Risk-Based DevOps Pipeline Framework for Real-Time Patient Monitoring Systems: Integrating SAP Workloads and Oracle EBS in Cloud Environments

Giulia Elisabetta Romano

doi:10.15662/IJRAI.2024.0706014

Authors

Giulia Elisabetta Romano DevOps Engineer, Italy Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0706014

Keywords:

DevOps, DevSecOps, risk-based pipeline, patient monitoring, real-time healthcare systems, SAP, Oracle EBS, cloud migration, continuous integration, continuous deployment

Abstract

This research proposes a risk-based DevOps pipeline framework tailored for real-time patient monitoring systems operating in cloud environments, specifically integrating enterprise workloads from SAP ERP (SAP) and Oracle E‑Business Suite (Oracle EBS). With the rise of continuous monitoring of patient vital signs, clinical alerts and device telemetry, healthcare organisations are increasingly moving core applications—such as ERP, analytics and transaction systems—into hybrid and multi-cloud contexts. The proposed framework emphasises early and continuous risk assessment, automated security and compliance gates, infrastructure as code, blue-green/Canary deployments, and live feedback loops for clinical safety and regulatory assurance. The pipeline is designed to support both the high-availability and low-latency requirements of patient monitoring, and the heavy-transaction, audit-intensive nature of SAP/Oracle workloads. The framework also accounts for healthcare-specific risk domains: patient safety, data integrity, regulatory compliance (e.g., HIPAA, GDPR), supply-chain vulnerability (third-party medical device firmware), and business continuity. A case study design applying the framework to a hypothetical tertiary care hospital is discussed, highlighting pipeline phases (Plan → Code → Build → Test → Release → Deploy → Monitor), risk evaluation at each gate, and integration of SAP and Oracle EBS modules in a cloud deployment. Outcomes are projected in terms of deployment frequency, mean time to recovery (MTTR), number of high-risk defects caught pre-production, and compliance audit results. The paper concludes with advantages, limitations and directions for future work.

References

1. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices. arXiv. (arxiv.org)

2. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150

3. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.

4. Rajendran, Sugumar (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm. Int. J. Business Intell. Data Mining 10 (2):1-20.

5. GUPTA, A. B., et al. (2023). "Smart Defense: AI-Powered Adaptive IDs for Real-Time Zero-Day Threat Mitigation."

6. Shatnawi, A., Orrù, M., Mobilio, M., Riganelli, O., & Mariani, L. (2018). CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services. arXiv. (arxiv.org)

7. Theaker, C. (2021). A Secure Cloud Service Deployment Framework for DevOps. Indonesian Journal of Electrical Engineering and Computer Science, 21(2), 874–885. (Grafiati)

8. Vijajakumar, R., & Arun, P. (2020). Cloud-Based Business Process Security Risk Management: A Systematic Review, Taxonomy, and Future Directions. Computers, 10(12), 160. (MDPI)

9. Anbalagan, B. (2023). Proactive Failover and Automation Frameworks for Mission-Critical Workloads: Lessons from Manufacturing Industry. International Journal of Research and Applied Innovations, 6(1), 8279-8296.

10. Shakil, K. A., Zareen, F. J., Alam, M., & Jabin, S. (2017). BAMHealthCloud: A Biometric Authentication and Data Management System for Healthcare Data in Cloud. arXiv. (arxiv.org)

11. Akyildiz, I. F., et al. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

12. Kadar, Mohamed Abdul. "MEDAI-GUARD: An Intelligent Software Engineering Framework for Real-time Patient Monitoring Systems." (2019).

13. Manda, P. (2023). A Comprehensive Guide to Migrating Oracle Databases to the Cloud: Ensuring Minimal Downtime, Maximizing Performance, and Overcoming Common Challenges. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8201-8209.

14. Batchu, K. C. (2024). Integrating Heterogeneous ETL Pipelines: Towards Unified Data Processing Across Cloud and Legacy Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10491-10498.

15. Gubbi, J., et al. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

16. Sivaraju, P. S. (2024). PRIVATE CLOUD DATABASE CONSOLIDATION IN FINANCIAL SERVICES: A CASE STUDY OF DEUTSCHE BANK APAC MIGRATION. ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA).

17. Sugu, S. Building a distributed K-Means model for Weka using remote method invocation (RMI) feature of Java. Concurr. Comp. Pract. E 2019, 31. [Google Scholar] [CrossRef]

18. Pasumarthi, A. (2022). Architecting Resilient SAP Hana Systems: A Framework for Implementation, Performance Optimization, and Lifecycle Maintenance. International Journal of Research and Applied Innovations, 5(6), 7994-8003.

19. Sethupathy, U. K. A. (2022). Integrating Multi-Tool DevOps Pipelines: Challenges and Solutions. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(6), 7319-7329.

20. Kumar, P., & Garg, R. (2018). Integrating SAP and Oracle ERP systems in hybrid cloud environments. Journal of Cloud Computing, 7(2), 122–136.

21. Christadoss, J., Sethuraman, S., & Kunju, S. S. (2023). Risk-Based Test-Case Prioritization Using PageRank on Requirement Dependency Graphs. Journal of Artificial Intelligence & Machine Learning Studies, 7, 116-148.

22. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2024). Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 9801-9806.

23. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.

24. Zerine, I., Islam, M. S., Ahmad, M. Y., Islam, M. M., & Biswas, Y. A. (2023). AI-Driven Supply Chain Resilience: Integrating Reinforcement Learning and Predictive Analytics for Proactive Disruption Management. Business and Social Sciences, 1(1), 1-12.

25. Bussu, V. R. R. (2024). Maximizing Cost Efficiency and Performance of SAP S/4HANA on AWS: A Comparative Study of Infrastructure Strategies. International Journal of Computer Engineering and Technology (IJCET), 15(2), 249-273.