gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

Mapping HL7v2 ORU and BAR Messages to openEHR Archetypes

Meeting Abstract

  • Michael Anywar - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Santiago Pazmino - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Ka Yung Cheng - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Neele Petersen - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Hao Qian - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Felix Rottmann - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Thomas Richter - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Tobias Bronsch - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Björn Bergh - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany
  • Björn Schreiweis - Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany, Kiel, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 100

doi: 10.3205/21gmds025, urn:nbn:de:0183-21gmds0256

Published: September 24, 2021

© 2021 Anywar et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction: Semantic interoperability of clinical data is an imperative element for the reuse and sharing of clinical data across institutions, especially for the purposes of precision medicine and research. This makes semantic interoperability one of the core goals of the Medical Informatics Initiative [1] with HiGHmed [2] as one of its four consortia.

Often, Hospital Information Systems communicate internally using HL7v2. Thus, we based our data integration for the Medical Data Integration Center (MeDIC) on HL7v2 while utilizing existing open-source technologies.

To achieve the objectives of the HiGHmed consortium; we mapped HL7v2 messages (BAR and ORU) to our openEHR-based HiGHmed platform and integrate medical data into the openEHR [3] Clinical Data Repository (CDR).

Methods: To load compositions into the openEHR repository, we chose Talend [4] and Java for mapping messages to openEHR templates, while Apache Kafka [5] and Apache Nifi [6] were responsible for transmission and translation respectively. We used the HAPI Library HL7 parser [7], for the translation of HL7v2 to XML, running on Apache Nifi, a framework for automated data flow between systems.

Better Care openEHR platform [8] alongside an ICW XDS registry [9] was deployed and accessed through SOAP API, by which Talend streamed mapped data as Template Data Document (TDD) [10] to the openEHR CDR.

The following procedures had to be performed in order to have compositions in the openEHR repository:

1.
We transformed the HL7 messages to XML files (UTF-8 encoded). The files were streamed to Talend using Kafka. Talend jobs were created based on the message and template requirements. Templates were pre-designed by the consortium and made available on the HiGHmed openEHR CKM portal [11].
2.
Once mapping to templates was complete, relevant IHE ITI-41 [12] metadata was generated and appended onto the resulting TDD which is a format to upload clinical data into an openEHR CDR. The TDD is first streamed to the Better Care XDS repository where it is stored as a document and then its content streamed to the openEHR CDR for composition [13] creation.

Results: Patient data were successfully loaded in the openEHR platform with 20 compositions generated from 5 different test patients. Test data were mainly observations, diagnoses and procedures. However, a varying number of compositions were created in the openEHR platform depending on the report type. There were variations in composition numbers for diagnoses and procedures because each single HL7 BAR message had a different number of encounters per patient, while observation report data was exactly same as the number of ORU messages received from the Hospital Information System.

Discussion and Conclusion: While Martinez-Costa et al. [14] and André Da Costa et al. [15] approached interoperability to openEHR, differently, both but presented limitations that result to loss of meaning and information especially where concept and data types cannot be mapped to target representation.

Despite being a promising approach towards interoperable healthcare data sharing, changes in either input or output format result in complete re-mapping. Thus, we are currently evaluating implementation of the mapping process in Java rather than Talend.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


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