AI-Driven Cloud-Native Microservices Framework for Secure Healthcare Prescription Automation, Software Reliability, and Scalable Deployment Optimization
DOI:
https://doi.org/10.64137/31078699/IJETET-V2I1P106Keywords:
Artificial Intelligence, Cloud-Native Systems, Microservices, Healthcare Prescription Automation, Software Reliability, E-Prescribing, Cyber Security, Cabernets, Mops, Develops, Observability, Clinical Decision SupportAbstract
Healthcare prescription automation is increasingly dependent on interoperable clinical data exchange, cloud-native service orchestration, secure application programming interfaces, and dependable software delivery pipelines. However, current prescription automation platforms often treat clinical decision support, e-prescribing workflow execution, software reliability engineering, cybersecurity governance, and deployment optimization as separate concerns. This separation creates architectural fragmentation in environments where prescription requests must be clinically valid, auditable, resilient to distributed failure, compliant with privacy obligations, and scalable under fluctuating enterprise workloads. This paper proposes a conceptual AI-driven cloud-native microservices framework for secure healthcare prescription automation, software reliability, and scalable deployment optimization. The framework integrates modular prescription services, AI-assisted clinical and operational intelligence, policy-driven security controls, observability-centered reliability engineering, and container-based deployment automation into a unified architecture. The major contribution of this paper is not an empirical claim of clinical superiority, but a structured reference model for designing prescription automation platforms that can support high-assurance workflows in regulated healthcare environments. The study identifies practical gaps in existing approaches, including a weak linkage between prescription standards and runtime reliability, insufficient integration of defect prediction with development pipelines, limited explainability in AI-assisted prescription decisions, and fragmented governance across APIs, containers, and machine learning components. The paper further presents a comparative methodology, architectural layers, implementation considerations, expected analytical outcomes, risk limitations, and future research directions. The proposed framework can guide healthcare enterprises, cloud architects, software reliability engineers, and AI governance teams in designing secure, scalable, and auditable prescription automation systems without binding the architecture to a single vendor or proprietary platform.
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