HW 04-1 USEFULNESS OF BIG DATA IN CLINICAL RESEARCH.

Big data indicates the large and ever-increasing volumes of data adhere to the following 4Vs: volume (ever-increasing amount), velocity (quickly generated), variety (many different types), veracity (from trustable sources). The last decade has seen huge advances in the amount of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. The routine operation of modern health care systems also produces an abundance of electronically stored data on an ongoing basis as a byproduct of clinical practice. Over the last decades, the technology and economics of data collection, storage and analysis have changed dramatically. Today, utilizing data is far less costly than ever before. To the extent that data can be collected and analyzed comprehensively and at scale, there is no fundamental need any more to work only with small samples under strict assumptions (1). Though randomized clinical trial (RCT) has been regarded as the golden standard for establishing clinical evidences, it has several limitations as follows: elderly and patients with comorbidity are under-represented, RCT has limited ability to detect rare and chronic toxicities, especially those that occur in patients with comorbidity or emerge after completion of the trial, often powered to detect a clinically modest effect size that may not apply to less selected patients (2). Conversely, population-based observational studies using big data have several strengths: provide insight into delivery of care in routine practice to all patients, including elderly and those with comorbidity, provide information to guide future knowledge translation, can provide evidence of effectiveness of new therapies in the general population, large samples provide the opportunity to study rare disease for which RCTs are not possible, can provide insight into short-and long-term toxicity in routine practice, can address questions that have not, and will not, be evaluated in an RCT; Recently, rare drug adverse events have been detected in observational studies using big data, which had never been detected in previous RCTs (e.g. olmesartan-associated enteropathy (3), azithryomycin and risk of cardiovascular death (4), incretin and heart failure (5)). Reproducing the results of previous RCTs using big data is also active for establishing the evidence in real-world and in many different countries. The benefits from intensive blood-pressure control was reconfirmed through the analysis of national administrative claim data in Korea (6). In many countries, researchers and administrative health institutions struggle to apply standardized data model to harmonize and collect medical data from various and heterogeneous sources. However, various barriers such as system heterogeneity, different formats, variation in human subject protection rules over the world, trust building, contracting and coordination and study governance policies, hampers it. Recently, distributed research network (DRN), such as Observational Health Data Sciences and Informatics (OHDSI) or National Patient-Centered Clinical Research Network (PICORNET) is getting popular for clinical data partners over the world. The DRN provides network-wide results by running the same analysis program for participating organizations using the same data structure, called a Common Data Model (CDM), and then combining the summarized results through the network (7). The OHDSI announced that more than 680 million of patients' data over the 12 countries have been converted into their CDM format, and had performed a monumental research on the treatment pathway of chronic diseases using 250 million of patients data from 4 countries within the network (8). Recently, the American National institutes of Health announced to launch the precision medicine initiative cohort program, which would build a large research cohort of one million or more Americans with comprehensive medical information including medical histories, genetic information and lifestyles. The government expects this comprehensive medical information would lead to medical innovation along with the 4 industrial revolution. In conclusion, big data analysis will serve as a key player to improve health, by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care.