Unified State Machine Normalization across Heterogeneous Clinical Systems: An Architectural Pattern for Enterprise Status Federation
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I3P106Keywords:
Enterprise Status Federation, State Machine Normalization, Healthcare Interoperability, Change Data Capture, Schema-Agnostic Mapping, Transition Validation, Clinical Workflow, Event-Driven ArchitectureAbstract
Hospitals run separate applications for registration, insurance verification, laboratory, pharmacy and billing, and each one describes the status of a workflow in its own words. Staff who need to know where a patient's process stands make telephone calls, search several screens, or guess. This paper specifies an architectural pattern for enterprise status federation that leaves the source systems unchanged. Adapters extract committed changes as events, a versioned mapping registry translates each native status into one of eight canonical states and keeps the original value, a transition guard checks every encounter against a table of legal moves, and a read-only store serves one dashboard. We give a formal model with three propositions: pairwise translation needs n(n − 1) translators where a hub needs n adapters, replay under recorded mapping versions rebuilds the store, and a lenient guard flags a subset of what a strict guard flags. A synthetic study with illustrative parameters examines four design questions. Timestamp polling missed 2.0% of the status changes in a simulated day, while log-based capture missed none and delivered the median change in 0.5 s against 30.0 s. In an in-process benchmark, a pre-normalized store answered aggregate queries 3.2 to 5.0 times faster than query-time translation, and in a fan-out model a twelve-source query had a 99th-percentile latency of 903 ms against 100 ms for the store. A strict guard exposed 41% of the encounters hit by faults that map a code to a neighboring state, but it raised false alarms on 9.9% of encounters when sources skipped statuses. No real data were used, the results depend on assumed parameters, and sub-second dashboard response remains a design target.
References
[1] E. Kihuba et al., "Assessing the ability of health information systems in hospitals to support evidence-informed decisions in Kenya," Glob. Health Action, vol. 7, no. 1, Art. no. 24859, 2014, doi: 10.3402/gha.v7.24859.
[2] J. Adler-Milstein, A. J. Holmgren, P. Kralovec, C. Worzala, T. Searcy, and V. Patel, "Electronic health record adoption in US hospitals: The emergence of a digital "advanced use" divide," J. Am. Med. Inform. Assoc., vol. 24, no. 6, pp. 1142–1148, 2017, doi: 10.1093/jamia/ocx080.
[3] A. S. O’Malley, K. Draper, R. Gourevitch, D. A. Cross, and S. H. Scholle, "Electronic health records and support for primary care teamwork," J. Am. Med. Inform. Assoc., vol. 22, no. 2, pp. 426–434, 2015, doi: 10.1093/jamia/ocu029.
[4] A. Friedman et al., "A typology of electronic health record workarounds in small-to-medium size primary care practices," J. Am. Med. Inform. Assoc., vol. 21, no. e1, pp. e78–e83, 2014, doi: 10.1136/amiajnl-2013-001686.
[5] Health Level Seven International, HL7 Messaging Standard Version 2.5.1. Ann Arbor, MI, USA: HL7 International, 2007.
[6] D. Bender and K. Sartipi, "HL7 FHIR: An Agile and RESTful approach to healthcare information exchange," in Proc. 26th IEEE Int. Symp. Com-put.-Based Med. Syst. (CBMS), 2013, pp. 326–331, doi: 10.1109/CBMS.2013.6627810.
[7] HL7 International, "FHIR Release 4 (v4.0.1)," 2019. [Online]. Available: https://hl7.org/fhir/R4/
[8] G. Hohpe and B. Woolf, Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Boston, MA, USA: Addison-Wesley, 2003.
[9] A. P. Sheth and J. A. Larson, "Federated database systems for managing distributed, heterogeneous, and autonomous databases," ACM Comput. Surv., vol. 22, no. 3, pp. 183–236, 1990, doi: 10.1145/96602.96604.
[10] M. Lenzerini, "Data integration: A theoretical perspective," in Proc. 21st ACM SIGMOD-SIGACT-SIGART Symp. Princ. Database Syst. (PODS), 2002, pp. 233–246, doi: 10.1145/543613.543644.
