Abstract

The increasing complexity of public health systems demands faster, data-informed decisions to enhance infrastructure planning, crisis response, and service delivery. This review explores how next-generation Business Intelligence (BI) systems—powered by AI, machine learning, and real-time analytics—are revolutionizing decision-making processes within government health infrastructure. Traditional BI tools often fall short in managing the volume, velocity, and variety of health-related data generated from diverse sources such as electronic health records, telemedicine platforms, and IoT-enabled medical devices. In contrast, modern BI ecosystems facilitate proactive decision-making through predictive dashboards, automated alerts, and scenario modeling, enabling administrators to optimize resource allocation and performance tracking. By integrating with national health information systems and leveraging cloud-native architectures, these BI solutions support agile governance, improved transparency, and data interoperability across departments. This paper systematically reviews the technological advancements, implementation challenges, and future prospects of BI-driven decision cycles in the public health sector, aiming to provide actionable insights for digital transformation in government healthcare.