Serverless Quantum-AI Cloud Framework for Real-Time Healthcare and Financial Analytics with NLP-Driven Business Optimization
DOI:
https://doi.org/10.15662/IJARCST.2022.0506012Keywords:
: real‐time healthcare intelligence, serverless cloud computing, quantum machine learning, AI-driven business rule automation, hybrid quantum-classical workflows, event-driven architectureAbstract
In modern healthcare environments, real‐time intelligence is increasingly critical for delivering timely, personalised patient care, optimising clinical workflows, and ensuring regulatory compliance. This research explores the fusion of serverless cloud computing, quantum‐machine-learning (QML) techniques, and AI‐driven business‐rule automation to create a next-generation architecture for healthcare intelligence platforms. Leveraging serverless models abstracts away infrastructure management while enabling dynamic scalability and cost-efficiency. Quantum-enhanced machine learning promises accelerated pattern recognition and predictive analytics on large and complex datasets typical of healthcare (e.g., imaging, genomics, streaming vital signs). Meanwhile, AI‐driven business rule engines automate operational decision logic—e.g., eligibility, alerting, triage—responding in real time to evolving clinical conditions. We present an integrated framework combining these elements, illustrate how healthcare data flows through event‐driven serverless pipelines, how quantum-classical hybrid models perform real-time inference, and how business rule automation orchestrates responses. We report on a simulated case study in which the system achieves reduced latency, improved decision accuracy, and operational cost savings. The findings indicate that such an architecture holds promise for transforming healthcare delivery—but also expose key challenges around quantum hardware maturity, data governance, latency constraints, and rule‐engine interpretability. We conclude with recommendations for deployment and areas for future research.
References
1. Grafberger A., Chadha M., Jindal A., Gu J., Gerndt M. „FedLess: Secure and Scalable Federated Learning Using Serverless Computing“ (2021), arXiv 2111.03396.
2. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
3. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
4. Begum RS, Sugumar R (2019) Novel entropy-based approach for cost- effective privacy preservation of intermediate datasets in cloud. Cluster Comput J Netw Softw Tools Appl 22:S9581–S9588. https:// doi. org/ 10.1007/ s10586- 017- 1238-0
5. 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.
6. Kesavan, E. (2022). Real-Time Adaptive Framework for Behavioural Malware Detection in Evolving Threat Environments. International Journal of Scientific Research and Modern Technology, 1(3), 32-39. https://ideas.repec.org/a/daw/ijsrmt/v1y2022i3p32-39id842.html
7. “Quantum machine learning: Transforming cloud-based AI solutions.” (2020) IJSRA.
8. IBM Quantum team. „Introducing Quantum Serverless, a new programming model for leveraging quantum and classical resources.“ (2021) IBM Quantum blog.
9. Peddamukkula, P. K. Ethical Considerations in AI and Automation Integration Within the Life Insurance Industry. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017494_Ethical_Considerations_in_AI_and_Automation_Integration_Within_the_Life_Insurance_Industry/links/690239c04baee165918ee584/Ethical-Considerations-in-AI-and-Automation-Integration-Within-the-Life-Insurance-Industry.pdf
10. Anbalagan, B., & Pasumarthi, A. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication, 5(4), 5423-5441.
11. Johnson B., Faro I., Behrendt M., Gambetta J. “Introducing Quantum Serverless, a new programming model for leveraging quantum and classical resources.” (2021) IBM Quantum blog.
12. Md R, Tanvir Rahman A. The Effects of Financial Inclusion Initiatives on Economic Development in Underserved Communities. American Journal of Economics and Business Management. 2019;2(4):191-8.
13. Kalyanasundaram, P. D., Kotapati, V. B. R., & Ratnala, A. K. (2021). NLP and Data Mining Approaches for Predictive Product Safety Compliance. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 1, 56-92.
14. TechCrunch. “Qubole launches Quantum, its serverless database engine.” (2019).
15. Cherukuri, B. R. (2020). Quantum machine learning: Transforming cloud-based AI solutions. https://www.researchgate.net/profile/Bangar-Raju-Cherukuri/publication/388617417_Quantum_machine_learning_Transforming_cloud-based_AI_solutions/links/67a33efb645ef274a46db8cf/Quantum-machine-learning-Transforming-cloud-based-AI-solutions.pdf
16. Manda, P. (2022). IMPLEMENTING HYBRID CLOUD ARCHITECTURES WITH ORACLE AND AWS: LESSONS FROM MISSION-CRITICAL DATABASE MIGRATIONS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111-7122.
17. Arjona, A., García López, P., Sampé, J., Slominski, A., & Villard, L. (2020, June 15). Triggerflow: Trigger based orchestration of serverless workflows [Preprint]. arXiv. https://arxiv.org/abs/2006.08654
18. Vengathattil, S. (2019). Ethical Artificial Intelligence - Does it exist? International Journal for Multidisciplinary Research, 1(3). https://doi.org/10.36948/ijfmr.2019.v01i03.37443
19. Anugula Sethupathy, Utham Kumar. (2019). Integrating Legacy ERP with Modern Analytics for Omni-Channel Retail Management. Journal of Emerging Technologies and Innovative Research. 6. 357-368. 10.56975/jetir.v6i9.568594.
20. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
21. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.
22. 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.
23. Sasidevi Jayaraman, Sugumar Rajendran and Shanmuga Priya P., “Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud,” Int. J. Business Intelligence and Data Mining, Vol. 15, No. 3, 2019.
24. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.
25. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
26. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7649
27. El Aboudi, N., & Benhlima, L. (2018). Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation. Advances in Bioinformatics, 2018, 4059018. https://doi.org/10.1155/2018/4059018
