AI-Powered Serverless Cloud Architecture Integrating Quantum Machine Learning and SAP for Real-Time Healthcare Decision Optimization
DOI:
https://doi.org/10.15662/IJRAI.2022.0506015Keywords:
Healthcare analytics, Serverless cloud architecture, Quantum machine learning, Real-time streaming, Business rule optimisation, Function-as-a-service, Hybrid quantum-classical modelling, Dynamic workflows, SAPAbstract
In the era of digital healthcare transformation, organisations are inundated with large-volume real-time data from clinical operations, patient monitoring, medical devices, administrative workflows and regulatory systems. Traditional analytics and business rule engines struggle to process this continuous stream of heterogeneous data, adapt business logic dynamically, and support near-real-time decision-making. This paper proposes an AI-enhanced, serverless cloud architecture incorporating quantum machine learning (QML) components to dynamically optimise business rules for healthcare analytics. The architecture leverages event-driven, function-as-a-service (FaaS) and managed cloud services for ingesting, storing and processing healthcare data streams, and overlays a hybrid quantum-classical machine learning engine to infer and adjust business rules (e.g., resource allocation, patient triage, billing rules, supply-chain thresholds) in real time. Through a proof-of-concept simulation, the framework demonstrates improved responsiveness, adaptability and scalability compared to static rule-engines. The novelty lies in the combination of serverless cloud elasticity with a QML-driven optimisation loop to support dynamic business rule adaptation in healthcare analytics contexts. The proposed approach addresses key challenges—scalability, cost-efficiency, rule-update latency and analytic agility—while exploring the potential of QML to enhance decision support in healthcare operations. The paper then outlines the architecture, implementation considerations, research methodology, advantages, limitations and future work required to bring such frameworks into production in healthcare settings.
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