Cloud Based Secure Enterprise Healthcare Software with AI Centered Risk Governance
DOI:
https://doi.org/10.15662/IJARCST.2024.0704017Keywords:
Cloud Computing, Enterprise Healthcare Systems, Artificial Intelligence, Risk Governance, Cybersecurity, Data Privacy, Electronic Health Records, Predictive Analytics, HIPAA Compliance, Healthcare IT InfrastructureAbstract
Cloud-based secure enterprise healthcare software has emerged as a transformative approach to managing clinical, administrative, and financial operations in modern healthcare systems. With the rapid digitization of medical records and integration of advanced analytics, healthcare organizations increasingly rely on cloud infrastructures to deliver scalable, interoperable, and cost-efficient services. At the same time, the proliferation of sensitive patient data and regulatory requirements necessitates robust security, privacy protection, and risk governance frameworks. Artificial Intelligence (AI) plays a critical role in enhancing cybersecurity, predictive analytics, clinical decision support, fraud detection, and compliance monitoring. This paper explores the architecture, governance structures, and risk management strategies associated with AI-centered cloud healthcare platforms. It analyzes the integration of machine learning models into enterprise systems such as electronic health records, telemedicine platforms, and population health management tools. Furthermore, it evaluates regulatory compliance frameworks including HIPAA and global data protection standards. The study proposes a comprehensive research methodology for designing and implementing secure AI-driven healthcare enterprise systems. Finally, advantages and limitations are discussed to provide a balanced understanding of technological, ethical, operational, and financial implications in contemporary healthcare ecosystems.
References
1. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
2. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
3. Lokiny, N. (2022). Kubernetes for container orchestration in artificial intelligence cloud technologies. International Journal of Science and Research (IJSR), 11(11), 1536-1538.
4. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
5. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations, 6(5), 9534-9538.
6. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
7. Ramidi, M. (2023). Implementing privacy-focused data sharing frameworks for mobile healthcare communication. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8746–8757.
8. 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.
9. Ponugoti, M. (2023). Bridging the digital divide: Architecture for equitable technological access. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6991–7002.
10. 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.
11. Hasenkhan, F., Keezhadath, A. A., & Amarapalli, L. (2023). Intelligent Data Partitioning for Distributed Cloud Analytics. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 106-145.
12. 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.
13. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.
14. Chennamsetty, C. S. (2023). Neural Pipeline Orchestration: Deep Learning Approaches to Software Development Bottleneck Elimination. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(4), 8674-8680.
15. Sugumar, R. (2024). Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape. International Journal of Humanities and Information Technology, 6(02), 89-105.
16. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
17. Kondisetty, K., Panda, M. R., & Murthy, C. J. (2023). Customer Experience Enhancement in Omnichannel Banking Using Reinforcement Learning. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 565-600.
18. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
19. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
20. Natta, P. K. (2023). Harmonizing enterprise architecture and automation: A systemic integration blueprint. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9746–9759. https://doi.org/10.15662/IJRPETM.2023.0606016
21. 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.
22. 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.
23. Singh, A. (2021). Mitigating DDoS attacks in cloud networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(4), 3386–3392. https://doi.org/10.15662/IJEETR.2021.0304003
24. 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.
25. Gaddapuri, N. S. (2023). A COMPARATIVE STUDY OF HEALTHCARE SYSTEMS IN THE UNITED STATES AND INDIA. Power System Protection and Control, 51(2), 18-31.
26. Patnaik, S. K., Sidhu, M. S., Gehlot, Y., Sharma, B., & Muthu, P. (2018). Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 11(3), 1429.
27. Mudunuri, P. R. (2022). Automating compliance in biomedical DevOps: A policy-as-code approach. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6770–6783.
28. Ponugoti, M. (2023). Bridging the digital divide: Architecture for equitable technological access. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6991–7002.
29. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
30. Kamadi, S. (2021). Risk Exception Management in Multi-Regulatory Environments: A Framework for Financial Services Utilizing Multi-Cloud Technologies.
31. Nalini, T., Rama, A., Shanmuganathan, M., Sam, D., & Sheeba, D. A. (2022, April). The Empirical Analysis For Effective Prediction of Crop Price Using Neuro Evolutionary Algorithm based on Machine Learning Approach. In Journal of Physics: Conference Series (Vol. 2251, No. 1, p. 012006). IOP Publishing.
32. Surisetty, L. S. (2022). Designing Intelligent Integration Engines for Healthcare: From HL7 and X12 to FHIR and Beyond. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(1), 5989-5998.
33. Kesavan, E. (2023). Assessing laptop performance: A comprehensive evaluation and analysis. Recent Trends in Management and Commerce, 4(2), 175–185. https://doi.org/10.46632/rmc/4/2/22
34. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
