Smart Cloud Ecosystems for Precision Healthcare Decision Systems
DOI:
https://doi.org/10.5281/zenodo.21412847Keywords:
Data Ecosystems, Cloud Native, Healthcare Systems, Predictive Analytics, Decision Systems, Microservices, Service Mesh, Data Contracts, Precision Healthcare, Disease Prediction, Hybrid Cloud, Multi Cloud, Scalability, Resilience, Disaster Recovery, Data Protection, Access Control, Regulatory Compliance, Cloud Infrastructure, Service Architecture.Abstract
The paper discusses the critical importance of data ecosystems within cloud-native environments designed for predictive and automated decision-making systems in healthcare. The evolving requirements for healthcare service delivery necessitate an alternative architectural paradigm that goes beyond traditional monolithic systems and monolithic software applications hosted in dedicated private data centers or single public cloud infrastructures. Service-oriented deployments are best modeled using microservices and service meshes, data contracts, and independent deployments.
Precision healthcare addresses disease prevention, detection, forecasting, diagnosis, management, and outcome prediction for individuals or specific population sets. It leverages underlying compute, storage, and networking services designed for traditional enterprise workloads with strict recoverability and business continuity requirements. Frameworks for scalability, resilience, and disaster recovery translate elasticity features from the public cloud domain, while supporting cloud-native solutions in hybrid or multi-cloud environments. The success of such an approach is entirely dependent on effective data protection techniques, access controls, and the ability to demonstrate compliance with multiple regulations.
References
1. Agrawal, A., Choudhary, A., & Sharma, R. (2021). Cloud computing for healthcare: Opportunities, challenges, and applications. Journal of Healthcare Engineering, 2021, 1–15.
2. Balch, J. A., Ruppert, M. M., Loftus, T. J., Guan, Z., Ren, Y., Upchurch, G. R., Ozrazgat-Baslanti, T., Rashidi, P., & Bihorac, A. (2023). Machine learning–enabled clinical information systems using Fast Healthcare Interoperability Resources data standards: Scoping review. JMIR Medical Informatics, 11, e48297.
3. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
4. Blobel, B., Ruotsalainen, P., Brochhausen, M., Prestes, E., & Houghtaling, M. A. (2023). Designing and managing advanced, intelligent and ethical health and social care ecosystems. Journal of Personalized Medicine, 13(8), 1209.
5. Bohr, A., & Memarzadeh, K. (2021). Artificial intelligence in healthcare. Academic Press.
6. Mattaparthi, R. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11, 1084.
7. Chen, M., Yang, J., Zhu, X., Wang, X., Liu, S., & Zhang, Y. (2021). Smart healthcare systems: Applications, challenges and future trends. Future Generation Computer Systems, 124, 255–272.
8. Dai, W., Brisimi, T. S., Adams, W. G., Mela, T., Saligrama, V., & Paschalidis, I. C. (2021). Prediction of hospitalization due to heart diseases by machine learning. IEEE Journal of Biomedical and Health Informatics, 25(3), 985–995.
9. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
10. Elayan, H., Aloqaily, M., & Guizani, M. (2021). Digital twin for intelligent healthcare: Vision and future perspectives. IEEE Network, 35(3), 80–86.
11. Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2021). The Internet of Things for health care: A comprehensive survey. IEEE Access, 9, 678–708.
12. Krishankumar, R., Sivagami, R., Saha, A., Rani, P., Arun, K., & Ravichandran, K. S. (2022). Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information. Applied Intelligence, 52(12), 13497–13519.
13. Lee, H. Y., Lee, K. H., Lee, K. H., Erdenbayar, U., Hwang, S., Lee, E. Y., Lee, J. H., Kim, H. J., Park, S. B., Park, J. W., Chung, T. Y., Kim, T. H., & Youk, H. (2023). Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform. Digital Health, 9.
