Title: A Dynamic Resource Optimization Model for Enhancing Patient Flow and Reducing Wait Times in U.S. Hospitals
Authors: Bamidele Samuel Adelusi, Damilola Osamika, MariaTheresa Chinyeaka Kelvin-Agwu, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, Nura Ikhalea
Volume: 9
Issue: 4
Pages: 25-47
Publication Date: 2025/04/28
Abstract:
Efficient patient flow and minimized wait times are critical indicators of quality healthcare delivery in hospitals across the United States. However, increasing patient volumes, limited resources, and inefficient scheduling systems often lead to overcrowded emergency departments, delayed admissions, and suboptimal patient outcomes. This study proposes a Dynamic Resource Optimization Model (DROM) designed to enhance patient flow and reduce wait times through the integration of real-time data analytics, predictive modeling, and adaptive scheduling. The model dynamically reallocates hospital resources-such as beds, medical personnel, and equipment-based on real-time patient inflow, acuity levels, and historical demand patterns. Using a hybrid framework that combines queuing theory, linear programming, and machine learning algorithms, the model predicts patient arrival rates and optimizes resource distribution to address bottlenecks. A simulation-based approach was employed using data from selected U.S. hospitals to evaluate the model's effectiveness under varying operational scenarios. Results demonstrate significant improvements in patient throughput, with up to a 30% reduction in emergency department wait times and a 25% increase in resource utilization efficiency. The model also supports decision-making for hospital administrators by generating actionable insights that align staffing and resource deployment with fluctuating demand. In addition, it incorporates feedback loops that enable continuous learning and adaptation to evolving healthcare dynamics. This study contributes to the growing body of healthcare operations research by offering a scalable and adaptable framework for resource management that can be customized across hospital departments, including emergency, outpatient, and surgical units. The findings underscore the potential of integrating advanced analytical techniques into hospital operations to improve patient satisfaction, reduce operational costs, and enhance overall system responsiveness. Future work will focus on integrating electronic health records (EHRs) and expanding the model to include community health metrics for predictive population health planning. The Dynamic Resource Optimization Model represents a strategic step toward smarter, data-driven hospital management in the era of digital healthcare transformation.