Latency-Optimized Interoperability Design
Keywords:
HL7 Fast Healthcare Interoperability Resources (FHIR), Real-Time Healthcare Data Exchange, Event-Driven Architecture, FHIR RESTful Services, Healthcare Systems Integration, Low-Latency Clinical Data Processing, Interoperability Standards in Healthcare, Event-Based Integration Patterns, Cloud-Enabled Healthcare Platforms, Secure Health Information Exchange, Protocol Translation Frameworks, Healthcare API Design, Life-Critical Clinical Systems, Publish–Subscribe Mechanisms, Open-Source Healthcare Integration Frameworks, Scalable Health IT Architectures.Abstract
The use of Health Level 7's Fast Healthcare Interoperability Resources (FHIR) for facilitating real-time data exchange among healthcare systems in life-critical scenarios enables patients to receive timely and appropriate treatment whenever and wherever they need it. FHIR provides models, rules, and interfaces for data exchange between computerized applications operating in the healthcare domain. Being a web-based standard, it can be easily deployed using an event-based architecture. Furthermore, it allows for online communications and moves away from standard file batch processes. The eventing mechanism facilitates the push of data from an event publisher to interested subscribers and notifies them of updates for resources of interest.
The increasing diversity and complexity of healthcare information systems, along with the demand for timely integration of data across these systems, point toward the need for a real-time event-based integration approach using FHIR REST services. Open-source frameworks have been developed for achieving the required integration. However, architectural decisions in designing such frameworks have a significant impact on their ability to support data exchange with low latency. The selection of an appropriate integration architecture is also crucial for meeting requirements such as interaction with external enterprises, protocol translation, security, and compliance with cloud deployment models. Therefore, several architecture patterns commonly considered and deployed for event-based integration using FHIR are discussed. Each architecture pattern is detailed, along with its strengths and weaknesses for supporting real-time data exchange.
References
[1] Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: A standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899–908.
[2] Benson, T., & Grieve, G. (2021). Principles of Health Interoperability: SNOMED CT, HL7 and FHIR (3rd ed.). Springer.
[3] HL7 International. (2024). HL7 FHIR Release 5 Specification. Health Level Seven International.
[4] Decker, M., Lhotska, L., Kubicek, J., & Suchanek, M. (2021). Event-driven architectures for healthcare interoperability. Computer Methods and Programs in Biomedicine, 200, 105930.
[5] Unertl, K. M., Jaffa, H., Field, J. R., Price, L., Peterson, K. J., & Richardson, J. E. (2020). SMART on FHIR implementation guide: Enhancing clinical decision support. Applied Clinical Informatics, 11(5), 746–754.
[6] Kamel Boulos, M. N., Brewer, A. C., Karimkhani, C., Buller, D. B., & Dellavalle, R. P. (2019). Mobile medical and health apps: State of the art, concerns, regulatory control. Journal of Medical Internet Research, 21(3), e13369.
[7] Groppe, J., & Groppe, S. (2020). Real-time data integration in healthcare using publish-subscribe systems. Future Generation Computer Systems, 108, 1042–1055.
[8] Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604.
[9] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
[10] Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5(1), 1–18.
[11] Martínez-García, A., Escudero, J. M., & García-Saiz, D. (2022). Cloud-based interoperability frameworks for healthcare IoT. IEEE Access, 10, 34421–34434.
[12] Rumbold, J. M. M., & Pierscionek, B. K. (2017). The effect of the general data protection regulation on medical research. Journal of Medical Internet Research, 19(2), e47.
[13] O’Connor, M. J., & Das, A. K. (2014). A methodology for modeling clinical knowledge in SNOMED CT. Journal of Biomedical Informatics, 50, 178–187.
[14] Payne, T. H., Corley, S., Cullen, T. A., Gandhi, T. K., Harrington, L., Kuperman, G. J., Mattison, J. E., McCallie, D., McDonald, C. J., Murphy, S., et al. (2018). Report of the AMIA EHR 2020 task force. Journal of the American Medical Informatics Association, 22(5), 110–112.
[15] Yu, F., Bilberg, A., & Rasmussen, J. (2021). Microservices architecture for healthcare systems. Procedia Computer Science, 180, 388–397.
[16] Haux, R. (2010). Medical informatics: Past, present, future. International Journal of Medical Informatics, 79(9), 599–610.
[17] Behl, A., & Behl, K. (2017). Cyberwar: The next threat to national security and what to do about it. Oxford University Press.
