A Systematic Review of Healthcare Big Data

Abstract

Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study on healthcare big data based on the systematic literature review (SLR) protocol. In particular, the present study highlights some valuable research aspects on healthcare big data, evaluating 34 journal articles (between 2015 and 2019) according to the defined inclusion-exclusion criteria. More specifically, the present study focuses to determine the extent of healthcare big data analytics together with its applications and challenges in healthcare adoption. Besides, the article discusses big data produced by these healthcare systems, big data characteristics, and various issues in dealing with big data, as well as how big data analytics contributes to achieve a meaningful insight on these data set. In short, the article summarizes the existing literature based on healthcare big data, and it also helps the researchers with a foundation for future study in healthcare contexts.

Use of artificial intelligence in healthcare delivery in India

“…With start-ups and large tech companies offering AI solutions for healthcare challenges the use of AI in healthcare in India is exponentially increasing (58). Though developing nations like India lag the superpowers in fundamental research and resources, they enjoy advantages in the form of a vast engineering workforce, a burgeoning startup scene, and a large pool of data waiting to be tapped (59). India also has the entrepreneurial spirit to help businesses derive value from real-time data and the ambition to carve a niche for themselves in an increasingly AI-driven world (60).…”

Database Supported Long-term Management of Chronic Diseases – Data from the German Disease Management Programmes as a Source for Continuing Medical Education

“…Digitisation in health care has started a new era allowing for - centralised, durable, and, ideally, interoperable documentation and storage of patient data in electronic health records [ 4–6 ] - continuous monitoring of individual patients’ physiologic data, e.g. by wearable sensors [ 7 ] - support of care by digital devices like apps [ 8 ] or other information services [ 9 ] - big data to feed artificial intelligence [ 10 ] etc. …”

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

“…The concept of BD given by Doug Laney cited in Refs. [8,11,12] is branded by volume, velocity, and variety, acknowledged as 3Vs. However, most studies [1,7,10] expand the concept of Doug Laney to five key characteristics (5 Vs), namely, volume, velocity, variety, value, and veracity (see Fig.…”