Collaborative Multi-Agent RL for Adaptive Healthcare Decision-Making
Keywords:
Multi-agent reinforcement learning, autonomous data exchange, clinical decision support, real-time interoperability, healthcare data standards, privacy, data quality, evaluation metrics. .Abstract
Autonomous, real-time exchange and adaptation of healthcare data is critical for optimizing clinical decisions while respecting trust, safety, and privacy concerns. An approach using multi-agent reinforcement learning (MARL) is proposed, enabling patient-specific, inference-based, and standard-compliant data-sharing policies between agents positioned at junctures in patient journeys. Data agents support decision agents and policy agents—the former learning to optimize care pathways from historical patient data, while the latter define safe, accurate, and reliable operation zones for responsiveness-complexity trade-offs. Prioritizing clinical efficacy and local communication, independent MARL minimizes safety risk; centralised Joint-Action Learning mitigates non-stationarity in sample-efficient subproblems; hybrid coordination strategise scale. The system architecture remains modular, resilient, and audit-ready—all properties supporting integration into clinical information ecosystems and real-time data streams such that decision support and patient-specific clinical data-sharing governance can occur concurrently.
Multiple avenues of privacy-preserving MARL research advance the prospects of real-time healthcare data compliance and quality. Recent focus on accessibility has included methods for safeguarding de-identification against re-identification attacks and calculating privacy risk before, during, and after sharing sensitive data, achieving compliance with local regulations such as the US Health Insurance Portability and Accountability Act. Further research is addressing privacy-preserving MARL for the de-identification of sensitive data shared across inference-based systems such as those driven by HL7 FHIR.
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