Optimized blockchain-based healthcare framework empowered by mixed multi-agent reinforcement learning

The world has recently witnessed the devastating impact of a global pandemic, where countless lives were tragically lost due to delayed abnormal disease detection and the lack of seamless coordination among healthcare organizations such as hospitals, medical labs, and pharmacies. Addressing the heterogeneity of healthcare entities and the extensive volume of medical-related data they possess gives rise to significant challenges in establishing trust, security, privacy, and low-latency connections. To this end, this paper introduces the IP-HealthChain framework, a comprehensive healthcare solution built upon blockchain technology to foster trust, decentralization, and secure data exchange while integrating intelligence within its ecosystem. In this work, we propose a blockchain-based optimization model that leverages the available computational resources to enhance each participant's latency, security, and cost. We achieve this by developing an online intelligent Deep Reinforcement Learning decision-making algorithm tailored to address the mixed cooperative-competitive nature inherent in decision-making processes within the shared blockchain network. The optimization model is formulated as a Mixed Decentralized Partially Observable Markov Decision Process and efficiently solved using state-of-the-art Deep Multi-Agent Q-Learning techniques. Through this approach, we enable indirect communication among participants and ensure the processing of low-urgency data, thereby promoting an inclusive and responsive healthcare ecosystem. Extensive simulations are conducted to evaluate the performance of our proposed solution. The results demonstrate significant advantages over traditional heuristics in terms of decision-making speed, resource utilization, latency and cost minimization, security maximization, and adherence to stringent Quality of Service requirements.