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Autonomous Healthcare Control System (AHCS)

Oct 2026 · IGI Global eBooks · 10 references
Healthcare Operations and Scheduling Optimization

Abstract

Today's healthcare systems represent complex adaptive systems that involve stochastic demand, tight coupling between subsystems, and nonlinearity of resource constraints. This paper proposes the Autonomous Healthcare Control System (AHCS), which brings together such approaches as stochastic state space models, Markov Decision Process, queuing theory, epidemic forecasting, and optimization through reinforcement learning. AHCS treats hospital operations as a high dimensional stochastic system and leverages the digital twin framework to simulate numerous scenarios of hospital operations. AHCS allows optimizing clinical performance, ensuring sustainable labor force, increasing supply chain resilience, and improving efficiency. In addition, the proposed approach incorporates elements of explainable AI, fairness aware learning, and human in the loop approach. Simulation results for the case of pandemic-related surge show that the proposed system is capable of decreasing the probability of ICU overload by approximately 40% and reducing the recovery period by 50%.

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