Healthcare Interoperability with AI Agents
Keywords:
Healthcare AI, Real Time, Interoperability, Decision Support, Reinforcement Learning, Multi Agent, Clinical Systems, COVID Modeling, Risk Awareness, Data Integration, Semantic Models, Uncertainty Modeling, Decision Policies, Workflow Optimization, Clinical Workflows, System Performance, Message Systems, Pipeline Architecture, Latency Reduction, Adaptive Systems.Abstract
Adaptive Real-Time Healthcare Interoperability and Decision Optimization Applied multi-agent reinforcement learning to jointly optimize adaptive real-time data integration and uncertainty-aware decision support across multiple healthcare agents. A simulated healthcare environment for a COVID-19 treatment workflow served as a multi-agent reinforcement learning environment. Adaptive real-time interoperability and decision-making were achieved. An implemented clinical testbed demonstrated improved test results and indicated the potential for improved clinical workflows. Adaptive real-time interoperability and adaptive risk-aware decision support will improve clinical workflows and support decision quality, speed, and safety while reducing the effects of gaps in the data provided by other agents, thereby facilitating agents with different data modelling paradigms to interact more efficiently and also enhancing clinical workflows.
Multiple innovations improve the usefulness and impact of multi-agent reinforcement learning on healthcare agents. The fundamental goal is to enhance the adaptive real-time interoperability and risk-aware decision-making of multiple healthcare agents in a simulated healthcare ecosystem modelled on a COVID-19 treatment workflow. Performance indications suggest that the framework achieves these goals. Automated and concurrent updates to both adaptive real-time semantic support and uncertainty-aware decision policies enable joint optimisation through multi-agent learning. The integration of message-based data transfer systems with pipeline architecture further enables multi-agent learning to address latency in the triggering of agents’ decision policies.
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