AI-Powered Quality Assurance Framework for Cloud Healthcare Systems: Leveraging Artificial Neural Networks, Oracle EBS, and Azure DevOps for Real-Time Error Prediction and Correction
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806807Keywords:
artificial neural networks, quality assurance, cloud healthcare systems, Oracle EBS, Azure DevOps, real-time error prediction, automated remediationAbstract
In modern health-care environments, cloud-based systems are increasingly deployed to support patient records, clinical workflows, and administrative operations. Ensuring quality, reliability and real-time error correction in these systems is critical given patient safety, regulatory and operational imperatives. This paper proposes an AI-powered quality assurance (QA) framework that integrates artificial neural networks (ANNs) with enterprise resource planning (ERP) via Oracle E‑Business Suite (Oracle EBS) and continuous delivery/DevOps pipelines via Azure DevOps operating on a cloud‐native infrastructure. The framework monitors system logs, transaction metrics, user behaviour and data‐flows in real-time, feeds features into the ANN, and predicts likely error conditions (such as data inconsistencies, transaction failures, integration faults) before they impact operations. When a high-risk condition is detected, the framework triggers corrective workflows in Azure DevOps and updates Oracle EBS error‐handling modules, enabling automated or semi-automated remediation. The approach is demonstrated via a simulated deployment in a cloud-healthcare subsystem, with metrics showing reduction in transaction failure rate and mean time to resolution (MTTR) compared to baseline. The results indicate that combining ANN-based prediction, enterprise system integration and DevOps automation can significantly improve QA in cloud healthcare systems. Limitations, including data-annotated training sets and model interpretability, are discussed. Future work will explore expanding the framework to multi-tenant cloud environments and incorporating federated learning.
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