Solving the interoperability challenge of a distributed complex patient guidance system: A data integrator based on HL7's Virtual Medical Record standard

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Abstract

Objective We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support systems. Materials and Methods To achieve seamless communication between the personal health record and heterogeneous data consumers, we used a three-pronged approach. First, the choice of the HL7 vMR as a message model for all components accompanied by the use of medical vocabularies eases their semantic interoperability. Second, the DI follows a service-oriented approach to provide access to system components. Third, an XML database provides the data layer. Results The DI supports requirements of a guideline-based clinical decision support system implemented in two clinical domains and settings, ensuring reliable and secure access, high performance, and simplicity of integration, while complying with standards for the storage and processing of patient information needed for decision support and analytics. This was tested within the framework of a multinational project (www.mobiguide-project.eu) aimed at developing a ubiquitous patient guidance system (PGS). Discussion The vMR model with its extension mechanism is demonstrated to be effective for data integration and communication within a distributed PGS implemented for two clinical domains across different healthcare settings in two nations.

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APA

Marcos, C., González-Ferrer, A., Peleg, M., & Cavero, C. (2015). Solving the interoperability challenge of a distributed complex patient guidance system: A data integrator based on HL7’s Virtual Medical Record standard. Journal of the American Medical Informatics Association, 22(3), 587–599. https://doi.org/10.1093/jamia/ocv003

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