[11] S. Das et al., "All aboard the Databus!: LinkedIn's scalable consistent change data capture platform," in Proc. 3rd ACM Symp. Cloud Comput. (SoCC), 2012, Art. no. 18, doi: 10.1145/2391229.2391247.
[12] J. Kreps, N. Narkhede, and J. Rao, "Kafka: A distributed messaging system for log processing," in Proc. 6th Int. Workshop Netw. Meets Data-bases (NetDB), Athens, Greece, 2011, pp. 1–7.
[13] D. Harel, "Statecharts: a visual formalism for complex systems," Sci. Comput. Program., vol. 8, no. 3, pp. 231–274, 1987, doi: 10.1016/0167-6423(87)90035-9.
[14] C. Richardson, Microservices Patterns. Shelter Island, NY, USA: Manning, 2018.
[15] M. Kleppmann, Designing Data-Intensive Applications. Sebastopol, CA, USA: O'Reilly Media, 2017.
[16] A. J. Holmgren, V. Patel, and J. Adler-Milstein, "Progress in interoperability: Measuring US hospitals' engagement in sharing patient data," Health Aff., vol. 36, no. 10, pp. 1820–1827, 2017, doi: 10.1377/hlthaff.2017.0546.
[17] B. W. Pickering et al., "The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: A pilot step-wedge cluster randomized trial," Int. J. Med. Inform., vol. 84, no. 5, pp. 299–307, 2015, doi: 10.1016/j.ijmedinf.2015.01.017.
[18] D. Dowding et al., "Dashboards for improving patient care: Review of the literature," Int. J. Med. Inform., vol. 84, no. 2, pp. 87–100, 2015, doi: 10.1016/j.ijmedinf.2014.10.001.
[19] W. Martinez, A. L. Threatt, S. T. Rosenbloom, K. A. Wallston, G. B. Hickson, and T. A. Elasy, "A patient-facing diabetes dashboard embedded in a patient web portal: Design sprint and usability testing," JMIR Hum. Factors, vol. 5, no. 3, Art. no. e26, 2018, doi: 10.2196/humanfactors.9569.
[20] B. Frandji, J. Schot, M. Joubert, I. Soadyh, and A. Kilsdonk, "The RICHE reference architecture," Med. Inform., vol. 19, no. 1, pp. 1–11, 1994, doi: 10.3109/14639239409044716.
[21] A. Winter, B. Brigl, G. Funkat, A. Häber, O. Heller, and T. Wendt, "3LGM2-Modeling to support management of health information systems," Int. J. Med. Inform., vol. 76, no. 2-3, pp. 145–150, 2007, doi: 10.1016/j.ijmedinf.2006.07.007.
[22] J. D. Duke et al., "Regenstrief Institute's Medical Gopher: A next-generation homegrown electronic medical record system," Int. J. Med. Inform., vol. 83, no. 3, pp. 170–179, 2014, doi: 10.1016/j.ijmedinf.2013.11.004.
[23] P. Rajkovic, D. Jankovic, and A. Milenkovic, "Developing and deploying medical information systems for Serbian public healthcare: Challenges, lessons learned and guidelines," Comput. Sci. Inf. Syst., vol. 10, no. 3, pp. 1429–1454, 2013, doi: 10.2298/CSIS120523056R.
[24] C. Tsavatewa, P. F. Musa, and I. Ramsingh, "Integration of Footprints information systems in palliative care: The case of Medical Center of Central Georgia," J. Med. Syst., vol. 36, no. 3, pp. 1511–1521, 2012, doi: 10.1007/s10916-010-9612-y.
[25] Ö. Köksal and B. Tekinerdogan, "Obstacles in data distribution service middleware: A systematic review," Future Gener. Comput. Syst., vol. 68, pp. 191–210, 2017, doi: 10.1016/j.future.2016.09.020.
[26] S. Sancar Gozukara, B. Tekinerdogan, and C. Catal, "Obstacles of on-premise enterprise resource planning systems and solution directions," J. Comput. Inf. Syst., vol. 62, no. 1, pp. 141–152, 2022, doi: 10.1080/08874417.2020.1739579.
