AI-Powered Healthcare Interoperability Architecture Utilizing Oracle Cloud Services
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Abstract
As healthcare organizations face growing demands for real-time data access, system integration, and secure information exchange, the modernization of legacy IT infrastructure has become critical. This paper explores the application of Artificial Intelligence (AI)-driven interoperability and data modernization within large-scale cloud-based IT systems to enable secure and scalable healthcare transformation. The integration of AI techniques—such as natural language processing (NLP), machine learning (ML), and predictive analytics—into cloud platforms fosters automated data harmonization, semantic interoperability, and intelligent decision support across heterogeneous systems. Furthermore, the adoption of standardized healthcare data models (e.g., HL7 FHIR) and APIs enhances data portability while ensuring compliance with privacy regulations such as HIPAA and GDPR. This research discusses architecture models, key implementation challenges, security considerations, and performance metrics for deploying AI-enhanced, cloud-native interoperability frameworks. The proposed solutions aim to improve clinical workflows, population health analytics, and patient outcomes while reducing operational complexity and costs.
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1. Chinta, S. (2021). Harnessing Oracle Cloud Infrastructure for Scalable AI Solutions: A Study on Performance and Cost Efficiency. Technix International Journal for Engineering Research, 8, a29-a43.
2. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
3. Karka, N. R. (2025). State Management in Large-Scale React Applications: A Comprehensive Analysis. International Journal of Advanced Research in Engineering and Technology (IJARET).
4. Konda, S. K. (2025). Designing scalable integrated building management systems for large-scale venues: A systems architecture perspective. International Journal of Computer Engineering and Technology, 16(3), 299–314. https://doi.org/10.34218/IJCET_16_03_022
5. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
6. Lin, T., Kukkadapu, S., & Suryadevara, G. (2025, March). A Cloud-Native Framework for Cross-Industry Demand Forecasting: Transferring Retail Intelligence to Manufacturing with Empirical Validation. In 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) (pp. 1115-1123). IEEE.
7. Kalpinagarajarao, G. K. (2025). AI-enhanced Oracle platforms: A new era of predictive healthcare analytics. International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), 1823-1830.
8. Lanka, S. (2025). ARCHITECTURAL PATTERNS FOR AI-ENABLED TRIAGE AND CRISIS PREDICTION SYSTEMS IN PUBLIC HEALTH PLATFORMS. International Journal of Research and Applied Innovations, 8(1), 11648-11662.
9. Oracle Corporation. (2025). Oracle Health Data Intelligence Solution Brief. Retrieved from https://www.oracle.com/a/ocom/docs/industries/healthcare/oracle-health-data-intelligence-solution-brief.pdf
10. Chakilam, C., Koppolu, H. K. R., & Recharla, M. (2022). Revolutionizing Patient Care with AI and Cloud Computing: A Framework for Scalable and Predictive Healthcare Solutions. International Journal of Science and Research, 11(4), 16842–16862.
11. Thambireddy, S., Bussu, V. R. R., Madathala, H., Mane, V., & Inamdar, C. (2025, August). AI-Enabled SAP Enterprise Systems: A Comprehensive Business Use Case Survey. In 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA) (pp. 1045-1052). IEEE.
12. Jameil, A. K. (2025). A digital twin framework for real-time healthcare monitoring. SpringerLink. Retrieved from https://link.springer.com/article/10.1007/s43926-025-00135-3
13. Kalpinagarajarao, G. K. (2025). Balancing AI Innovation and Data Privacy in Oracle Cloud-Based Health Systems. International Journal of Innovative Research in Multidisciplinary Private Studies, 13(1), 232087.
14. Pathak, D. K., Singh, V., & Gupta, P. (2023). Integrating Artificial Intelligence and Machine Learning into Healthcare ERP Systems: A Framework for Oracle Cloud and Beyond. ESP Journal of Engineering & Technology Advancements, 3(2), 171-178.
15. Peddamukkula, P. K. (2024). The Impact of AI-Driven Automated Underwriting on the Life Insurance Industry. International Journal of Computer Technology and Electronics Communication, 7(5), 9437-9446.
16. Karvannan, R. (2025). Architecting DSCSA-compliant systems for real-time inventory management in high-volume retail pharmacy networks. International Journal of Computer Engineering and Technology, 16(2), 4181–4194. https://doi.org/10.34218/IJCET_16_02_036
17. Cao, M. (2024). Developing remote patient monitoring infrastructure using scalable cloud architectures. PubMed Central. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC11576445/
18. Nallamothu, T. K. (2025). THE FUTURE OF BUSINESS INTELLIGENCE: INTEGRATING AI ASSISTANTS LIKE DAX COPILOT INTO ANALYTICAL WORKFLOWS. International Journal of Research and Applied Innovations, 8(1), 11663-11674.
19. Arjunan, T. (2024). A comparative study of deep neural networks and support vector machines for unsupervised anomaly detection in cloud computing environments. International Journal for Research in Applied Science and Engineering Technology, 12(9), 10-22214.
20. Batchu, K. C. (2022). Modern Data Warehousing in the Cloud: Evaluating Performance and Cost Trade-offs in Hybrid Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(6), 7343-7349.
21. M. Krishnapatnam, "Cutting-Edge AI Techniques for Securing Healthcare IAM: A Novel Approach to SAML and OAuth Security," International Journal of Computing and Engineering, vol. 7, no. 2, pp. 39-50, 2025, doi: 10.47941/ijce.2630.
22. Sugumar, Rajendran (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection (13th edition). Bulletin of Electrical Engineering and Informatics 13 (3):1935-1942.
23. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
24. Jameil, A. K. (2025). A digital twin framework for real-time healthcare monitoring. SpringerLink. Retrieved from https://link.springer.com/article/10.1007/s43926-025-00135-3