AI Based Workflow Optimization in Hospitals Using Constraint Satisfaction and Machine Learning Algorithms

This paper focuses on enhancing the efficiency of operations in a hospital based on constraint satisfaction problems (CSP) and popular machine learning techniques. The purpose of this paper is to improve the increase in organizational performance by managing the problems and constantly revolving issues in the operation of hospitals such as scheduling. The categorization includes positing hospital workflow issues into CSPs and using machine learning algorithms for solving these CSPs. Using various ML algorithms, including classification ones, the improvement of various decision-making processes regarding, for instance, patient scheduling, the distribution of resources, or staff assignment is ensured. The above proposed system was tested using hypothetical hospital data to determine the effects of proposed system, and it is evident that it will improve the flow of patients, cut down the waiting time and efficiently use various resources. These outcomes provide implications for future studies and opportunities for using AI and CSP-based approaches in modernizing hospital management systems, such as using more intensive AI algorithms and implementing the method in real-time.