[27] J. C. Mandel, D. A. Kreda, K. D. Mandl, I. S. Kohane, and R. B. Ramoni, "SMART on FHIR: a standards-based, interoperable apps platform for electronic health records," J. Am. Med. Inform. Assoc., vol. 23, no. 5, pp. 899–908, 2016, doi: 10.1093/jamia/ocv189.
[28] W. E. Hammond, "The making and adoption of health data standards," Health Aff., vol. 24, no. 5, pp. 1205–1213, 2005, doi: 10.1377/hlthaff.24.5.1205.
[29] A. Doan, A. Halevy, and Z. Ives, Principles of Data Integration. Waltham, MA, USA: Morgan Kaufmann, 2012.
[30] E. Rahm and P. A. Bernstein, "A survey of approaches to automatic schema matching," VLDB J., vol. 10, no. 4, pp. 334–350, 2001, doi: 10.1007/s007780100057.
[31] P. Vassiliadis, "A survey of extract–transform–load technology," Int. J. Data Warehous. Min., vol. 5, no. 3, pp. 1–27, 2009, doi: 10.4018/jdwm.2009070101.
[32] R. F. van der Lans, Data Virtualization for Business Intelligence Systems. Waltham, MA, USA: Morgan Kaufmann, 2012.
[33] E. Evans, Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston, MA, USA: Addison-Wesley, 2003.
[34] M. J. Sax, G. Wang, M. Weidlich, and J.-C. Freytag, "Streams and tables: Two sides of the same coin," in Proc. Int. Workshop Real-Time Bus. Intell. Anal. (BIRTE), Rio de Janeiro, Brazil, 2018, Art. no. 1, doi: 10.1145/3242153.3242155.
[35] G. Wang et al., "Consistency and completeness: Rethinking distributed stream processing in Apache Kafka," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2021, pp. 2602–2613, doi: 10.1145/3448016.3457556.
[36] P. Helland, "Idempotence is not a medical condition," Commun. ACM, vol. 55, no. 5, pp. 56–65, 2012, doi: 10.1145/2160718.2160734.
[37] T. Akidau et al., "The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing," Proc. VLDB Endow., vol. 8, no. 12, pp. 1792–1803, 2015, doi: 10.14778/2824032.2824076.
[38] G. Cugola and A. Margara, "Processing flows of information: From data stream to complex event processing," ACM Comput. Surv., vol. 44, no. 3, pp. 1–62, 2012, doi: 10.1145/2187671.2187677.
[39] M. Overeem, M. Spoor, S. Jansen, and S. Brinkkemper, "An empirical characterization of event sourced systems and their schema evolution — Lessons from industry," J. Syst. Softw., vol. 178, Art. no. 110970, 2021, doi: 10.1016/j.jss.2021.110970.
[40] R. Laigner, Y. Zhou, M. A. V. Salles, Y. Liu, and M. Kalinowski, "Data management in microservices: State of the practice, challenges, and research directions," Proc. VLDB Endow., vol. 14, no. 13, pp. 3348–3361, 2021, doi: 10.14778/3484224.3484232.
[41] W. M. P. van der Aalst, "The application of Petri nets to workflow management," J. Circuits Syst. Comput., vol. 8, no. 1, pp. 21–66, 1998, doi: 10.1142/S0218126698000043.
[42] W. M. P. van der Aalst, A. H. M. ter Hofstede, B. Kiepuszewski, and A. P. Barros, "Workflow patterns," Distrib. Parallel Databases, vol. 14, no. 1, pp. 5–51, 2003, doi: 10.1023/A:1022883727209.
[43] W. van der Aalst et al., "Process mining manifesto," in Business Process Management Workshops (BPM 2011), ser. Lecture Notes in Business Information Processing, vol. 99. Berlin, Germany: Springer, 2012, pp. 169–194, doi: 10.1007/978-3-642-28108-2_19.
