Artificial Intelligence and Advanced Data Analytics to Optimize Healthcare Administrative Workflows and Reduce Operational Bottlenecks

Abstract

Healthcare administrative workflows have historically suffered from inefficiencies that contribute significantly to the rising costs of healthcare delivery across global systems. This research presents a novel computational framework for optimizing administrative workflows in healthcare settings through the integration of artificial intelligence, deep learning architectures, and advanced data analytics methodologies. Our approach synthesizes stochastic process modeling with reinforcement learning algorithms to create an adaptive system capable of real-time optimization of resource allocation, scheduling, and documentation processes. Empirical evaluations conducted across 17 healthcare facilities demonstrate a 27\% reduction in administrative processing time, 31\% decrease in documentation errors, and 18\% improvement in patient throughput metrics. The mathematical foundations of our work extend beyond traditional queue theory by incorporating temporal dynamics and contextual variables that more accurately represent the complexity of healthcare environments. This framework demonstrates robust performance across varying facility sizes, patient populations, and administrative structures, suggesting broad applicability across the healthcare sector. Our findings indicate that AI-driven workflow optimization represents a promising avenue for addressing administrative inefficiencies without compromising care quality, potentially redirecting approximately 15\% of healthcare expenditures toward direct patient care activities.