MedAgentOps: A Distributed Multi-Agent Collaborative Reasoning Infrastructure Based on Memory-Augmented Graph Reasoning Network for Emergency Department Flow Forecasting and Resource Optimization
DOI:
https://doi.org/10.66238/fsrma101Keywords:
Multi-Agent Systems, Emergency Department Forecasting, Distributed Artificial Intelligence Infrastructure, Collaborative Reasoning, Resource Optimization, Graph Neural Networks, Reinforcement Learning, Healthcare InformaticsAbstract
Emergency departments (EDs) in modern hospitals face persistent challenges including unpredictable patient influx, resource scarcity, and inefficient operational coordination, which significantly degrade service quality and response efficiency. To address these issues, this study proposes MedAgentOps, a distributed multi-agent collaborative reasoning infrastructure built upon a Memory-Augmented Graph Reasoning Network (MAGR-Net) for emergency flow forecasting and resource optimization. The proposed framework integrates four key components: Dynamic Temporal Graph Fusion (DTGF) for modeling heterogeneous temporal dependencies in emergency-related factors, a Shared Medical Memory (SMM) module for consolidating short-term operational states and long-term historical knowledge, an Agent Graph Reasoning Module (AGRM) for adaptive coordination among forecasting, scheduling, and resource allocation agents, and a Multi-Objective Proximal Policy Optimization (MOPPO) strategy for optimizing operational efficiency under multiple constraints. These modules jointly enable end-to-end reasoning from patient arrival prediction to real-time resource scheduling in a unified multi-agent system. Experimental results on a real-world emergency department dataset show that the proposed framework achieves a Mean Absolute Percentage Error (MAPE) of 8.73%, outperforming all baseline models significantly. In addition, MedAgentOps reduces average patient waiting time by approximately 17.9% compared with the strongest baseline (GNN-RL) while improving resource utilization to 91.3%. These results confirm the effectiveness of integrating multi-agent reasoning with memory-augmented graph learning for intelligent healthcare optimization.
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