Background: The interoperability of Clinical Decision Support (CDS) systems is an important obstacle for their adoption. The lack of appropriate mechanisms to specify the semantics of their interfaces is a common barrier in their implementation. Objective: In this review we aim to provide a clear insight of current approaches for the integration and semantic interoperability of CDS systems Methods: published conference papers, book chapters and journal papers from Pubmed, IEEE Xplore and Science Direct databases were searched since 2007 until January 2016. Inclusion criteria was based on the approaches to enhance semantic interoperability of CDS systems. Results: We selected 41 papers to include in the review. Five main complementary mechanisms to enable CDS systems interoperability were found. 22% of the studies covered the application of medical logic and guidelines representation formalisms; 63% presented the use of clinical information standards; 32% made use of semantic web technologies such as ontologies; 46% covered the use of standard terminologies; and 32% proposed the use of web services for CDS encapsulation or new techniques for the discovery of systems. Conclusion: information model standards, terminologies, ontologies, medical logic specification formalisms and web services are the main areas of work for semantic interoperability in CDS. Main barriers in the interoperability of CDS systems are related to the effort of standardization, the variety of terminologies available, vagueness of concepts in clinical guidelines, terminological expressions computation and definitions of reusable models.
clinical decision support systems; semantic interoperability; terminologies; clinical models; ontologies
[1] Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005;330:765. doi: 10.1136/bmj.38398.500764.8F.
[2] Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10:523–30. doi: 10.1197/jamia.M1370.
[3] Ahmadian L, van Engen-Verheul M, Bakhshi-Raiez F, Peek N, Cornet R, de Keizer NF. The role of standard-ized data and terminological systems in computerized clinical decision support systems: Literature review and survey. International Journal of Medical Informatics 2011;80:81–93. doi: 10.1016/j.ijmedinf.2010.11.006.
[4] Peleg M, Keren S, Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J Biomed Inform 2008;41:180–201. doi: 10.1016/j.jbi.2007.05.003.
[5] HL7 Standards Product Brief - HL7 Version 3 Standard: Clinical Decision Support; Virtual Medical Record (vMR) Logical Model, Release 2 n.d. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=338 (accessed December 30, 2015).
[6] Huff SM, Oniki TA, Coyle JF, Parker CG, Rocha RA. Chapter 17 - Ontologies, Vocabularies and Data Models. In: Greenes RA, editor. Clinical Decision Support (Second Edition), Oxford: Academic Press; 2014, p. 465–98.
[7] Marco-Ruiz L, Bellika JG. Semantic Interoperability in Clinical Decision Support Systems: A Systematic Review. Stud Health Technol Inform 2015;216:958.
[8] Dixon BE, Simonaitis L, Goldberg HS, Paterno MD, Schaeffer M, Hongsermeier T, et al. A pilot study of distributed knowledge management and clinical decision support in the cloud. Artif Intell Med 2013;59. doi: 10.1016/j.artmed.2013.03.004.
[9] Fu Jr. PC, Rosenthal D, Pevnick JM, Eisenberg F. The impact of emerging standards adoption on automated quality reporting. Journal of Biomedical Informatics 2012;45:772–81. doi: 10.1016/j.jbi.2012.06.002.
[10] Goldberg HS, Paterno MD, Rocha BH, Schaeffer M, Wright A, Erickson JL, et al. A highly scalable, interoperable clinical decision support service. J Am Med In-form Assoc 2014;21. doi: 10.1136/amiajnl-2013-001990.
[11] González-Ferrer A, Peleg M. Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study. Computer Standards & Interfaces 2015;42:125–36. doi: 10.1016/j.csi.2015.06.002.
[12] Hosseini M, Ahmadi M, Dixon BE. A Service Oriented Architecture Approach to Achieve Interoperability between Immunization Information Systems in Iran. AMIA Annu Symp Proc 2014;2014:1797–805.
[13] Hrabak KM, Campbell JR, Tu SW, McClure R, Weida RT. Creating interoperable guidelines: requirements of vocabulary standards in immunization decision support. Stud Health Technol Inform 2007;129:930–4.
[14] Iqbal AM, Shepherd M, Abidi SSR. An Ontology-Based Electronic Medical Record for Chronic Disease Management. System Sciences (HICSS), 2011 44th Hawaii International Conference on, 2011, p. 1–10. doi: 10.1109/HICSS.2011.61.
[15] Koutkias VG, McNair P, Kilintzis V, Skovhus Andersen K, Nies J, Sarfati J-C, et al. From Adverse Drug Event Detection to Prevention. A Novel Clinical Decision Support Framework for Medication Safety. Methods Inf Med 2014;53. doi: 10.3414/ME14-01-0027.