[44] E. Rojas, J. Munoz-Gama, M. Sepúlveda, and D. Capurro, "Process mining in healthcare: A literature review," J. Biomed. Inform., vol. 61, pp. 224–236, 2016, doi: 10.1016/j.jbi.2016.04.007.
[45] M. Dumas, M. La Rosa, J. Mendling, and H. A. Reijers, Fundamentals of Business Process Management, 2nd ed. Berlin, Germany: Springer, 2018, doi: 10.1007/978-3-662-56509-4.
[46] E. Brewer, "CAP twelve years later: How the "rules" have changed," Computer, vol. 45, no. 2, pp. 23–29, 2012, doi: 10.1109/MC.2012.37.
[47] S. Gilbert and N. Lynch, "Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services," ACM SIGACT News, vol. 33, no. 2, pp. 51–59, 2002, doi: 10.1145/564585.564601.
[48] W. Vogels, "Eventually consistent," Commun. ACM, vol. 52, no. 1, pp. 40–44, 2009, doi: 10.1145/1435417.1435432.
[49] P. Helland, "Life beyond distributed transactions: An apostate's opinion," in Proc. 3rd Biennial Conf. Innov. Data Syst. Res. (CIDR), Asilomar, CA, USA, 2007, pp. 132–141.
[50] H. Garcia-Molina and K. Salem, "Sagas," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 1987, pp. 249–259, doi: 10.1145/38713.38742.
[51] L. Lamport, "Time, clocks, and the ordering of events in a distributed system," Commun. ACM, vol. 21, no. 7, pp. 558–565, 1978, doi: 10.1145/359545.359563.
[52] J. Dean and L. A. Barroso, "The tail at scale," Commun. ACM, vol. 56, no. 2, pp. 74–80, 2013, doi: 10.1145/2408776.2408794.
[53] N. G. Weiskopf and C. Weng, "Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical re-search," J. Am. Med. Inform. Assoc., vol. 20, no. 1, pp. 144–151, 2013, doi: 10.1136/amiajnl-2011-000681.
[54] M. G. Kahn et al., "A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data," eGEMs, vol. 4, no. 1, Art. no. 18, 2016, doi: 10.13063/2327-9214.1244.
[55] I. P. Fellegi and A. B. Sunter, "A theory for record linkage," J. Amer. Statist. Assoc., vol. 64, no. 328, pp. 1183–1210, 1969, doi: 10.1080/01621459.1969.10501049.
[56] R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman, "Role-based access control models," Computer, vol. 29, no. 2, pp. 38–47, 1996, doi: 10.1109/2.485845.
[57] D. F. Ferraiolo, R. Sandhu, S. Gavrila, D. R. Kuhn, and R. Chandramouli, "Proposed NIST standard for role-based access control," ACM Trans. Inf. Syst. Secur., vol. 4, no. 3, pp. 224–274, 2001, doi: 10.1145/501978.501980.
[58] J. H. Saltzer and M. D. Schroeder, "The protection of information in computer systems," Proc. IEEE, vol. 63, no. 9, pp. 1278–1308, 1975, doi: 10.1109/PROC.1975.9939.
[59] U.S. Department of Health and Human Services, "Security standards for the protection of electronic protected health information," 45 C.F.R. pt. 164, subpt. C.
[60] U.S. Department of Health and Human Services, "Privacy of individually identifiable health information," 45 C.F.R. pt. 164, subpt. E.
[61] Joint Task Force, Security and Privacy Controls for Information Systems and Organizations, NIST Special Publication 800-53 Rev. 5, Gaithersburg, MD, USA, 2020, doi: 10.6028/NIST.SP.800-53r5.
[62] S. Rose, O. Borchert, S. Mitchell, and S. Connelly, Zero Trust Architecture, NIST Special Publication 800-207, Gaithersburg, MD, USA, 2020, doi: 10.6028/NIST.SP.800-207.
[63] M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009, doi: 10.1016/j.ipm.2009.03.002.
[64] A. M. Law, Simulation Modeling and Analysis, 5th ed. New York, NY, USA: McGraw-Hill, 2015.
