USABILITY OF BIG DATA ANALYTICS WITHIN CLINICAL DECISION SUPPORT SYSTEMS

Abstract

The adoption of electronic health record systems and other digital technologies such as Magnetic Resonance Imaging (MRI) techniques, automated laboratory tests, and body sensors have brought the era of big data technology into the healthcare industry. The use of big data technologies has the potential to provide medical organizations with powerful tools to gather and analyze large data volumes and to use this information to their advantage. However, special skills, systems, and capabilities are required to be able to analyze and extract useful information from big data. The objective of this paper was to explore the literature regarding the usability of big data analytics in supporting medical decision making. This information will guide healthcare organizations in understanding how they can adopt the utilization of big data to enhance decision making. A systematic review of evidence-based research articles from within the past five years was used to gather information in regards to this topic. The articles were derived from scientific databases. Based on the literature review, big data and big data analytics has the capability to improve decision making in the healthcare sector, predict disease outbreaks as well as the trends and patterns of the spread of such diseases, predict occurrence of medical phenomenon's such as hospital readmission, reoccurrence of diseases, and risk of infection among others. Moreover, big data analytics has the capability to help healthcare organizations to streamline processes within the healthcare setting. However, the process of integrating big data analytics in the healthcare setting follows distinct phases. Healthcare organizations also have to consider the challenges associated with adopting big data analytics. Nevertheless, based on the literature, big data analytics has the capability to improve delivery systems and outcomes within the healthcare sector.