Agentic AI system for hospital management

In modern-day hospital administration, there are many problems like long waiting time for patients, poor scheduling of physicians, lack of coordination between departments, among others. As such, this research suggests an Agentic AI System for Hospital Administration that utilizes a multi agent architecture and intelligence embedding to automate healthcare processes. This model entails a Patient Interaction Agent, a Care Coordination Agent, a Doctor Interaction Agent, and a Bed Allocation Agent, whose main roles are facilitating patients' entry and sorting out the patients' needs, allocation of doctors to attend to patients, coordination within the hospital, and hospital resource management, respectively.Through reinforcement learning, decision-making based on information processing, and synchronization, the framework becomes more adaptive and scalable. It is expected that due to intelligence distribution and intelligent decision-making, this model will minimize total waiting time, improve physicians' usage, and enhance scheduling of patients. Empirical analysis shows that hospital operations exhibit significant performance superiority of agentic models over the traditional models.Modern hospital administration faces major challenges such as prolonged patient waiting times, inefficient physician scheduling, poor interdepartmental coordination, and suboptimal resource utilization. Traditional hospital management systems often rely on static and centralized approaches that lack adaptability in dynamic healthcare environments. To address these limitations, this paper proposes a cooperative Multi-Agent Reinforcement Learning (MARL)-based intelligent hospital administration framework for workflow optimization and resource management. The proposed system consists of four collaborative agents: a Patient Interaction Agent for patient triage, a Doctor Scheduling Agent for dynamic physician allocation, a Care Coordination Agent for interdepartmental communication, and a Bed Allocation Agent for adaptive resource management. The agents utilize decentralized decision-making and reinforcement learning to optimize hospital operations in real time. The framework is evaluated using a hospital simulation environment and compared with conventional scheduling and management approaches. The results show that the effectiveness of the proposed system model is able to complete the tasks at a rate of 82.50\% in a period of 5.12 seconds with a success rate in handling queries of 79.15\%. Furthermore, the system attains a coordination efficiency of 87.50\%, indicating effective collaboration among agents, while maintaining an agent conflict rate of 5.00\%, ensuring stable and interference-free operations. The framework records a low communication overhead of 0.88, reflecting efficient inter-agent communication, and achieves a convergence rate of 96.4\%, demonstrating learning stability and adaptability.This indicates that the model is efficient, scalable, and adaptable in managing intelligent hospitals.