Clinical Decision Support Systems Powered by Big Data Analytics in Diagnosis and Treatment Planning

Big data analytics is playing an increasingly vital role in medical research and care delivery by enabling the management, analysis, and assimilation of vast amounts of fragmented and structured data generated by healthcare systems. However, some fundamental issues with the big data paradigm still hinder its adoption and research progress in this field. Healthcare generates significant data due to record keeping, regulatory requirements, and patient care, which are increasingly being digitized. In this study, we examine the challenges medical research faces in image, signal, and genomics-based analytics and explore potential research opportunities that could transform healthcare delivery. We also discuss a recent study that combines multimodal data from various sources with vast medical data. The research aims at describing the hurdles and bottlenecks that healthcare organizations might face during adopting and implementing of large-scale data analytics initiatives. Through the defining of these difficulties, the analysis aims at building up the expertise and analytic tools that are needed for a successful big data initiative in a way that stakeholders may be zeroed with the barriers and overcome them efficiently. Along with the tackling of challenges, analytics use in healthcare nowadays poses some threats. The investigation, which provides case studies and real-life examples, has been conducted in order to demonstrate the power big data analytics can have on healthcare delivery, patient outcomes, and organizational efficiency.