[16] Lezcano L, Sicilia M-A, Rodríguez-Solano C. Integrat-ing reasoning and clinical archetypes using OWL ontologies and SWRL rules. J Biomed Inform 2011;44:343–53. doi: 10.1016/j.jbi.2010.11.005.
[17] Marco-Ruiz L, Maldonado JA, Karlsen R, Bellika JG. Multidisciplinary Modelling of Symptoms and Signs with Archetypes and SNOMED-CT for Clinical Decision Support. Studies in Health Technology and Infor-matics 2014;210:125–9.
[18] Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG. Archetype-based data warehouse environ-ment to enable the reuse of electronic health record data. International Journal of Medical Informatics 2015;84:702–14. doi: 10.1016/j.ijmedinf.2015.05.016.
[19] Marcos C, González-Ferrer A, Peleg M, Cavero C. Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s Virtual Medical Record standard. Jour-nal of the American Medical Informatics Association 2015;22:587–99. doi: 10.1093/jamia/ocv003.
[20] Marcos M, Maldonado JA, Martínez-Salvador B, Boscá D, Robles M. Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility. J Bio-med Inform 2013;46:676–89. doi: 10.1016/j.jbi.2013.05.004.
[21] Nee O, Hein A, Gorath T, Hulsmann N, Laleci GB, Yuksel M, et al. SAPHIRE: intelligent healthcare monitoring based on semantic interoperability platform: pilot applications. Communications, IET 2008;2:192–201. doi: 10.1049/iet-com:20060699.
[22] Sáez C, Bresó A, Vicente J, Robles M, García-Gómez JM. An HL7-CDA wrapper for facilitating semantic in-teroperability to rule-based Clinical Decision Support Systems. Comput Methods Programs Biomed 2013;109:239–49. doi: 10.1016/j.cmpb.2012.10.003.
[23] Sordo M, Boxwala AA. Chapter 18 - Grouped Knowledge Elements. In: Greenes RA, editor. Clinical Decision Support (Second Edition), Oxford: Academic Press; 2014, p. 499–514.
[24] Sartipi K, Yarmand MH. Standard-based data and ser-vice interoperability in eHealth systems. Software Maintenance, 2008. ICSM 2008. IEEE International Conference on, 2008, p. 187–96. doi: 10.1109/ICSM.2008.4658067.
[25] Tu SW, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay J, et al. The SAGE Guideline Model: Achievements and Overview. Journal of the American Medical Informatics Association 2007;14:589–98. doi: 10.1197/jamia.M2399.
[26] Wright A, Sittig DF, Ash JS, Erickson JL, Hickman TT, Paterno M, et al. Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study. International Journal of Medical Informatics 2015;84:901–11. doi: 10.1016/j.ijmedinf.2015.08.008.
[27] Zhang Y-F, Tian Y, Zhou T-S, Araki K, Li J-S. Integrat-ing HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems. Computer Methods and Programs in Biomedicine 2016;123:94–108. doi: 10.1016/j.cmpb.2015.09.020.
[28] Bouhaddou O, Cromwell T, Davis M, Maulden S, Hsing N, Carlson D, et al. Translating standards into practice: Experience and lessons learned at the Department of Veterans Affairs. Journal of Biomedical Informatics 2012;45:813–23. doi: 10.1016/j.jbi.2012.01.003.
[29] Fernández-Breis JT, Maldonado JA, Marcos M, Legaz-García MDC, Moner D, Torres-Sospedra J, et al. Lever-aging electronic healthcare record standards and seman-tic web technologies for the identification of patient cohorts. J Am Med Inform Assoc 2013. doi: 10.1136/amiajnl-2013-001923.
[30] Kawamoto K, Del Fiol G, Strasberg HR, Hulse N, Cur-tis C, Cimino JJ, et al. Multi-National, Multi-Institutional Analysis of Clinical Decision Support Data Needs to Inform Development of the HL7 Virtual Medical Record Standard. AMIA Annu Symp Proc 2010;2010:377–81.
[31] Komatsoulis GA, Warzel DB, Hartel FW, Shanbhag K, Chilukuri R, Fragoso G, et al. caCORE version 3: Im-plementation of a model driven, service-oriented archi-tecture for semantic interoperability. Journal of Biomedical Informatics 2008;41:106–23. doi: 10.1016/j.jbi.2007.03.009.
