Interoperable Health Data Architecture: Enabling Seamless Patient Engagement Across Multi-Platform Systems

Authors

  • Jagdish Kumar Vinjamuri

Keywords:

Interoperability, Health Data Architecture, Patient Engagement, Multi-Platform Systems, Electronic Health Records (EHR), HL7 FHIR, mHealth, Telemedicine, Data Integration, Healthcare IT

Abstract

- The healthcare sector's rapid digital modernization is creating an insatiable demand for health data architectures designed to enable interoperable health data. An interoperable health data architecture can connect interoperable systems and platforms, disseminate data among different constituents of health data, and improve patient engagement via different digital channels, such as electronic health records (EHRs), from cloud-based applications to mobile health apps and wearables, and telehealth. This paper has described how the design, development, and evaluation of an interoperable health data architecture can facilitate patient engagement. The benefits of using health data exchanges based on data exchange formats, protocols, and/or APIs like HL7 FHIR and open APIs are also described to allow real-time use and synchronization of patient data as patients and healthcare professionals share and exchange data with health data exchanges. In addition, the importance of privacy-preserving sharing of data, data governance, and collaborative or user-centred design and development are described to build trust and increase or improve the adoption and use of common data exchange products utilised by patients and healthcare professionals. In summary, the proposed interoperable health data architecture is a step towards a unified and patient data accessible ecosystem, improving continuity of care, informed decision-making, and fostering an informed and engaged patient, as well as showing the protocols linking to proposed best practices and the key challenges that must be overcome for scalable and sustainable interoperability in a fragmented digital landscape of health care

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References

Zhang, L., Wang, Y., Liu, Y., & Li, F. (2021). Recent advances in microbial electrolysis cells for hydrogen production: A review. Bioresource Technology, 319, 124222. https://doi.org/10.1016/j.biortech.2020.124222

Xie, L., Zhou, Y., Wang, Z., & Ren, Z. J. (2020). Membrane materials and configurations for microbial electrolysis cells: A review. Renewable and Sustainable Energy Reviews, 132, 110032. https://doi.org/10.1016/j.rser.2020.110032

Chen, X., Liu, J., Zhang, T., & Huang, Y. (2022). Mechanisms of electron transfer in microbial electrolysis cells and strategies for performance enhancement. Journal of Power Sources, 524, 231093. https://doi.org/10.1016/j.jpowsour.2022.231093

Li, M., Sun, W., Yang, L., & Zhang, Q. (2023). Voltage optimization in microbial electrolysis cells for efficient hydrogen production. Energy Reports, 9, 453–461. https://doi.org/10.1016/j.egyr.2022.11.072

Rahman, M., Ko, M., Jin, Y., & Park, R. W. (2021). Impact of interoperable electronic health records on patient outcomes and healthcare efficiency: A systematic review.

BMC Health Services Research, 21(1), 1065. https://doi.org/10.1186/s12913-021-07071-4

Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre Díez, I., López-Coronado, M., & Saleem, K. (2023). Interoperability of heterogeneous health information systems: A systematic literature review. BMC Medical Informatics and Decision Making, 23(1), 115. https://doi.org/10.1186/s12911-023-02115-5

Wang, Y., Yu, Y., & Zhang, H. (2022). OpenEHR-based smart health records: Achievements and challenges. Journal of Biomedical Informatics, 127, 104017. https://doi.org/10.1016/j.jbi.2022.104017

Zhang, Y., Milinovich, A., & Wang, Y. (2023). Designing interoperable health care services based on Fast Healthcare Interoperability Resources: Literature review. JMIR Medical Informatics, 11(1), e44842. https://doi.org/10.2196/44842

Barker, W., Chang, W., Everson, J., Gabriel, M., Patel, V., Richwine, C., & Strawley, C. (2024). The evolution of health information technology for enhanced patient-centric care in the United States: Data-driven descriptive study. Journal of Medical Internet Research, 26, e59791. https://doi.org/10.2196/59791

Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The Fast Health Interoperability Resources (FHIR) Standard: Systematic literature review of implementations, applications, challenges, and opportunities. JMIR Medical Informatics, 9(7), e21929. https://doi.org/10.2196/21929

World Health Organization. (2021). Global strategy on digital health 2020–2025. https://www.who.int/publications/i/item/9789240020924

