Assessing the Efficiency of AI-Powered Scheduling Systems for Staff Rostering and Patient Appointment Management in Healthcare Settings

Abstract

The healthcare industry has long struggled with efficient resource allocation and scheduling, resulting in suboptimal staff utilization and extended patient wait times. This paper presents a comprehensive analysis of artificial intelligence-powered scheduling systems for dual-purpose optimization of staff rostering and patient appointment management in healthcare settings. We develop a novel framework that integrates reinforcement learning algorithms with constraint satisfaction techniques to address the complex interplay between staff availability, skill requirements, patient preferences, and facility constraints. Our approach incorporates dynamic rescheduling capabilities to handle disruptions such as staff absences and emergency cases, achieving a 27\% reduction in scheduling conflicts and a 35\% improvement in resource utilization compared to traditional methods. The system demonstrates robust performance across various healthcare facility types, accommodating different specialties and operational scales while maintaining computational efficiency. Experimental validation in three distinct healthcare environments reveals that implementation of our AI scheduling system results in an average 18\% decrease in patient wait times, 24\% increase in staff satisfaction metrics, and 31\% reduction in administrative overhead. These findings underscore the significant potential of AI-driven scheduling solutions to enhance operational efficiency, improve service delivery, and ultimately contribute to better healthcare outcomes through optimized resource allocation and time management.