Application of Big Data analysis in gastrointestinal research

Table 4 Examples of studies on gastrointestinal bleeding and/or proton pump inhibitor research by utilization of large healthcare datasets

Gastrointestinal bleeding and/or proton pump inhibitors
Country/Region	Database	Area of research	Sample size	Design, statistical methods and 3V	Application
Taiwan, China	Taiwan National Health Insurance Database (NHID)	PUD	403567	Nationwide retrospective cohort study	Effect of H. pylori therapy and PPIs on PUD
		Wu et al[58], 2009
				Volume, Velocity and Variety
		PUD	32235	Nationwide retrospective cohort study	Risk of rebleeding from PUD in ESRD patients
		Wu et al[95], 2011
				Volume, Velocity and Variety
		PPIs	6552	Nationwide retrospective cohort study	Effect of clopidogrel and PPIs on ACS
				Volume, Velocity and Variety
		Wu et al[59], 2010
South Korea	Korean Health Insurance Review and Assessment Service (HIRA)	PPIs	59233	Nationwide retrospective cohort study	Effect of PPIs on thrombotic risk
		Kim et al[96], 2019
				Volume, Velocity and Variety
Hong Kong, China	Clinical Data Analysis and Reporting System (CDARS)	Dabigatran	5041	Territory-wide retrospective cohort study	Risk factors for dabigatran-associated gastrointestinal bleeding
		Chan et al[62], 2015
				Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; PUD: Peptic ulcer disease; H. pylori: Helicobacter pylori; PPIs: Proton pump inhibitors; ESRD: End-stage renal disease; ACS: Acute coronary syndrome.