A COMPREHENSIVE FRAMEWORK FOR ACHIEVING SEAMLESS HEALTHCARE DATA INTEGRATION ACROSS HETEROGENEOUS SYSTEMS FOR ENHANCED CLINICAL DECISION-MAKING AND PATIENT OUTCOMES
Keywords:
healthcare data integration, interoperability, EHRs, clinical decision support, health IT, big data, data standardization, HL7 FHIR, patient outcomesAbstract
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
Tang, P. C., Ash, J. S., & Bates, D. W. (2006). Personal health records: definitions, benefits, and strategies. JAMIA, 13(2), 121–126. Link
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
Mandl, K. D., Tang, P. C., & Halamka, J. D. (2008). Early experiences with personal health records. JAMIA, 15(1), 1–7. Link
Mandel, J. C., Kreda, D. A., & Mandl, K. D. (2016). SMART on FHIR. JAMIA, 23(5), 899–908. Link
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in healthcare. Health Affairs, 33(7), 1139–1145. Link
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
Safran, C., Bloomrosen, M., & Hammond, W. E. (2007). Toward a national framework for secondary use of health data. JAMIA, 14(1), 1–9. Link
Ginsburg, G. S., & Phillips, K. A. (2018). Precision medicine. Health Affairs, 37(5), 694–701. Link
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
Zhang, G. Q., Cui, L., & Mueller, R. (2018). The National Sleep Research Resource. JAMIA, 25(10), 1351–1358. Link
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare. Health Information Science and Systems, 2(1), 3. Link
Angraal, S., Krumholz, H. M., & Schulz, W. L. (2017). Blockchain technology in health care. Circulation: CQO, 10(10), e003800. Link
Johnson, K. B., Wei, W. Q., & Weeraratne, D. (2021). Precision medicine and AI in healthcare. Clinical and Translational Science, 14(4), 1212–1223. Link
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
