Optimization of Big Data Analytics in Healthcare by Overcoming Security, Interoperability, and Cost Challenges

Abstract

Big Data Analytics (BDA) is revolutionizing the delivery of health care services by enabling organizations extract values from huge volumes, varieties, and velocities of information from sources such as EHRs, Wearable Devices, and Medical Imaging. It optimizes decisions concerning clinical practices, creates individualized treatment plans, and optimizes patient’s care. Advanced and newer technologies like Machine learning, and artificial intelligence have greatly boosted the functionality of predictive analytics especially in disease identification and prevention and more personal touch with patients. However, there are major challenges in incorporating BDA into the healthcare organizations More. The issues facing implementation include data security and protection of their privacy, integration of the different systems, data quality, and issues to do with biases in algorithms, as well as the high costs of setting up the systems. These issues are even more significant at the present time because various healthcare organizations tended to implement BDA solutions to achieve the intended objectives within the healthcare domain such as enhanced patients’ overall health and organizational performance. This article examines these modern issues, and enunciates some of the following major problem statements of BDA in the healthcare context. To this end, this paper presents approaches and practical solution models for enhancing BDA solutions concerning efficiency, integration, and security challenges. To our knowledge, this work will serve as an initial attempt at systematically summarizing the challenges and best practices for BDA integration in healthcare organizations to inform the development of future research and applications.