Johnson, C., Richwine, C., & Patel, V. (2022). Integrating a patient engagement app into an electronic health record-enabled workflow using interoperability standards. Journal of the American Medical Informatics Association, 29(1), 12–20. https://doi.org/10.1093/jamia/ocab171

Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The Fast Health Interoperability Resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 9(7), e21929. https://doi.org/10.2196/21929

Raghupathi, W., & Raghupathi, V. (2022). Current and emerging technologies for health information systems: A review. Health Information Science and Systems, 10(1), 1–8. https://doi.org/10.1007/s13755-021-00154-5

Mandl, K. D., Gottlieb, D., Ellis, A., & Mandl, K. (2020). Leveraging SMART on FHIR to create interoperable apps. npj Digital Medicine, 3, 1–4.https://doi.org/10.1038/s41746-020-0220-8

Nabavi, S. A., Yu, C. Y., Lee, T. M., & Lin, H. C. (2023). A framework for API-based interoperability in healthcare. BMC Medical Informatics and Decision Making, 23(1), 145. https://doi.org/10.1186/s12911-023-02132-7

Chung, K., Park, Y. R., & Han, H. (2022). Cloud computing in healthcare: A scoping review of benefits, risks, and architectures. Healthcare Informatics Research, 28(3), 207–218. https://doi.org/10.4258/hir.2022.28.3.207

Singh, S., Chandna, P., & Singh, N. (2023). Blockchain technology in healthcare: A systematic review and future research directions. Journal of Biomedical Informatics, 142, 104376. https://doi.org/10.1016/j.jbi.2023.104376

Alami, H., Gagnon, M. P., Fortin, J. P., & Lévesque-Chouinard, A. (2020). Healthcare stakeholders’ perceptions of barriers to and facilitators of the adoption of health information technology: A systematic review. Journal of Medical Internet Research, 22(4), e15144. https://doi.org/10.2196/15144

Cresswell, K. M., & Hirose, M. (2021). Advancing qualitative research methods in health informatics: Using grounded theory to explore data interoperability. BMJ

Health & Care Informatics, 28(1), e100225. https://doi.org/10.1136/bmjhci-2020-100225

Vindrola-Padros, C., & Vindrola-Padros, B. (2022). A practical guide to using rapid qualitative research methods in healthcare. BMC Health Services Research, 22, 491. https://doi.org/10.1186/s12913-022-07834-4

Tong, H., Luo, X., Zhang, M., & Liu, H. (2023). Comparing digital health platforms for interoperability and patient engagement: A case study analysis. International

Journal of Medical Informatics, 178, 105204. https://doi.org/10.1016/j.ijmedinf.2023.105204

Kariuki, J. M., Mwaniki, P., & Omondi, L. (2021). Understanding implementation challenges of interoperable digital health systems in low-resource settings. Journal of Global Health Reports, 5, e2021058. https://doi.org/10.29392/001c.23678

Nguyen, L., Bellucci, E., & Nguyen, L. T. (2020). Electronic health records implementation: An evaluation of information system impact and contingency factors.

International Journal of Medical Informatics, 141, 104218. https://doi.org/10.1016/j.ijmedinf.2020.104218

Al-Turjman, F., & Alturjman, S. (2020). Context-sensitive access control and data integration in e-health systems. Journal of Ambient Intelligence and Humanized Computing, 11, 3585–3599. https://doi.org/10.1007/s12652-019-01387-z

Chung, K., Park, Y. R., & Han, H. (2022). Cloud computing in healthcare: A scoping review of benefits, risks, and architectures. Healthcare Informatics Research, 28(3), 207–218. https://doi.org/10.4258/hir.2022.28.3.207

Nabavi, S. A., Yu, C. Y., Lee, T. M., & Lin, H. C. (2023). A framework for API-based interoperability in healthcare. BMC Medical Informatics and Decision Making, 23(1), 145. https://doi.org/10.1186/s12911-023-02132-7

Raghupathi, W., & Raghupathi, V. (2022). Current and emerging technologies for health information systems: A review. Health Information Science and Systems, 10(1), 1–8. https://doi.org/10.1007/s13755-021-00154-5

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Published

2025-11-03

How to Cite

1.
Vinjamuri JK. Interoperable Health Data Architecture: Enabling Seamless Patient Engagement Across Multi-Platform Systems. J Neonatal Surg [Internet]. 2025Nov.3 [cited 2025Nov.21];14(32S):9303-15. Available from: https://jneonatalsurg.com/index.php/jns/article/view/9470

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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