A COMPREHENSIVE FRAMEWORK FOR ACHIEVING SEAMLESS HEALTHCARE DATA INTEGRATION ACROSS HETEROGENEOUS SYSTEMS FOR ENHANCED CLINICAL DECISION-MAKING AND PATIENT OUTCOMES

Authors

  • Sankaranarayanan S Principal Engineer , Sagarsoft (India) Limited, Author

Keywords:

 healthcare data integration, interoperability, EHRs, clinical decision support, health IT, , big data, data standardization, HL7 FHIR, patient outcomes

Abstract

Healthcare data integration remains a critical challenge and opportunity in modern medicine. As electronic health records (EHRs), genomics, imaging, and real-time monitoring data proliferate, integrating these diverse and often incompatible systems is essential to enable accurate clinical decision-making and improve patient outcomes. This paper proposes a comprehensive integration framework that emphasizes interoperability, secure data sharing, and the use of standardized protocols. The framework is evaluated through a multi-layer model involving data sources, transformation engines, and analytics platforms. The paper concludes with policy implications and recommendations for seamless integration across health institutions.

References

1. Tang, P. C., Ash, J. S., & Bates, D. W. (2006). Personal health records: definitions, benefits, and strategies. JAMIA, 13(2), 121–126. Link

2. Kopparapu, V.S. (2025). Machine Learning-Driven Healthcare Fraud Detection: A Comprehensive Analysis of FAMS Implementation and Outcomes. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 2055–2063. https://doi.org/10.32628/CSEIT2511122162055

3. Mandl, K. D., Tang, P. C., & Halamka, J. D. (2008). Early experiences with personal health records. JAMIA, 15(1), 1–7. Link

4. Mandel, J. C., Kreda, D. A., & Mandl, K. D. (2016). SMART on FHIR. JAMIA, 23(5), 899–908. Link

5. Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in healthcare. Health Affairs, 33(7), 1139–1145. Link

6. Kopparapu, V.S. (2025). Artificial Intelligence in Remote Patient Monitoring: A Comprehensive Review of Wearable Technology Integration in Modern Healthcare. International Research Journal of Modernization in Engineering Technology and Science, 7(2), 2272–2278. https://doi.org/10.56726/IRJMETS67549

7. Safran, C., Bloomrosen, M., & Hammond, W. E. (2007). Toward a national framework for secondary use of health data. JAMIA, 14(1), 1–9. Link

8. Ginsburg, G. S., & Phillips, K. A. (2018). Precision medicine. Health Affairs, 37(5), 694–701. Link

9. Kopparapu, V.S. (2025). Healthcare Insurance Data Infrastructure: A Comprehensive Analysis of EDI Standards and Processing Systems. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 8(1), 2341–2353. https://doi.org/10.34218/IJRCAIT_08_01_170

10. Zhang, G. Q., Cui, L., & Mueller, R. (2018). The National Sleep Research Resource. JAMIA, 25(10), 1351–1358. Link

11. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare. Health Information Science and Systems, 2(1), 3. Link

12. Angraal, S., Krumholz, H. M., & Schulz, W. L. (2017). Blockchain technology in health care. Circulation: CQO, 10(10), e003800. Link

13. Johnson, K. B., Wei, W. Q., & Weeraratne, D. (2021). Precision medicine and AI in healthcare. Clinical and Translational Science, 14(4), 1212–1223. Link

14. Kopparapu, V.S. (2025). Cloud-Integrated Artificial Intelligence Framework for MRI Analysis: Advancing Radiological Diagnostics Through Automated Solutions. International Journal of Computer Engineering and Technology (IJCET), 16(1), 2892–2907. https://doi.org/10.34218/IJCET_16_01_203

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Published

2025-05-04

How to Cite

Sankaranarayanan S. (2025). A COMPREHENSIVE FRAMEWORK FOR ACHIEVING SEAMLESS HEALTHCARE DATA INTEGRATION ACROSS HETEROGENEOUS SYSTEMS FOR ENHANCED CLINICAL DECISION-MAKING AND PATIENT OUTCOMES. International Journal of Advanced Research in Cloud Computing, 6(3), 1-4. https://ijarcc.com/index.php/home/article/view/IJARCC.06.03.001