AI-Powered Healthcare Transformation in Serverless Cloud Environments: Integrating Quantum Machine Learning with ERP Business Rules for Scalable Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2022.0506014Keywords:
serverless cloud computing, quantum machine learning, healthcare transformation, ERP business rules, scalable intelligence, predictive analytics, hybrid quantum-classical, healthcare workflowsAbstract
In an era where healthcare delivery is being radically transformed by digital technologies, this paper presents a framework leveraging serverless cloud computing, enterprise resource planning (ERP) business-rules engines and quantum-machine-learning (QML) models to achieve scalable intelligence in healthcare systems. The proposed architecture supports event-driven, real-time processing of clinical, operational and administrative data, integrates decision-logic from ERP-style business rules to enforce regulatory, workflow and resource-allocation policies, and incorporates quantum-enhanced ML algorithms to derive predictive and prescriptive insights from large-scale healthcare datasets. We examine the technical architecture, deployment strategy in a serverless cloud environment, and integration pathways between ERP business-rules modules and QML-based analytics. Empirical simulation (on synthetic healthcare workflows) demonstrates improvements in latency, scalability and predictive accuracy compared to classical ML alone. The paper discusses advantages of combining serverless elasticity, business-rules governance and quantum-accelerated learning; also it addresses the limitations, including maturity of quantum hardware, state-management in serverless functions and regulatory compliance. We conclude by outlining a research roadmap for integrating hybrid classical/quantum workflows, real-world pilot deployments, and governance frameworks for large-scale adoption in healthcare institutions.
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