Cloud Native Enterprise Healthcare Platform Integrating AI Machine Learning Blockchain Governance and Clinical Risk Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806026Keywords:
Cloud-native healthcare, Artificial Intelligence, Machine Learning, Blockchain governance, Clinical risk intelligence, Kubernetes, Hyperledger Fabric, FHIR interoperability, Healthcare cybersecurity, Predictive analyticsAbstract
The rapid digital transformation of healthcare demands scalable, secure, and intelligent platforms capable of managing complex clinical, operational, and regulatory ecosystems. This paper proposes a Cloud Native Enterprise Healthcare Platform integrating Artificial Intelligence (AI), Machine Learning (ML), blockchain-based governance, and clinical risk intelligence to deliver secure, interoperable, and data-driven healthcare services. Built upon cloud-native architectures such as microservices, containerization, and orchestration frameworks like Kubernetes, the platform ensures elasticity, resilience, and continuous deployment. AI/ML modules enable predictive analytics, personalized treatment planning, and early risk detection, while blockchain frameworks such as Hyperledger Fabric provide tamper-resistant audit trails and decentralized governance. Clinical risk intelligence engines synthesize electronic health records (EHR), medical imaging, genomic data, and real-time IoT streams to support proactive decision-making. The architecture aligns with global healthcare interoperability standards including FHIR to ensure seamless data exchange. The proposed model emphasizes security-by-design, zero-trust principles, explainable AI, and regulatory compliance. This integrated approach addresses scalability challenges, data fragmentation, fraud detection, and patient safety while enabling precision medicine and value-based care. The research demonstrates how convergence of cloud-native computing, AI/ML, blockchain governance, and clinical intelligence forms a resilient and future-ready enterprise healthcare ecosystem
References
1. Ponugoti, M. (2024). AI-Driven Microservice Architectures: Enhancing Compliance and Decision Intelligence in Cloud Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14880.
2. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
3. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
4. Mudunuri, P. R. (2023). Governance-aware infrastructure-as-code for regulated research environments. International Journal of Research in Engineering, Project Management and Technology (IJRPETM), 6(4), 9017–9028.
5. Gurajapu, A., & Garimella, V. (2025). Edge-to-cloud workflows for low-latency telecom services: Optimizing offload decisions. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12638–12641.
6. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12-24.
7. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
8. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
9. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566-1570). IEEE.
10. Lokiny, N. (2020). The Role of AI and Machine Learning in DevOps Automation, 7(2), 328–333.
11. Keezhadath, A. A., Sethuraman, S., & Das, D. (2021). Cost-Efficient Cloud Data Processing: Strategies for Enterprise-Wide Cost Optimization. American Journal of Data Science and Artificial Intelligence Innovations, 1, 135-168.
12. 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.
13. Sriramoju, S. (2024). Designing scalable and fault-tolerant architectures for cloud-based integration platforms. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13839–13851.
14. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
15. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
16. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
17. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
18. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
19. Genne, S. (2023). Improving enterprise web responsiveness through server-side rendering in Next.js. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(4), 7313–7323.
20. Ramidi, M. (2025). AI integration in government mobile platforms for secure and innovative digital solutions. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14532–14543.
21. Panda, M. R., & Chinthalapelly, P. R. (2023). Banking Sandbox Evaluation for Open Banking Ecosystems Using Agent-Based Modeling. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.
22. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
23. Surisetty, L. S. (2023). Proactive Threat Mitigation in API Ecosystems through AI-Powered Anomaly Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633-7642.
24. Nandhini, T., Babu, M. R., Natarajan, B., Subramaniam, K., & Prasanna, D. (2024). A NOVEL HYBRID ALGORITHM COMBINING NEURAL NETWORKS AND GENETIC PROGRAMMING FOR CLOUD RESOURCE MANAGEMENT. Frontiers in Health Informatics, 13(8).
25. Keezhadath, A. A., Sethuraman, S., & Das, D. (2021). Cost-Efficient Cloud Data Processing: Strategies for Enterprise-Wide Cost Optimization. American Journal of Data Science and Artificial Intelligence Innovations, 1, 135-168.
26. Chennamsetty, C. S. (2022). Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(5), 7121-7133.
27. Kamadi, S. Multi-Cloud ETL Automation and Rollback Strategies: An Empirical Study for Distributed workload orchestration system. https://www.researchgate.net/profile/Sandeep-Kamadi/publication/399059730_Multi-Cloud_ETL_Automation_and_Rollback_Strategies_An_Empirical_Study_for_Distributed_workload_orchestration_system/links/694ca68106a9ab54f84a6805/Multi-Cloud-ETL-Automation-and-Rollback-Strategies-An-Empirical-Study-for-Distributed-workload-orchestration-system.pdf
28. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49-63.
29. Panda, M. R., & Sethuraman, S. (2022). Blockchain-Based Regulatory Reporting with Zero-Knowledge Proofs. Essex Journal of AI Ethics and Responsible Innovation, 2, 495-532.
30. Thakran, V. (2025, June). An Analysis of Machine Learning Solutions for Precise Forecasting of Oil and Gas Pipeline. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1-6). IEEE.
31. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
32. Prasanna, D., Ahamed, N. A., Abinesh, S., Karthikeyan, G., & Inbatamilan, R. (2024, November). Cloud based automatically human document authentication processes for secured system. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.
33. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
34. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
35. Kalyanasundaram, P. D., Devi, C., & Pachyappan, R. (2024). Autoencoder-Based Anomaly Detection on Metadata Metrics for Privacy Enforcement Monitoring. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124-155.
36. Varde, Y., Tiwari, S. K., Shawn, M. A. A., Gopianand, M., & Makin, Y. (2025, September). A Machine Learning Approach for Predictive Financial Analysis: Enhancing Fraud Detection and Investment Strategies. In 2025 7th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-5). IEEE.
37. Meshram, A. K. (2025). Real-time financial fraud prediction using big data streaming on cloud platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12834-12845.
38. Muthirevula, G. R., Sethuraman, S., & Mohammed, A. S. (2022). Microservices-Driven Manufacturing: Accelerating Legacy Application Modernization with Cloud-Native Strategies. American Journal of Autonomous Systems and Robotics Engineering, 2, 73-107.
39. Sikarwar, V. (2025). AI-Augmented in Enterprise Domain Modeling and its impact on Data Modernization projects. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(3), 9944-9952.
40. Inampudi, R. K., Surampudi, Y., & Kondaveeti, D. (2023). AI-driven real-time risk assessment for financial transactions: leveraging deep learning models to minimize fraud and improve payment compliance. Journal of Artificial Intelligence Research and Applications, 3(1), 716-758.
