events), and birth/death registration programs, among others.
When real-world research was first proposed, it was mainly aimed at the actual clinical diagnosis and treatment and medical management decision-making questions that could not be answered in the phase III clinical trials of new drugs and medical devices. Through the establishment of a set of methodological systems that are closer to the real conditions of the clinic, it is possible to answer the questions that could not be answered by the traditional clinical trials, such as the actual effects of drug treatment and the differences in the populations, the comparative effects of different drugs, and adherence to the treatments. The use of real-world research is widespread, and policymakers are increasingly concerned about the impact of real-world research on clinical outcomes. In order to better manage uncertainty in reimbursement decisions and to monitor the safety of medicines after marketing, policy makers (e.g., pharmacovigilance, healthcare administration, health insurance, etc.) need a large amount of research results that are close to the actual clinical and medical practice, as well as epidemiological data that are closer to natural environments, including data on adherence to existing treatment measures, compliance, and even costs, which has led to a wider application of real-world research. applications more broadly. With the advent of the big data era, technological innovations, the development of machine learning, and especially the widespread use of EDC (electronic medical record reporting), the strength of evidence and importance of large-sample size observational studies are beginning to change, even challenging randomized controlled trials in health policy decisions.
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