[18] El-Sappagh, S., Ali, F., Hendawi, A., & Kwak, K. S. (2020). A mobile health monitoring-and-treatment system based on integration of the Internet of Things and cloud computing in medical applications. IEEE Access, 8, 19877–19898.
[19] Sittig, D. F., & Singh, H. (2016). A socio-technical approach to preventing, mitigating, and recovering from EHR-related safety hazards. Journal of the American Medical Informatics Association, 23(4), 641–647.
[20] Bender, D., & Sartipi, K. (2013). HL7 FHIR: An agile and RESTful approach to healthcare information exchange. Proceedings of the IEEE 26th International Symposium on Computer-Based Medical Systems.
[21] Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2017). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88–95.
[22] Kuo, A. M. H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of Medical Internet Research, 13(3), e67.
[23] Lehne, M., Luijten, S., Vellinga, J., & Chablis, G. (2019). The use of FHIR in digital health. Studies in Health Technology and Informatics, 267, 52–58.
[24] Alshahrani, A., & Sheikh, A. (2020). Interoperability challenges in healthcare information systems. Journal of Healthcare Engineering, 2020, 1–13.
[25] Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed Research International, 2015, 1–16.
[26] Perera, C., Liu, C. H., & Jayawardena, S. (2015). The emerging Internet of Things marketplace. IEEE Cloud Computing, 2(4), 50–58.
[27] Chen, M., Ma, Y., Li, Y., Wu, D., Zhang, Y., & Youn, C. H. (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems. IEEE Communications Magazine, 55(1), 54–61.
[28] Gagnon, M. P., Ghandour, E. K., Talla, P. K., Simonyan, D., Godin, G., Labrecque, M., & Ouimet, M. (2014). Electronic health record acceptance by physicians. Journal of Medical Systems, 38(2), 1–10.
[29] Kouroubali, A., & Katehakis, D. G. (2019). The new European interoperability framework as a facilitator of digital transformation for citizen-centric public services. Journal of Biomedical Informatics, 94, 103166.
[30] Hashmi, M. F., Katiyar, S., Keskar, A. G., Bokde, N. D., & Geem, Z. W. (2020). Efficient healthcare data management using machine learning and cloud computing. Sensors, 20(15), 4238.
[31] Zhang, P., White, J., Schmidt, D. C., Lenz, G., & Rosenbloom, S. T. (2018). FHIRChain: Applying blockchain to securely and scalably share clinical data. Computational and Structural Biotechnology Journal, 16, 267–278.
[32] Roehrs, A., da Costa, C. A., & Righi, R. R. (2017). OmniPHR: A distributed architecture model to integrate personal health records. Journal of Biomedical Informatics, 71, 70–81.
[33] Hoy, M. B. (2016). Personal health records. Medical Reference Services Quarterly, 35(3), 213–224.
[34] Abidi, S. S. R., & Abidi, S. R. (2007). Ontology-based modeling of clinical pathways. Journal of Biomedical Informatics, 40(6), 722–738.
[35] Chen, Y., Argentinis, J. D., & Weber, G. (2016). IBM Watson: How cognitive computing can be applied to big data challenges. IBM Research White Paper.
[36] Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare. Health Information Science and Systems, 2(1), 1–10.
[37] Meystre, S. M., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., & Lehmann, C. U. (2017). Clinical data reuse or secondary use. Yearbook of Medical Informatics, 26(1), 54–62.
[38] Koufi, V., & Malamateniou, F. (2015). Privacy-preserving solutions in eHealth. Studies in Health Technology and Informatics, 213, 245–248.
[39] Schreiber, G., Akkermans, H., Anjewierden, A., et al. (2000). Knowledge Engineering and Management. MIT Press.
[40] Behl, A., & Behl, K. (2019). Cybersecurity and cyberwar. Oxford University Press.
[41] Lian, J., Zhou, M., & Wang, F. Y. (2017). Cloud computing for electronic health records. IEEE Intelligent Systems, 32(3), 10–14.
[42] Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records. Nature Reviews Genetics, 13(6), 395–405.
[43] Goldstein, M. M., & Thorpe, J. H. (2010). The first anniversary of the HITECH Act. Health Affairs, 29(6), 1042–1047.
[44] Wilkowska, W., & Ziefle, M. (2012). Privacy and data security in e-health. Health Policy and Technology, 1(3), 119–131.
[45] Kocabas, O., Soyata, T., & Aktas, M. K. (2018). Emerging security mechanisms for medical cyber-physical systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3), 866–880.
[46] Al-Janabi, S., Al-Shourbaji, I., Shojafar, M., & Shamshirband, S. (2017). Survey of main challenges in smart healthcare. IEEE Access, 5, 6765–6776.
