Risk-Aware Software Maintenance Framework for Cloud-Based Patient Monitoring Platforms: Integrating Oracle EBS, SAP Workloads, and DevOps Pipelines
DOI:
https://doi.org/10.15662/IJARCST.2024.0706012Keywords:
Risk-aware maintenance, DevOps pipelines, cloud-based patient monitoring, SAP integration, Oracle EBS, predictive analytics, healthcare IT, software reliability, risk management, continuous improvementAbstract
The increasing adoption of cloud-based patient monitoring platforms presents new challenges for maintaining software reliability, data security, and operational resilience in healthcare environments. This paper proposes a Risk-Aware Software Maintenance Framework that integrates Oracle E-Business Suite (EBS) and SAP workloads within intelligent DevOps pipelines to ensure continuous improvement and compliance. Modern healthcare ecosystems rely heavily on real-time data from connected medical devices and enterprise systems for clinical and administrative decision-making. However, the complexity of integrating diverse systems introduces significant risks—ranging from data inconsistency and performance degradation to regulatory non-compliance.
The proposed framework embeds risk management principles across the software maintenance lifecycle using automated risk detection, adaptive testing, and predictive analytics. The architecture leverages AI-driven risk evaluation models and DevOps automation tools to streamline maintenance operations across cloud environments. It enables continuous assessment of service performance, incident prediction, and the prioritization of maintenance tasks based on business and clinical impact. Data pipelines unify operational logs from SAP and Oracle EBS workloads with real-time telemetry from patient monitoring platforms. This integration allows comprehensive visibility into system health, failure patterns, and compliance metrics.
The paper outlines a design-science approach to building and validating the framework, highlighting its benefits in reducing downtime, enhancing change management efficiency, and ensuring healthcare regulatory adherence. Experimental evaluation demonstrates improved fault detection accuracy and maintenance responsiveness compared with traditional models. The findings emphasize the importance of embedding risk awareness into DevOps-based maintenance pipelines to achieve sustainable digital healthcare transformation.
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