[32] Peleg M, González-Ferrer A. Chapter 16 - Guidelines and Workflow Models. In: Greenes RA, editor. Clinical Decision Support (Second Edition), Oxford: Academic Press; 2014, p. 435–64.
[33] Gordon CL, Weng C. Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement. Journal of Biomedical Informatics 2015;57:42–52. doi: 10.1016/j.jbi.2015.07.014.
[34] Brochhausen M, Spear AD, Cocos C, Weiler G, Martín L, Anguita A, et al. The ACGT Master Ontology and its applications – Towards an ontology-driven cancer research and management system. Journal of Biomedical Informatics 2011;44:8–25. doi: 10.1016/j.jbi.2010.04.008.
[35] Ahmadian L, Cornet R, de Keizer NF. Facilitating pre-operative assessment guidelines representation using SNOMED CT. Journal of Biomedical Informatics 2010;43:883–90. doi: 10.1016/j.jbi.2010.07.009.
[36] Wilk S, Michalowski W, O’Sullivan D, Farion K, Sayyad-Shirabad J, Kuziemsky C, et al. A task-based sup-port architecture for developing point-of-care clinical decision support systems for the emergency department. Methods Inf Med 2013;52. doi: 10.3414/ME11-01-0099.
[37] Madsen M. Health care ontologies: knowledge models for record sharing and decision support. Stud Health Technol Inform 2010;151:104–14.
[38] Ye Y, Jiang Z, Diao X, Yang D, Du G. An ontology-based hierarchical semantic modeling approach to clinical pathway workflows. Computers in Biology and Medicine 2009;39:722–32. doi: 10.1016/j.compbiomed.2009.05.005.
[39] Samwald M, Fehre K, de Bruin J, Adlassnig K-P. The Arden Syntax standard for clinical decision support: Experiences and directions. Journal of Biomedical Informatics 2012;45:711–8. doi: 10.1016/j.jbi.2012.02.001.
[40] Sordo M, Palchuk MB. 15 - Grouped knowledge ele-ments A2 - Greenes, Robert A. Clinical Decision Support, Burlington: Academic Press; 2007, p. 325–43.
[41] Tu SW, Campbell J, Musen MA. The SAGE guideline modeling: motivation and methodology. Stud Health Technol Inform 2004;101:167–71.
[42] Abugessaisa I, Saevarsdottir S, Tsipras G, Lindblad S, Sandin C, Nikamo P, et al. Accelerating translational research by clinically driven development of an informatics platform - a case study. PLoS One 2014;9. doi: 10.1371/journal.pone.0104382.
[43] Kawamoto K, Lobach DF. Proposal for Fulfilling Stra-tegic Objectives of the U.S. Roadmap for National Action on Decision Support through a Service-oriented Architecture Leveraging HL7 Services. Journal of the American Medical Informatics Association 2007;14:146–55. doi: 10.1197/jamia.M2298.
[44] Kawamoto K. 23 - Integration of knowledge resources into applications to enable clinical decision support: Architectural considerations A2 - Greenes, Robert A. Clinical Decision Support, Burlington: Academic Press; 2007, p. 503–38.
[45] Zhang M, Velasco FT, Musser RC, Kawamoto K. Ena-bling cross-platform clinical decision support through Web-based decision support in commercial electronic health record systems: proposal and evaluation of initial prototype implementations. AMIA Annu Symp Proc 2013;2013:1558–67.
[46] Bouhaddou O, Warnekar P, Parrish F, Do N, Mandel J, Kilbourne J, et al. Exchange of Computable Patient Data between the Department of Veterans Affairs (VA) and the Department of Defense (DoD): Terminology Mediation Strategy. Journal of the American Medical Informatics Association 2008;15:174–83. doi: 10.1197/jamia.M2498.
[47] Maldonado JA, Moner D, Boscá D, Fernández-Breis JT, Angulo C, Robles M. LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics. Int J Med Inform 2009;78:559–70. doi: 10.1016/j.ijmedinf.2009.03.006.
[48] HL7 Standards Product Brief - GELLO (HL7 Version 3 Standard: Gello: A Common Expression Language, Re-lease 2) n.d. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=5 (accessed December 20, 2014).
[49] HL7 Standards Product Brief - HL7 Version 3 Standard: Clinical Decision Support Knowledge Artifact Specification, Release 1.2 n.d. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=337 (accessed December 17, 2014).
[50] HL7 Standards Product Brief - HL7 Implementation Guide: Decision Support Service, Release 1 n.d. http://www.hl7.org/implement/standards/product_brief.cfm?product_id=334 (accessed October 8, 2015).