Domain Modeling and Application Development of an Archetype- and XML-based EHRS

Summary

Background: Access to patient data within the hospital or between hospitals is still problematic since a variety of information systems is in use applying different vendor specific terminologies and underlying knowledge models. Beyond, the development of electronic health record systems (EHRSs) is time and resource consuming. Thus, there is a substantial need for a development strategy of standardized EHRSs. We are applying a reuse-oriented process model and demonstrate its feasibility and realization on a practical medical use case, which is an EHRS holding all relevant data arising in the context of treatment of tumors of the sella region. In this paper, we describe the development process and our practical experiences.

Methods: Requirements towards the development of the EHRS were collected by interviews with a neurosurgeon and patient data analysis. For modelling of patient data, we selected openEHR as standard and exploited the software tools provided by the openEHR foundation. The patient information model forms the core of the development process, which comprises the EHR generation and the implementation of an EHRS architecture. Moreover, a reuse-oriented process model from the business domain was adapted to the development of the EHRS.

Results: The reuse-oriented process model is a model for a suitable abstraction of both, modeling and development of an EHR centralized EHRS. The information modeling process resulted in 18 archetypes that were aggregated in a template and built the boilerplate of the model driven development. The EHRs and the EHRS were developed by openEHR and W3C standards, tightly supported by well-established XML techniques. The GUI of the final EHRS integrates and visualizes information from various examinations, medical reports, findings and laboratory test results.

Conclusion: We conclude that the development of a standardized overarching EHR and an EHRS is feasible using openEHR and W3C standards, enabling a high degree of semantic interoperability. The standardized representation visualizes data and can in this way support the decision process of clinicians.

Citation: Kropf S, Chalopin C, Lindner D, Denecke K. Domain modeling and application development of an archetype- and XML-based EHRS: Practical experiences and lessons learnt. Appl Clin Inform 2017; 8: 660–679 https://doi.org/10.4338/ACI-2017-01-RA-0009

Keywords

Electronic health record - EHR standards - medical informatics applications - health information exchange - openEHR - XML

^* Contributed equally

Human Subject Research Approval

The development was a proof of concept project. During development, the system was tested with randomized patient data. After finishing, we tested the system with anonymized real patient data. There was no person-identifying data included on any EHR at any time.

