ENABLING SCALABLE AND SECURE HEALTHCARE DATA ANALYTICS WITH CLOUD-NATIVE AI ARCHITECTURES
Keywords:
Healthcare Analytics, Cloud-Native Architecture, Artificial Intelligence, Data Security, Clinical Decision SupportAbstract
Healthcare organizations face unprecedented challenges in managing and analyzing vast volumes of medical data while ensuring security and regulatory compliance. This comprehensive article explores the implementation of cloud-native architectures integrated with artificial intelligence and machine learning capabilities for healthcare analytics. The article examines the challenges of data volume, security requirements, and real-time processing demands, presenting solutions through cloud infrastructure, AI/ML integration, and serverless computing. Through case studies and empirical analysis, the article demonstrates significant improvements in clinical decision support, operational efficiency, and patient engagement. The findings highlight the importance of structured implementation approaches, robust technical frameworks, and continuous evaluation mechanisms in achieving successful healthcare analytics deployments.
References
H. Mumtaz, et al., "Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review," Front Digit Health, vol. 5, pp. 1203945, Sep. 2023. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10568450/
O. Oyeniran et al., "A comprehensive review of leveraging cloud-native technologies for scalability and resilience in software development," ResearchGate, Mar. 2024. Available: https://www.researchgate.net/publication/379429890_A_comprehensive_review_of_leveraging_cloud-native_technologies_for_scalability_and_resilience_in_software_development
L. Hong et al., "Big Data in Health Care: Applications and Challenges," Data and Information Management, vol. 2, no. 3, pp. 175-197, Dec. 2018. Available: https://www.sciencedirect.com/science/article/pii/S2543925122000791
A. Almalawi et al., "Managing Security of Healthcare Data for a Modern Healthcare System," ResearchGate, Mar. 2023. Available: https://www.researchgate.net/publication/369718759_Managing_Security_of_Healthcare_Data_for_a_Modern_Healthcare_System
V. Salunkhe et al., "The Impact of Cloud Native Technologies on Healthcare Application Scalability and Compliance," SSRN, Dec. 2024. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4984999
N. A. Azeez and C. Van der Vyver, "Security and privacy issues in e-health cloud-based system: A comprehensive content analysis," Egyptian Informatics Journal, vol. 20, no. 2, pp. 97-108, July 2019. Available: https://www.sciencedirect.com/science/article/pii/S1110866517302797
F. Magrabi et al., "Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications," Yearb Med Inform, vol. 28, no. 1, pp. 128-134, Apr. 2019. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC6697499/
R. Ghosh, "Case Study – Power of Operational Analytics in Healthcare," Analytics, Sep. 2016. Available: https://pubsonline.informs.org/do/10.1287/LYTX.2016.05.14/full/
E. Davidson et al., "Quantifying the Impact of Healthcare Analytics: A Multi-Institution Study," Journal of Healthcare Management, vol. 68, no. 4, pp. 278-293, Aug 2023. Available: https://doi.org/10.1097/JHM-D-22-00789
P. Wilson et al., "Operational Transformation Through Healthcare Analytics: Metrics and Outcomes," Healthcare Innovation Journal, vol. 15, no. 2, pp. 156-171, June 2023. Available: https://doi.org/10.1016/j.hij.2023.06.002
M. Sony et al., "Critical Success Factors for Successful Implementation of Healthcare 4.0: A Literature Review and Future Research Agenda," ResearchGate, Mar. 2023. Available: https://www.researchgate.net/publication/369033452_Critical_Success_Factors_for_Successful_Implementation_of_Healthcare_40_A_Literature_Review_and_Future_Research_Agenda
J. van Gemert-Pijnen, "Implementation of health technology: Directions for research and practice," Front Digit Health, vol. 4, pp. 1030194, Nov. 2022. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9685419/
