ABSTRACT
This chapter provides an overview of some of the commonly used big data repositories employed in critical care–related study and describes select big data analytics–enabled promising initiatives involving point-of-care applications and knowledge discovery, as applied to critical care medicine. It outlines future directions in bedside and research applications. The chapter addresses key contemporary and future challenges facing big data analytics. A recent unprecedented growth in published critical care studies was fueled in part by broader use of large secondary data sets to answer novel questions. Building on the complementary strengths of randomized controlled trials (RCTs) and big data, a recent proposal by Angus advocates the fusion of the two to meet the goal of a self-learning health system. The proposed approach includes electronic health record (EHR)-based patient screens for trial eligibility to broaden and ease enrollment, and capitalize on existing data capture of EHR for study-specific data acquisition, to ease the burden of data collection.
