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Volume 2 · Number 1 · 2010 29 Combining Ontologies and Cognitive Engineering to Innovate Electronic Health Records Eric Little, Ph.D Chief Knowledge Engineer Computer Task Group (CTG), Buffalo, NY Abstract Several challenges exist for implementing electronic health records (EHRs). Tantamount to these challenges are issues surrounding the most effective ways to model medical information and subsequently deliver that information to necessary stakeholders. Therefore, it is important to design and implement EHR systems that are robust in terms of their content and intuitive in terms of their use value. Ontologies can provide robust and intuitive EHR capabilities, since ontological approaches more closely mirror the ordinary ways in which people interact (and problem solve) with the world. In a like manner, cognitive systems engineering – particularly the area of cognitive work analysis (CWA) – offers empirically-based methodologies for better understanding the ways in which people interact with data. By combining ontologies and CWA methodologies in healthcare settings, it is possible to build systems that both model information in correct ways and present it to those that need to utilize it in their day-to-day work environments. Keywords: ontology, cognitive engineering, electronic health records, knowledge management, semantics, life sciences. 1. INTRODUCTION Ontologies are being applied to ever expanding areas of investigation to provide improved capabilities for knowledge management, data modeling, data querying, relationship discovery/definition, and rule manipulation [1-7]. Electronic health records (EHRs) are a growing technology, which are becoming more prevalent throughout healthcare systems, but where challenges exist in terms of their implementation and use [8-10]. The purpose of this paper is to provide an argument for how ontologies, combined with certain practices from cognitive systems engineering, can be utilized , in tandem, to provide for better structured, and easier to use, EHRs. Ontologies can be utilized to enhance EHRs in knowledge management applications by providing: better realist models of reality, explicitly defined semantics, more flexibility in the system’s design/use, and improved capabilities for merging heterogeneous data sources, ultimately resulting in improved knowledge sharing, retrieval and collaboration across medical areas [11-13]. Likewise, cognitive systems engineering practices, particularly those in the area of work domain analysis (WDA) can be utilized to empirically study the tasks and work environment of medical professionals and utilize that information to better design EHRs from the standpoint of system functionality, visualization and ease of use. In this sense, ontologies coupled with WDA can serve to augment heritage medical record systems, databases, and the like, providing an improved overarching structure which can allow better manipulation of medical information for a variety of user communities [14]. This approach understands ontologies as providing a metaphysical structure in the form of an overarching upper ontology that sits above numerous domain-level sub-ontologies, providing a unified classification system and semantic framework for all entities, attributes, processes and relations of interest [15-17]. Transactional data specific to a given enterprise remains housed within heritage data systems and is ingested by the ontology as needed, thereby becoming transformed into instances within the domain represented by the ontology. This allows for improved reasoning capabilities across various sorts of heterogeneous data, since numerous perspectives can be captured from subject matter experts (SMEs) within the enterprise and added to the ontology over time. Subsequently, through the utilization of WDA (and its capabilities to model tasks, user needs and the like) SME knowledge can be more effectively utilized without the need for retooling the heritage systems themselves.

Combining Ontologies and Cognitive Engineering to Innovate Electronic Health

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Several challenges exist for implementing electronic health records (EHRs). Tantamountto these challenges are issues surrounding the most effective ways to model medicalinformation and subsequently deliver that information to necessary stakeholders.Therefore, it is important to design and implement EHR systems that are robust in termsof their content and intuitive in terms of their use value. Ontologies can provide robustand intuitive EHR capabilities, since ontological approaches more closely mirror theordinary ways in which people interact (and problem solve) with the world. In a likemanner, cognitive systems engineering – particularly the area of cognitive work analysis(CWA) – offers empirically-based methodologies for better understanding the ways inwhich people interact with data. By combining ontologies and CWA methodologies inhealthcare settings, it is possible to build systems that both model information in correctways and present it to those that need to utilize it in their day-to-day work environments.

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Page 1: Combining Ontologies and Cognitive Engineering to Innovate Electronic Health

Volume 2 · Number 1 · 2010

29

Combining Ontologies and CognitiveEngineering to Innovate Electronic Health

RecordsEric Little, Ph.D

Chief Knowledge EngineerComputer Task Group (CTG), Buffalo, NY

Abstract Several challenges exist for implementing electronic health records (EHRs). Tantamountto these challenges are issues surrounding the most effective ways to model medicalinformation and subsequently deliver that information to necessary stakeholders.Therefore, it is important to design and implement EHR systems that are robust in termsof their content and intuitive in terms of their use value. Ontologies can provide robustand intuitive EHR capabilities, since ontological approaches more closely mirror theordinary ways in which people interact (and problem solve) with the world. In a likemanner, cognitive systems engineering – particularly the area of cognitive work analysis(CWA) – offers empirically-based methodologies for better understanding the ways inwhich people interact with data. By combining ontologies and CWA methodologies inhealthcare settings, it is possible to build systems that both model information in correctways and present it to those that need to utilize it in their day-to-day work environments.

