Data Analytics For Healthcare Improvement: Develop Systems For Analyzing Large Health Data Sets To Improve Patient Outcomes, Manage Pandemics, And Optimize Healthcare Delivery

Abstract

The integration of data analytics into healthcare systems has revolutionized the management, delivery, and quality of healthcare services. This study systematically reviews and synthesizes findings from 105 peer-reviewed articles to explore the transformative role of data analytics in enhancing diagnostic accuracy, improving operational efficiency, managing public health crises, and addressing health disparities. Machine learning and artificial intelligence emerged as significant tools in supporting early diagnosis of chronic and acute diseases, improving clinical decision-making, and streamlining hospital workflows. Real-time analytics systems have been instrumental in managing healthcare resources, monitoring hospital capacities, and responding effectively to pandemics such as COVID-19. The integration of big data analytics with electronic health records (EHRs) has optimized resource allocation, reduced redundant processes, and lowered operational costs. Additionally, emerging technologies such as blockchain and Internet of Things (IoT) devices have addressed challenges related to secure data sharing, interoperability, and real-time health monitoring. Despite these advancements, the review identifies critical gaps, including the lack of long-term evaluations of analytics systems and scalability challenges in low-resource settings. These findings emphasize the need for robust infrastructure, ethical frameworks, and scalable solutions to ensure the widespread adoption and equitable use of healthcare analytics.