• References

• 1 Bisbal J, Berry D. An analysis framework for electronic health record systems. Methods Inf Med 2011; 50 (02) 180-189.
• 2 Boochever SS. HIS/RIS/PACS integration: getting to the gold standard. Radiol Manage 2004; May-Jun; 26 (03) 16-24 quiz 25–7.
• 3 Cohan S, Gilboa F, Shani U. PACS and electronic health records. Medical Imaging. 2002: 288-298.
• 4 Weitschek E, Felici G, Bertolazzi P. Clinical data mining: problems, pitfalls and solutions. Database and Expert Systems Applications (DEXA), IEEE Computer Society. 2013: 90-94.
• 5 Eichelberg M, Aden T, Riesmeier J, Dogac A, Laleci G. Electronic health record standards-a brief overview. Proceedings of the 4th IEEE International Conference on Information and Communications Technology. 2006
• 6 Begoyan A. An overview of interoperability standards for electronic health records. Proceedings of the 10^th International Conference on Integrated Design and Process Technology, IDPT 2007. 2007: 3-8.
• 7 Health informatics –Health informatics profiling framework. 2004 ISO/TR 17119:2004(E).
• 8 Operational Data Model (ODM)-XML [cited 2017 Feb 20]. Available from: http://www.cdisc.org/standards/foundational/odm
• 9 Health informatics –Electronic health record communication. 2012 EN ISO 13606 standard family.
• 10 Schloeffel P, Beale T, Hayworth G, Heard S, Leslie H. The relationship between CEN 13606, HL7, and openEHR. HIC 2006 and HINZ. 2006 Proceedings, 2006.
• 11 Health informatics –General purpose information components. 2006 DIN EN 14822 standard family.
• 12 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo Shvo A. HL7 Clinical Document Architecture, Release 2. J Am Med Inform Assoc 2006; 13 (01) 30-39.
• 13 Comparison-v3 –FHIR v1.0.2 [cited 2017 Feb 22]. Available from: http://www.hl7.org/fhir/comparison-v3.html
• 14 Introducing HL7 FHIR [cited 2016 Dec 22]. Available from:. http://www.hl7.org/implement/standardsfhir/summary.html
• 15 openEHR Clinical Knowlede Manager [cited 2017 Feb 21]. Available from: http://www.openehr.org/ckm
• 16 openEHR Modelling Tools [cited 2017 Feb 21]. Available from: http://www.openehr.org/downloads/modellingtools
• 17 GasroOS [cited 2017 Feb 20]. Available from: http://sourceforge.net/projects/gastros
• 18 EHRflex: An archetype-driven EHR system [cited 2017 Feb 21]. Available from: http://ehrflex.source-forge.net
• 19 LiU-IMT/EEE: A REST based Educational EHR Environment based on openEHR [cited 2017 Feb 21]. Available from: http://github.com/LiU-IMT/EEE
• 20 MedRecord: The fastes way to medical grade apps [cited 2017 Feb 21] Available from: http://medrecord.nl
• 21 Sundvall E, Nyström M, Karlsson D, Eneling M, Chen R, Örman H. Applying representational state transfer (REST) architecture to archetype-based electronic health record systems. BMC Med Inform Decis Mak. 2013 doi:10.1186/1472–6947–13–57
• 22 Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG. Archetype-based data warehouse environment to enable the reuse of electronic health record data. Int J Med Inform 2015; 84 (09) 702-714.
• 23 Dziuballe P, Forster C, Breil B, Thiemann V, Fritz F, Lechtenbörger J, Vossen G, Duglas M. The single source architecture x4T to connect medical documentation and clinical research. Stud Health Technol Inform 2011; 169: 902-906.
• 24 Bruland P, Forster C, Breil B, Ständer S, Dugas M, Fritz F. Does single-source create an added value? Evaluating the impact of introducing x4T into the clinical routine on workflow modifications, data quality and cost-benefit. Int J Med Inform 2014; 83 (12) 915-928.
• 25 Schloeffel P. openEHR archetypes: Putting the clinician back in the driver‘s seat. HIC 2003 RACGP12CC. 2003.
• 26 Caldiera G, and Basili VR. Identifying and qualifying reusable software components. Computer 1991; 24 (02) 61-70.
• 27 Kropf S, Chalopin C, Denecke K. Template and model driven development of standardized electronic health records. Stud Health Technol Inform 2015; 216: 30-34.
• 28 Meier W. eXist: An open source native XML database. Web, Web-Services, and Database Systems 2003: 169-183.
• 29 XQuery 3.0: An XML Query Language [cited 2016 Jun 29]. Available from: http://www.w3.org/TRxquery-30/
• 30 RFC 3986: Uniform Resource Identifier (URI): Generic Syntax [cited 2016 Jun 27]. Available from: http://tools.ietf.org/html/rfc3986
• 31 openEHR Data Types Information Model [cited 2016 Jun 29]. Available from: http://www.openehr.org/releases/RM/latest/docs/data_types/data_types.html#_dv_multimedia_class
• 32 The official homepage of Orthanc, the DICOM server for medical imaging from Belgium. www.orthancserver.com [cited 2016 Jun 29]
• 33 hGraph: Your health in one picture [cited 2016 Jun 29]. Available from: http://www.hgraph.org
• 34 Charts –Google Developers [cited 2016 Jun 29]. Available from: http://developers.google.com/chart
• 35 HTML Canvas 2D Context [cited 2016 Jun 29]. Available from: http://www.w3.org/TR/2dcontext
• 36 Scalable Vector Graphics (SVG) 1.1 (Second Edition) [cited 2016 Jun 29]. Available from: http://www.w3.org/TR/SVG
• 37 openEHR specifications [cited 2016 Jun 29]. Available from: http://github.com/openEHR/specifications
• 38 openEHR Persistence FAQs [cited 2016 Jun 07]. Available from: http://openehr.atlassian.net/wiki/displayresources/Persistence+FAQs
• 39 Atalag K, Kingsford D, Paton C, Warren J. Putting Health Record Interoperability Standards to Work. Electron J Heal Informatics 2010; 5 (01) 1-17.
• 40 PopularitY of Programming Language [cited 2017 Feb 20]. Available from: http://pypl.github.ioPYPL.html
• 41 Kropf S, Krücken P, Mueller W, Denecke K. Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying. Methods Inf Med. 2017 Feb 28. DOI:10.3414/ME16–01–0073.
• 42 Kropf S, Uciteli A, Krücken P, Denecke K, Herre H. Querying standardized EHRs by a Search Ontology XML extension (SOX). Ontologies and Data in Live Sciences. Proceedings of the 7^th Workshop of the GI Workgroup OBML. 2016
• 43 Health and Lifesciences Extension): terms relating to healthcare, medicine and the life sciences. [cited 2016 Nov 03]. Available from: http://health-lifesci.schema.org
• 44 Botsis Hartvigsen G, Chen F, Weng C. Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. AMIA Jt Summits Transl Sci Proc 2010; 2010: 1-5.
• 45 Hovenga E, Grain H. An Information Paradigm Shift is Required to realise EHR Benefits. Stud Health Technol Inform 2015; 216: 26-29.
• 46 Jensen PB, 1 Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012; 13 (06) 395-405.
• 47 Lee EK, Atallah HY, Wright MD, Post ET, Thomas IVC, Wu DT, Haley Jr LL. Transforming hospital emergency department workflow and patient care. Journal Interfaces 2015; 45 (01) 58-82.
• 48 Warner JL, Jain SK, Levy MA. Integrating cancer genomic data into electronic health records. Genome Med 2016; 8 (01) 113.
• 49 Simpao AF, Ahumada LM, Gálvez JA, Rehman MA. A review of analytics and clinical informatics in health care. J Med Syst 2014; 38 (04) 45.