Technology and Innovation in Healthcare: Adoption of AI and Predictive Analytics in Hospital Management

Abstract

This review investigates how artificial intelligence (AI) and predictive analytics are reshaping hospital management by enhancing clinical decision-making, operational efficiency, and equitable healthcare delivery. People are increasingly applying AI-driven models to forecast patient demand, optimize workforce planning, improve diagnostic accuracy, and expand telemedicine, especially in the post-pandemic era. When implemented responsibly, predictive tools across healthcare systems can reduce readmissions, strengthen patient outcomes, and support resource utilization. Successful adoption is enabled by robust digital infrastructure, committed leadership, and cross-disciplinary collaboration, yet barriers persist, including fragmented IT systems, interoperability gaps, algorithmic bias, and workforce skill shortages. Ethical and governance challenges centered on transparency, data protection, and accountability remain pivotal to sustainable clinical integration. Comparative insights reveal divergent adoption pathways: high-income countries leverage mature digital ecosystems, while low- and middle-income contexts are innovating with resource-sensitive applications to address workforce constraints and expand access. By synthesizing theoretical frameworks and global case studies, this review underscores that responsible AI adoption can accelerate hospital transformation. Policy recommendations emphasize that it is time for standardized validation, strengthened data ecosystems, inclusive telehealth expansion, and institutional capacity-building to advance efficiency, resilience, and equity in healthcare systems.