14. Kolla, S. H. (2023). Large Language Model-Driven Enterprise Service Intelligence for Digital Workflow Transformation. International Journal of Research and Applied Innovations, 6(1), 8380-8391.
15. Lee, K. H., Urtnasan, E., Hwang, S., Lee, H. Y., Lee, J. H., Koh, S. B., & Youk, H. (2022). Concept and proof of the lifelog bigdata platform for digital healthcare and precision medicine on the cloud. Yonsei Medical Journal, 63(Suppl.), S84–S92.
16. Lee, K., Lee, J., Hwang, S., Kim, Y., Lee, Y., Urtnasan, E., Koh, S. B., & Youk, H. (2022). Diffusion of a lifelog-based digital healthcare platform for future precision medicine: Data provision and verification study. Journal of Personalized Medicine, 12(5), 803.
17. Nan, J., & Xu, L. Q. (2023). Designing interoperable healthcare services based on Fast Healthcare Interoperability Resources: Literature review. JMIR Medical Informatics, 11, e44842.
18. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
19. Oh, S., Joo, H. J., Sohn, J. W., Park, S., Jang, J. S., Seong, J., Park, K. J., & Lee, S. H. (2023). Cloud-based digital healthcare development for precision medical hospital information system. Personalized Medicine, 20(5), 435–444.
20. Santos, L. O. B. S., Pires, L. F., Martinez, V. G., Moreira, J. L. R., & Guizzardi, R. S. S. (2023). Personal Health Train architecture with dynamic cloud staging. SN Computer Science, 4(1), 14.
21. Stevens, G., Hantson, L., Larmuseau, M., & Verdonck, P. (2022). A human-centered, health data-driven ecosystem. Discover Health Systems, 1, 10.
22. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
23. Topol, E. (2021). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 27(1), 44–56.
24. Wang, L., Alexander, C. A., & Rajan, S. (2021). Artificial intelligence in precision medicine. Journal of Personalized Medicine, 11(5), 367.
25. Xu, B., Xu, L. D., Cai, H., Xie, C., Hu, J., & Bu, F. (2021). Ubiquitous data accessing method in IoT-based information systems. IEEE Transactions on Industrial Informatics, 17(2), 1475–1483.
26. Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2021). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 15(2), 1888–1899.
27. Ahmad, M., et al. (2022). Federated learning for healthcare informatics: A comprehensive survey. IEEE Access, 10, 80864–80895.
28. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
29. Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2022). A smart healthcare monitoring framework using IoT and cloud computing. Future Generation Computer Systems, 127, 361–376.
30. Arshad, J., Azam, M. A., Rehman, M. H., & Loo, J. (2022). Security and privacy in cloud-enabled healthcare systems: A survey. Journal of Network and Computer Applications, 198, 103302.
31. Benda, N. C., Veinot, T., Sieck, C. J., & Ancker, J. S. (2022). Broadband internet access is a social determinant of health. American Journal of Public Health, 112(8), 1123–1130.
32. Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2022). Wearable health technology and electronic health record integration. NPJ Digital Medicine, 5, 89.
33. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
34. El-Sappagh, S., Ali, F., Hendawi, A., Jang, J. H., & Kwak, K. S. (2022). Comprehensive digital health platform integrating AI, IoT and cloud computing. Sensors, 22(9), 3382.
35. Glicksberg, B. S., et al. (2022). Patient representation learning for precision health. NPJ Digital Medicine, 5, 43.
36. Kaissis, G., Makowski, M., Rückert, D., & Braren, R. (2022). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 4(3), 192–203.
37. Li, X., Guo, L., & Chen, J. (2022). Edge intelligence for smart healthcare systems: A survey. IEEE Internet of Things Journal, 9(12), 9398–9418.
38. Rieke, N., et al. (2022). The future of digital health with federated learning. NPJ Digital Medicine, 5, 119.
39. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
40. Seneviratne, M. G., et al. (2022). Bridging the implementation gap of AI in healthcare. BMJ Health & Care Informatics, 29(1), e100541.
