Health Big Data Analytics: A Technology Survey

TLDR

Health data are rapidly expanding in volume, complexity, and variety—from electronic medical records and imaging to genomic and IoT sources—making comprehensive analysis increasingly challenging. The paper seeks to identify and address the obstacles that hinder effective mining of health data so that meaningful insights can be generated to improve patient outcomes. It reviews key challenges, data sources, analytical techniques, and emerging technologies, offering a simplified, do‑it‑yourself guide for building integrated health analytics applications.

Abstract

Because of the vast availability of data, there has been an additional focus on the health industry and an increasing number of studies that aim to leverage the data to improve healthcare have been conducted. The health data are growing increasingly large, more complex, and its sources have increased tremendously to include computerized physician order entry, electronic medical records, clinical notes, medical images, cyber-physical systems, medical Internet of Things, genomic data, and clinical decision support systems. New types of data from sources like social network services and genomic data are used to build personalized healthcare systems, hence health data are obtained in various forms, from varied sources, contexts, technologies, and their nature can impede a proper analysis. Any analytical research must overcome these obstacles to mine data and produce meaningful insights to save lives. In this paper, we investigate the key challenges, data sources, techniques, technologies, as well as future directions in the field of big data analytics in healthcare. We provide a do-it-yourself review that delivers a holistic, simplified, and easily understandable view of various technologies that are used to develop an integrated health analytic application.