Keywords: ontology, cognitive engineering, electronic health records, knowledgemanagement, semantics, life sciences.

1. INTRODUCTIONOntologies are being applied to ever expanding areas of investigation to provide improved capabilitiesfor knowledge management, data modeling, data querying, relationship discovery/definition, and rulemanipulation [1-7]. Electronic health records (EHRs) are a growing technology, which are becomingmore prevalent throughout healthcare systems, but where challenges exist in terms of theirimplementation and use [8-10]. The purpose of this paper is to provide an argument for how ontologies,combined with certain practices from cognitive systems engineering, can be utilized , in tandem, toprovide for better structured, and easier to use, EHRs. Ontologies can be utilized to enhance EHRs inknowledge management applications by providing: better realist models of reality, explicitly definedsemantics, more flexibility in the system’s design/use, and improved capabilities for mergingheterogeneous data sources, ultimately resulting in improved knowledge sharing, retrieval andcollaboration across medical areas [11-13]. Likewise, cognitive systems engineering practices,particularly those in the area of work domain analysis (WDA) can be utilized to empirically study thetasks and work environment of medical professionals and utilize that information to better design EHRsfrom the standpoint of system functionality, visualization and ease of use. In this sense, ontologiescoupled with WDA can serve to augment heritage medical record systems, databases, and the like,providing an improved overarching structure which can allow better manipulation of medicalinformation for a variety of user communities [14].

This approach understands ontologies as providing a metaphysical structure in the form of anoverarching upper ontology that sits above numerous domain-level sub-ontologies, providing a unifiedclassification system and semantic framework for all entities, attributes, processes and relations ofinterest [15-17]. Transactional data specific to a given enterprise remains housed within heritage datasystems and is ingested by the ontology as needed, thereby becoming transformed into instances withinthe domain represented by the ontology. This allows for improved reasoning capabilities across varioussorts of heterogeneous data, since numerous perspectives can be captured from subject matter experts(SMEs) within the enterprise and added to the ontology over time. Subsequently, through the utilizationof WDA (and its capabilities to model tasks, user needs and the like) SME knowledge can be moreeffectively utilized without the need for retooling the heritage systems themselves.

Page 2: Combining Ontologies and Cognitive Engineering to Innovate Electronic Health

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Page 3: Combining Ontologies and Cognitive Engineering to Innovate Electronic Health

[5] Rosse C, Mejino JLV. A reference ontology for bioinformatics: The Foundational Model ofAnatomy. Journal of Biomedical Informatics. 2003;36:478-500.

[6] Scheuermann RH, Ceusters W, Smith B. Toward an Ontological Treatment of Disease andDiagnosis. Proceedings of the 2009 AMIA Summit on Translational Bioinformatics, SanFrancisco, California, March 15-17, 2009: American Medical Informatics Association; 2009. p.116-20.

[7] Grenon, P., Smith, B. (2003) SNAP and SPAN: Towards Dynamic Spatial Ontology, SpatialCognition and Computation.

[8] Lawrence, K. Implementing EHR applications: a Canadian experience, Br J Healthcare ComputInfo Manage 2007; 24(4): 17–19. May 2007

[9] Zandieh, S. et al. Challenges to EHR Implementation in Electronic- Versus Paper-based OfficePractices, Journal of General Internal Medicine, Vol. 3, no. 6, June 2008, pp. 755-761.

[10] Goldaschmidt, P. HIT and MIS: implications of health information technology and medicalinformation systems, Communications of the ACM, 48 (10), Oct 2005, pp. 68-74.

[11] Smith B, Ceusters W. An Ontology-Based Methodology for the Migration of BiomedicalTerminologies to Electronic Health Records. AMIA 2005. Washington DC; 2005. p. 669-73.

[12] Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies.Biomedical and Health Informatics: Proceedings of the 2006 AMIA Annual Symposium.Washington DC: American Medical Informatics Association; 2006. p. 121-5.

[13] Smith B, Ashburner M, Ceusters W, et al. The OBO Foundry: coordinated evolution of ontologiesto support biomedical data integration. Nature Biotechnology. 2007;25:1251-5.

[14] Little, E. On an Ontological Foundation for Work Domain Analysis, CH 11 in Applications ofCognitive Work Analysis (Bisantz and Burns, eds.), CRC Press, 2008.

