A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Autor:	Kruse J; LADR Laboratory Group Dr Kramer & Colleagues, Geesthacht, Germany., Wiedekopf J; IT Center for Clinical Research, University of Luebeck, Luebeck, Germany.; Institute of Medical Informatics, University of Luebeck, Luebeck, Germany., Kock-Schoppenhauer AK; IT Center for Clinical Research, University of Luebeck, Luebeck, Germany., Essenwanger A; mio42 LLC, Berlin, Germany., Ingenerf J; IT Center for Clinical Research, University of Luebeck, Luebeck, Germany.; Institute of Medical Informatics, University of Luebeck, Luebeck, Germany., Ulrich H; IT Center for Clinical Research, University of Luebeck, Luebeck, Germany.; Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kaistraße 101, Kiel, 24114, Germany, 49 431-500-31601.
Jazyk:	angličtina
Zdroj:	JMIR medical informatics [JMIR Med Inform] 2024 Oct 18; Vol. 12, pp. e57569. Date of Electronic Publication: 2024 Oct 18.
DOI:	10.2196/57569
Abstrakt:	Background: Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). Objective: In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. Methods: FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. Results: The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. Conclusions: The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology. (© Jesse Kruse, Joshua Wiedekopf, Ann-Kristin Kock-Schoppenhauer, Andrea Essenwanger, Josef Ingenerf, Hannes Ulrich. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)
Databáze:	MEDLINE
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