Healthcare Workforce Management: Leveraging AI for Staff Scheduling and Optimization

Abstract

This research investigates the application of Artificial Intelligence (AI) in optimizing healthcare workforce management, focusing majorly on staff scheduling and operational efficiency. The study makes use of four AI algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Random Forest (RF), to research and optimize staff scheduling in healthcare. The real-world healthcare data was used as input, with factors such as scheduling accuracy, computation efficiency, and adaptation to demand changes. The models were then evaluated in terms of their accuracy. The highest accuracy achieved by the Random Forest model was 92.6%, followed by the Genetic Algorithm and Particle Swarm Optimization with an accuracy of 88.4% and 85.3%, respectively. Simulated Annealing reached an acceptable accuracy of 83.2%. These findings also were compared to related work in manufacturing and military contexts, in which the increasing importance of AI in workforce management optimization in industries is indicated. Challenges such as data privacy and algorithmic fairness were also seen to provide an aspect of the ethics surrounding AI applications. As evidenced by the study, AI-driven workforce management solution improves scheduling efficiency, decreases operational costs, and enhances service delivery, therefore increasing sustainability in healthcare systems