Big data and real-world data-based medicine in the management of hypertension

Big data has been a hot topic in medical and healthcare research. Big data in healthcare is considered to comprise massive amounts of information from various sources, including electronic health records (EHRs), administrative or claims data, and data from self-monitoring devices. Biomedical research has also generated a significant portion of big data relevant to healthcare. Other large datasets arise from cohorts that are recruited and followed on the basis of specific questions, although such research questions may later be expanded to enable other investigations. While the availability of big data offers many possibilities for an improved understanding of disease and treatment, the need for careful and productive use of statistical concepts should be kept in mind. Patient data routinely collected via electronic means are called real-world data (RWD) and are becoming common in healthcare research. RWD and big data are not synonymous with each other, but the two terms seem to be used without distinction with respect to observational studies. In this article, we review hypertension-related papers that use big data or RWD. There are many other sources of big data or RWD that are not covered here, each of which may pose special challenges and opportunities. While randomized clinical trials (RCTs) are considered to be the criterion standard for generating clinical evidence, the use of real-world evidence (RWE) to evaluate the efficacy and safety of medical interventions is gaining interest. On-going efforts to make use of RWD to generate RWE for regulatory decisions, as well as the challenges confronted, including reliability (quality) and relevance (fitness for purpose) of data, will also be addressed.