[15] Smith, B. "Basic Formal Ontology," 2002. http://ontology.buffalo.edu/bfo/.

[16] Grenon, P. "Spatiotemporality in Basic Formal Ontology: SNAP and SPAN, Upper-LevelOntology and Framework for Formalization, IFOMIS Technical Report Series, (http://ifomis.de),2003

[17] Bittner, T & Smith, B. Normalizing Medical Ontologies using Basic Formal Ontology inKooperative Versorgung, Vernetzte Forschung, Ubiquitäre Information (Proceedings of GMDSInnsbruck, 26-30 September 2004), Niebüll: Videel OHG, 199–201.

[18] Smith B, Ceusters W. HL7 RIM: An Incoherent Standard. In: Hasman A, Haux R, Lei Jvd, ClercqED, Roger-France F, editors. Studies in Health Technology and Informatics Ubiquity:Technologies for Better Health in Aging Societies - Proceedings of MIE2006. Amsterdam: IOSPress; 2006. p. 133-8.

[19] Rudnicki R, Ceusters W, Manzoor S, Smith B. What Particulars are Referred to in EHR Data? ACase Study in Integrating Referent Tracking into an Electronic Health Record Application. In:Teich JM, Suermondt J, C H, editors. American Medical Informatics Association 2007 AnnualSymposium Proceedings, Biomedical and Health Informatics: From Foundations to Applicationsto Policy. Chicago, IL; 2007. p. 630-4.

[20] Smith. B. Ontology and Information Systems http://ontology.buffalo.edu/ontology(PIC).pdf,2001.

[21] Little, E. A Proposed Methodology for Application-Based Formal Ontologies, Proceedings of theWorkshop on Reference Ontologies vs. Application Ontologies, 15-18 Sept. 2003, University ofHamburg, CEUR-WS.org.

[22] Little, E. & Vizenor, L. Principles for the Development of Upper Ontologies in Higher-levelInformation Fusion Applications, in Proceedings of the 2006 Formal Ontology for InformationSystems Conference, Baltimore, Maryland, November 9-11, 2006.

[23] Smith B. and Grenon, P. (2004) The Cornucopia of Formal-Ontological Relations, Dialectica58, No. 3, pp. 279-296.

[24] Vicente, K.J. (1999). Cognitive work analysis: Toward safe, productive, and healthycomputerbased work. Mahwah, NJ: Lawrence Erlbaum Associates.

[25] Rasmussen, J., Pejtersen, A.M., & Goodstein, L.P. (1994). Cognitive Systems Engineering. NewYork: Wiley-Interscience.

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[26] Bisantz, AM, & Vicente, KJ. Making the abstraction hierarchy concrete. International Journal ofHuman-computer Studies, 40, 83 – 117, 1994.

[27] Naikar, N, & Sanderson, P. Evaluating design proposals for complex systems with work domainanalysis. Human Factors, 43, 529 - 542. 2001

[28] Bisantz and Burns, (eds.), (2008). Applications of Cognitive Work Analysis, CRC Press.

[29] Ceusters W, Smith B. Referent Tracking and its Applications CEUR Workshop Proceedings.Banff, Canada: CEUR; 2007.

[30] Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. Journal ofBiomedical Informatics. 2006 June;39(3):362-78.

[31] Top Quadrant: http://www.topquadrant.com/

[32] Ontoprise: http://www.ontoprise.de/en/home/

[33] Protege : http://protege.stanford.edu/

[34] BFO Version 1.1, http://www.ifomis.org/bfo/1.1

[35] Smith, B. (2006) Against Idiosyncrasy in Ontology Development, B. Bennett and C. Fellbaum(Eds.), Formal Ontology and Information Systems, (FOIS 2006), Baltimore November 9-11.

[36] Noy, N. F. & McGuinness. (2001) Ontology Development 101: A Guide to Creating Your FirstOntology, Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and StanfordMedical Informatics Technical Report SMI-2001-0880.

[37] Thomas Bodenheimer, MD; Kate Lorig, RN, DrPH; Halsted Holman, MD; Kevin Grumbach,MD. Patient Self-management of Chronic Disease in Primary Care, JAMA. 2002;288:2469-2475.

[38] June Forkner-Dunn, PhD, RN. Internet-based Patient Self-care: The Next Generation of HealthCare Delivery, J Med Internet Res. 2003 Apr–Jun; 5(2): e8.

[39] OBO Foundry - http://www.obofoundry.org

[40] NCBO BioPortal - http://bioportal.bioontology.org/

38 Combining Ontologies and Cognitive Engineering to Innovate Electronic Health Records

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