Deep Reinforcement Learning for Optimizing Healthcare Resource Allocation

Abstract

Healthcare resource allocation is a critical challenge faced by healthcare systems globally. The complexity of this task necessitates innovative solutions to ensure optimal allocation of resources such as medical staff, equipment, and facilities. Deep Reinforcement Learning (DRL) has emerged as a promising approach for addressing such complex optimization problems. This research proposes a novel DRL framework for optimizing healthcare resource allocation, leveraging its ability to learn from interactions with the environment to make informed decisions. The framework is designed to adapt to dynamic healthcare environments, optimizing resource allocation in real-time. The effectiveness of the proposed framework is demonstrated through simulations and comparisons with traditional methods, highlighting its potential to enhance healthcare resource management.