BIG DATA AND MACHINE LEARNING FOR HEALTHCARE RESOURCE ALLOCATION AND OPTIMIZATION

Abstract

Healthcare systems face increasing pressure to allocate limited resources effectively due to growing populations, rising healthcare costs, and the increasing complexity of medical needs. The advent of big data and machine learning (ML) technologies offers transformative potential for addressing these challenges. This paper explores the integration of big data and ML in healthcare resource allocation and optimization, focusing on how these technologies enable data-driven decision-making, improve operational efficiency, and enhance patient outcomes. We discuss applications such as predictive modeling for patient admissions, optimization of staffing, inventory management, and strategic planning. Additionally, challenges such as data privacy, interoperability, and algorithmic bias are analyzed, and potential solutions are proposed. This paper concludes with insights into future directions for research and practice in leveraging big data and ML to create more efficient and equitable healthcare systems.