Design and Evaluation of Secure Microservices Architecture for HIPAA-Compliant Prescription Processing on AWS and OpenShift
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P116Keywords:
Microservices Architecture, Prescription Processing, HIPAA Compliance, AWS Healthcare, OpenShift Container PlatformAbstract
With evermore challenges in balancing compliance with innovation, the healthcare industry is strugglingto update its prescription processing systems. This Research Analyses the Secure Microservices Architecture Designand Evaluation of secure micro services architecture for Hipaa compliant prescription processing deployed on AWSand OpenShift Platforms. The main goals are to analyze security protocols, measure performances and evaluatecompliance approaches for containers-based healthcare systems. We conducted a mixed-methods researchmethodology that quantitatively analyzed performance and then qualitatively assessed security-based reasons acrossmultiple implanting microservices-based prescription inside a wide range of healthcare organizations. Hypothesis:Microservices architecture can offer better HIPAA compliance rates, as compared to monolithic systems, if it isproperly set up with appropriate defense-in-depth security controls. The research shows that organizations runningmicroservices on hybrid cloud platforms were 94.7% more likely to achieve compliance, with large gains in auditlogging, access control, and data encryption metrics. In the article, we discuss how service mesh and containerorchestration give you the per pod security controls required for handlign protected health information. In summary,secure microservices architecture shows a possible route for modernizing healthcare organizations while moving toawareness of the strong HIPAA requirements still in place.
References
1. Aitken, M., & Kleinrock, M. (2019). Medicine use and spending in the U.S.: A review of 2018 and outlook to 2023. IQVIA Institute for Human Data Science, 1-49.
2. Basak, S. C., & Sathyanarayana, D. (2010). Pharmacy education in India. American Journal of Pharmaceutical Education, 74(4), 68.
3. Gunda, S. K. (2023). Software defect prediction using advanced ensemble techniques: A focus on boosting and voting methods. 2023 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India, 157–161. https://doi.org/10.1109/ICESIC61777.2023.10846550
4. Carlson, J. L. (2013). Redis in action. Manning Publications.
5. Gunda SK, Yettapu SDR, Bodakunti S, Bikki SB. Decision Intelligence Methodology for AI-Driven Agile Software Lifecycle Governance and Architecture-Centered Project Management, 2023 Mar. 30;4(1):102-8. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P112
6. Chen, Y., Li, K., Kawamoto, K., & Kohane, I. S. (2020). Machine learning-enabled predictive analytics for pharmaceutical inventory management. Journal of Biomedical Informatics, 104, 103395.
7. Gunda, S. K. (2023). Enhancing software fault prediction with machine learning: A comparative study on the PC1 dataset. 2023 Global Conference on Communications and Information Technologies (GCCIT), Bangalore, India, 1–4. https://doi.org/10.1109/GCCIT63234.2023.10862351
8. Grolinger, K., Higashino, W. A., Tiwari, A., & Capretz, M. A. (2013). Data management in cloud environments: NoSQL and NewSQL data stores. Journal of Cloud Computing: Advances, Systems and Applications, 2(1), 22.
9. Haque, S. N., Gellad, W. F., & Engel, K. G. (2017). Specialty medication management: Key considerations for hospital-based pharmacies. American Journal of Health-System Pharmacy, 74(7), 482-488.
10. Gunda, S. K. (2023). Comparative analysis of machine learning models for software defect prediction. 2023 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 1–6. https://doi.org/10.1109/ICPECTS62210.2023.10780167
11. Kang, J., Lee, S., & Park, S. (2021). Intelligent inventory management system using machine learning and caching mechanisms. Computers & Industrial Engineering, 156, 107274.
12. Gunda, S. K. (2023). Machine learning approaches for software fault diagnosis: Evaluating decision tree and KNN models. 2023 Global Conference on Communications and Information Technologies (GCCIT), Bangalore, India, 1–5. https://doi.org/10.1109/GCCIT63234.2023.10861953
13. Kilimci, Z. H., Akyuz, A. O., Uysal, M., Akyokus, S., Uysal, M. O., Bulbul, B. A., & Ekmis, M. A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity, 2019, 9067367.
14. Gunda, S. K. (2023). Analyzing machine learning techniques for software defect prediction: A comprehensive performance comparison. 2023 Asian Conference on Intelligent Technologies (ACOIT), Kolar, India, 1–5. https://doi.org/10.1109/ACOIT62457.2023.10939610
15. Merkuryeva, G., Valberga, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 3-10.
16. Nguyen, T., Li, Z., & Spiegler, V. (2020). Caching strategies for pharmaceutical inventory systems: A performance evaluation. International Journal of Production Economics, 228, 107743.
17. Shah, N. (2004). Pharmaceutical supply chains: Key issues and strategies for optimization. Computers & Chemical Engineering, 28(6-7), 929-941.
18. Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26.
19. Gunda, S. K. G. (2023). The Future of Software Development and the Expanding Role of ML Models. International Journal of Emerging Research in Engineering and Technology, 4(2), 126-129. https://doi.org/10.63282/3050-922X.IJERET-V4I2P113
20. Tichy, E. M., Hoffman, J. M., Suda, K. J., Rim, M. H., Tadrous, M., Cuellar, S., & Schumock, G. T. (2019). National trends in prescription drug expenditures and projections for 2019. American Journal of Health-System Pharmacy, 76(15), 1105-1121.
21. Gunda, S. K. (2023). Device for continuous software testing and validation (UK Registered Design No. 6400738). Registered with the UK Intellectual Property Office, Class 14-02, granted in November 2023.