41. Shilo, S., Rossman, H., & Segal, E. (2022). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 28(1), 29–38.
42. Sun, J., et al. (2023). Artificial intelligence and cloud-enabled clinical decision support systems: Recent advances and future directions. Artificial Intelligence in Medicine, 140, 102563.
43. Wornow, M., et al. (2023). The shaky foundations of clinical foundation models. Nature, 624, 246–252.
44. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260.
45. Zhang, X., Wang, H., & Chen, Y. (2023). Intelligent cloud-edge collaborative computing for smart healthcare applications. Future Generation Computer Systems, 145, 250–264.
46. Vorisek, C. N., Lehne, M., Klopfenstein, S. A. I., Mayer, P. J., Bartschke, A., Haese, T., & Thun, S. (2022). Fast Healthcare Interoperability Resources (FHIR) for interoperability in health research: Systematic review. JMIR Medical Informatics, 10(7), e35724.
47. Griffin, A. C., He, L., Sunjaya, A. P., King, A. J., Khan, Z., Nwadiugwu, M., Douthit, B., Subbian, V., Nguyen, V., Braunstein, M., Jaffe, C., & Schleyer, T. (2022). Clinical, technical, and implementation characteristics of real-world health applications using FHIR. JAMIA Open, 5(4), ooac077.
48. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
49. Blobel, B., Ruotsalainen, P., Brochhausen, M., Prestes, E., & Houghtaling, M. A. (2023). Designing and managing advanced, intelligent and ethical health and social care ecosystems. Journal of Personalized Medicine, 13(8), 1209.
50. Balch, J. A., Ruppert, M. M., Loftus, T. J., Guan, Z., Ren, Y., Upchurch, G. R., Ozrazgat-Baslanti, T., Rashidi, P., & Bihorac, A. (2023). Machine learning–enabled clinical information systems using Fast Healthcare Interoperability Resources data standards: Scoping review. JMIR Medical Informatics, 11, e48297.
51. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
52. Kramer, M. A. (2023). Reducing FHIR "profiliferation": A data-driven approach. AMIA Annual Symposium Proceedings, 2022, 634–643.
53. Anand, G., & Sadhna, D. (2023). Electronic health record interoperability using FHIR and blockchain: A bibliometric analysis and future perspective. Perspectives in Clinical Research, 14(4), 161–166.
54. Chaballout, B., Shaw, R. J., & Reuter-Rice, K. (2023). The SMART healthcare solution. Advances in Precision Medicine, 2(1).
55. Kaissis, G., Makowski, M., Rückert, D., & Braren, R. (2022). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 4(3), 192–203.
56. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
57. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2022). The future of digital health with federated learning. NPJ Digital Medicine, 5, 119.
58. Shilo, S., Rossman, H., & Segal, E. (2022). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 28(1), 29–38.
59. Glicksberg, B. S., et al. (2022). Patient representation learning for precision health. NPJ Digital Medicine, 5, 43.
60. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
61. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
62. Seneviratne, M. G., et al. (2022). Bridging the implementation gap of AI in healthcare. BMJ Health & Care Informatics, 29(1), e100541.
63. Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2022). Wearable health technology and electronic health record integration. NPJ Digital Medicine, 5, 89.
64. Benda, N. C., Veinot, T., Sieck, C. J., & Ancker, J. S. (2022). Broadband internet access is a social determinant of health. American Journal of Public Health, 112(8), 1123–1130.
65. Lee, K. H., Urtnasan, E., Hwang, S., Lee, H. Y., Lee, J. H., Koh, S. B., & Youk, H. (2022). Concept and proof of the lifelog big data platform for digital healthcare and precision medicine on the cloud. Yonsei Medical Journal, 63(Suppl.), S84–S92.
66. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
67. Lee, K., Lee, J., Hwang, S., Kim, Y., Lee, Y., Urtnasan, E., Koh, S. B., & Youk, H. (2022). Diffusion of a lifelog-based digital healthcare platform for future precision medicine. Journal of Personalized Medicine, 12(5), 803.
68. Lee, H. Y., Lee, K. H., Lee, K. H., Erdenbayar, U., Hwang, S., Lee, E. Y., et al. (2023). Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using the MEDBIZ platform. Digital Health, 9.
69. Stevens, G., Hantson, L., Larmuseau, M., & Verdonck, P. (2022). A human-centered, health data-driven ecosystem. Discover Health Systems, 1, 10.
70. Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
71. Santos, L. O. B. S., Pires, L. F., Martinez, V. G., Moreira, J. L. R., & Guizzardi, R. S. S. (2023). Personal Health Train architecture with dynamic cloud staging. SN Computer Science, 4(1), 14.
72. Nan, J., & Xu, L. Q. (2023). Designing interoperable healthcare services based on Fast Healthcare Interoperability Resources: Literature review. JMIR Medical Informatics, 11, e44842.
73. Oh, S., Joo, H. J., Sohn, J. W., Park, S., Jang, J. S., Seong, J., Park, K. J., & Lee, S. H. (2023). Cloud-based digital healthcare development for precision medical hospital information systems. Personalized Medicine, 20(5), 435–444.
74. Li, X., Guo, L., & Chen, J. (2022). Edge intelligence for smart healthcare systems: A survey. IEEE Internet of Things Journal, 9(12), 9398–9418.
75. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
76. El-Sappagh, S., Ali, F., Hendawi, A., Jang, J. H., & Kwak, K. S. (2022). Comprehensive digital health platform integrating AI, IoT, and cloud computing. Sensors, 22(9), 3382.
77. Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K. S. (2022). A smart healthcare monitoring framework using IoT and cloud computing. Future Generation Computer Systems, 127, 361–376.
78. Ahmad, M., et al. (2022). Federated learning for healthcare informatics: A comprehensive survey. IEEE Access, 10, 80864–80895.
79. Arshad, J., Azam, M. A., Rehman, M. H., & Loo, J. (2022). Security and privacy in cloud-enabled healthcare systems: A survey. Journal of Network and Computer Applications, 198, 103302.
80. Wang, L., Alexander, C. A., & Rajan, S. (2021). Artificial intelligence in precision medicine. Journal of Personalized Medicine, 11(5), 367.
81. Bandi, V. D. V. K. (2023). MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms.
82. Chen, M., Yang, J., Zhu, X., Wang, X., Liu, S., & Zhang, Y. (2021). Smart healthcare systems: Applications, challenges and future trends. Future Generation Computer Systems, 124, 255–272.
83. Elayan, H., Aloqaily, M., & Guizani, M. (2021). Digital twin for intelligent healthcare: Vision and future perspectives. IEEE Network, 35(3), 80–86.
84. Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2021). Health-CPS: Healthcare cyber-physical systems assisted by cloud and big data. IEEE Systems Journal, 15(2), 1888–1899.
85. Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2021). The Internet of Things for health care: A comprehensive survey. IEEE Access, 9, 678–708.
86. Xu, B., Xu, L. D., Cai, H., Xie, C., Hu, J., & Bu, F. (2021). Ubiquitous data accessing method in IoT-based information systems. IEEE Transactions on Industrial Informatics, 17(2), 1475–1483.
87. Krishankumar, R., Sivagami, R., Saha, A., Rani, P., Arun, K., & Ravichandran, K. S. (2022). Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information. Applied Intelligence, 52(12), 13497–13519.
88. Topol, E. (2021). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 27(1), 44–56.
89. Bohr, A., & Memarzadeh, K. (2021). Artificial intelligence in healthcare. Academic Press.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2023 The American Journal of Analytics and Artificial Intelligence (AJAAI)
This work is licensed under a Creative Commons Attribution 4.0 International License.
