Improving Hospital Operations and Resource Management in Vietnam Through Big Data Analytics

Abstract

Hospitals encounter a multitude of operational challenges that impede efficient resource management. Persistent issues such as prolonged patient wait times, suboptimal allocation of resources, and inefficiencies within the supply chain have significant ramifications on the quality and financial aspects of healthcare provision. This paper critically examines the application of big data analytics as a strategic solution to address these challenges and optimize both operational processes and resource utilization within Vietnamese healthcare institutions. The utilization of big data techniques, including predictive modeling, data mining, and optimization algorithms, emerges as a pivotal approach to derive actionable insights from the substantial volume of data generated in hospitals on a daily basis. This analytical capability empowers hospital administrators to anticipate patient volumes, determine optimal staffing levels, streamline clinical pathways, and improve the efficiency of supply chain processes. To underscore the tangible benefits of big data analytics, the paper presents three specific case studies illustrating how leading hospitals in Vietnam have successfully employed these techniques to enhance scheduling, bed management, and inventory control. Through a comprehensive analysis of these examples, the paper asserts that big data analytics holds significant promise for Vietnamese hospitals. By harnessing the power of data-driven insights, hospitals can not only mitigate costs but also elevate the quality of care delivered to patients. The adoption of big data analytics emerges as a transformative strategy with the potential to fortify the long-term sustainability of healthcare institutions in Vietnam, positioning them to navigate the complexities of modern healthcare delivery.