Designing Scalable and Interoperable Data Architectures for Seamless Healthcare Systems Integration and Real-Time Clinical Decision Support
Keywords:
Healthcare Data Architecture, Interoperability, System Integration, Cloud Computing, Real-Time Clinical Decision Support, HL7 FHIR, Data Security, AI in HealthcareAbstract
The increasing complexity of healthcare data and the need for real-time decision support necessitate scalable and interoperable data architectures. This paper explores the fundamental principles, challenges, and best practices for designing healthcare data architectures that support seamless system integration, secure data exchange, and real-time analytics. It highlights the role of cloud computing, AI, and standardized interoperability frameworks such as HL7 FHIR in optimizing healthcare workflows. The study further examines existing literature on healthcare data integration and presents practical implementation strategies, along with performance evaluations of various architectural models. The proposed design framework is validated using case studies and empirical data. The findings underscore the importance of adopting modular, cloud-based, and AI-driven architectures to enhance healthcare delivery and patient outcomes.
References
D’Amore, J. D., Mandel, J. C., Kreda, D. A., et al. (2018). Interoperability Progress and Remaining Challenges. JAMIA, 25(8), 1102-1110.
Dubovitskaya, A., Xu, Z., Ryu, S., et al. (2020). Secure and Interoperable Data Exchange in Healthcare. Blockchain in Healthcare, 9, 100069.
Mandel, J. C., Kreda, D. A., Mandl, K. D., et al. (2016). SMART on FHIR: A Platform for Interoperability. Journal of Biomedical Informatics, 63, 93-101.
Raghupathi, W., & Raghupathi, V. (2014). Big Data Analytics in Healthcare. Journal of Big Data, 1(1), 1-17.
Rieke, N., Hancox, J., Li, W., et al. (2020). Federated Learning for Medical AI. Nature Machine Intelligence, 2, 266-274.
Zhang, X., Zhang, Y., Shi, W., et al. (2018). Cloud-Based Healthcare Data Integration. IEEE Transactions on Cloud Computing, 6(4), 820-830.
Kuo, A. M. (2019). Opportunities and Challenges of Cloud Computing in Healthcare IT. Healthcare Informatics Research, 25(3), 158-162.
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep Learning in Healthcare. Journal of Biomedical Informatics, 84, 93-102.
Chen, M., Hao, Y., Hwang, K., et al. (2017). Edge Computing for Healthcare AI. IEEE Internet of Things Journal, 5(1), 37-54.
Kumar, R., Tripathi, R., & Kumar, A. (2021). Healthcare Data Security with Blockchain. Computers & Security, 103, 102159.