[47] Bui, N., & Zorzi, M. (2011). Health care applications. IEEE Communications Magazine, 49(1), 110–117.
[48] Ristevski, B., & Chen, M. (2018). Big data analytics in medicine and healthcare. Journal of Integrative Bioinformatics, 15(3).
[49] Khan, F. A., Sadiq, S., & Qureshi, M. A. (2020). Interoperable IoT-based healthcare system. IEEE Access, 8, 228482–228497.
[50] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics. MIS Quarterly, 36(4), 1165–1188.
[51] Devarakonda, M., Kella, K., & Sarma, A. (2018). Healthcare interoperability using FHIR and RESTful web services. International Journal of Engineering & Technology, 7(3), 159–164.
[52] Khezr, S., Moniruzzaman, M., Yassine, A., & Benlamri, R. (2019). Blockchain technology in healthcare. IEEE Access, 7, 159–175.
[53] Zhou, L., & Parmanto, B. (2019). Reaching people with disabilities through accessible health information technology. Journal of Medical Internet Research, 21(2), e12222.
[54] Boehm, B. (2006). A view of 20th and 21st century software engineering. Proceedings of ICSE.
[55] Laplante, P. A., & Neill, C. J. (2004). The demise of the waterfall model. ACM Queue, 1(10), 10–12.
[56] Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures. Doctoral dissertation, University of California.
[57] Pahl, C. (2015). Containerization and microservices. IEEE Cloud Computing, 2(3), 24–31.
[58] Villamizar, M., Garcés, O., Castro, H., et al. (2016). Evaluating microservices architecture. Journal of Internet Services and Applications, 7(1), 1–20.
[59] Dragoni, N., et al. (2017). Microservices. Springer.
[60] Newman, S. (2015). Building microservices. O’Reilly.
[61] Erl, T. (2017). Service-oriented architecture. Prentice Hall.
[62] Tanenbaum, A. S., & Van Steen, M. (2017). Distributed systems. Pearson.
[63] Bass, L., Clements, P., & Kazman, R. (2013). Software architecture in practice. Addison-Wesley.
[64] Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns. Addison-Wesley.
[65] Fowler, M. (2018). Patterns of enterprise application architecture. Addison-Wesley.
[66] Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107–113.
[67] Zaharia, M., et al. (2016). Apache Spark. Communications of the ACM, 59(11), 56–65.
[68] Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka. Proceedings of NetDB.
[69] Akidau, T., et al. (2015). The dataflow model. Proceedings of VLDB.
[70] Stonebraker, M., et al. (2005). The 8 requirements of real-time stream processing. ACM SIGMOD Record, 34(4), 42–47.
[71] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things. Future Generation Computer Systems, 29(7), 1645–1660.
[72] Buyya, R., Broberg, J., & Goscinski, A. (2010). Cloud computing. Wiley.
[73] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST.
[74] ISO/IEC 27001. (2022). Information security management systems.
[75] ISO/IEC 27799. (2016). Health informatics security management.
[76] World Health Organization. (2021). Global strategy on digital health 2020–2025.
[77] OECD. (2023). Health data governance.
[78] European Commission. (2022). European Health Data Space proposal.
[79] National Academy of Medicine. (2021). Digital health interoperability.
[80] HIMSS. (2024). Interoperability in healthcare report.
[81] Topol, E. (2019). Deep medicine. Basic Books.
[82] Shortliffe, E. H., & Cimino, J. J. (2021). Biomedical informatics. Springer.
[83] McGinnis, J. M., et al. (2014). Best care at lower cost. National Academies Press.
[84] Stead, W. W., & Lin, H. S. (2009). Computational technology for effective health care. National Academies Press.
[85] Bates, D. W., Cohen, M., Leape, L. L., et al. (2001). Reducing adverse drug events. Journal of the American Medical Informatics Association, 8(4), 299–308.
[86] Berwick, D. M., Nolan, T. W., & Whittington, J. (2008). The triple aim. Health Affairs, 27(3), 759–769.
[87] Mandl, K. D., & Kohane, I. S. (2015). No small change for the health information economy. New England Journal of Medicine, 372(14), 1278–1281.
[88] Greenhalgh, T., et al. (2017). Beyond adoption: A new framework. Journal of Medical Internet Research, 19(11), e367.
[89] Sinsky, C., et al. (2016). Allocation of physician time. Annals of Internal Medicine, 165(11), 753–760.
[90] Adler-Milstein, J., & Jha, A. K. (2017). HITECH Act. Health Affairs, 36(5), 789–795.
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