Luis Marco-Ruiz1, David Moner2, José A Maldonado3, Nils Kolstrup4, Johan G Bellika5. 1. Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Norway; Department of Clinical Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, Norway. Electronic address: Luis.Marco.Ruiz@telemed.no. 2. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain. 3. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain; VeraTech for Health SL, Valencia, Spain. 4. Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Norway; General Practice Research Unit, UIT The Arctic University of Norway, Norway. 5. Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Norway; Department of Clinical Medicine, Faculty of Health Sciences, UIT The Arctic University of Norway, Norway.
Abstract
BACKGROUND: The reuse of data captured during health care delivery is essential to satisfy the demands of clinical research and clinical decision support systems. A main barrier for the reuse is the existence of legacy formats of data and the high granularity of it when stored in an electronic health record (EHR) system. Thus, we need mechanisms to standardize, aggregate, and query data concealed in the EHRs, to allow their reuse whenever they are needed. OBJECTIVE: To create a data warehouse infrastructure using archetype-based technologies, standards and query languages to enable the interoperability needed for data reuse. MATERIALS AND METHODS: The work presented makes use of best of breed archetype-based data transformation and storage technologies to create a workflow for the modeling, extraction, transformation and load of EHR proprietary data into standardized data repositories. We converted legacy data and performed patient-centered aggregations via archetype-based transformations. Later, specific purpose aggregations were performed at a query level for particular use cases. RESULTS: Laboratory test results of a population of 230,000 patients belonging to Troms and Finnmark counties in Norway requested between January 2013 and November 2014 have been standardized. Test records normalization has been performed by defining transformation and aggregation functions between the laboratory records and an archetype. These mappings were used to automatically generate open EHR compliant data. These data were loaded into an archetype-based data warehouse. Once loaded, we defined indicators linked to the data in the warehouse to monitor test activity of Salmonella and Pertussis using the archetype query language. DISCUSSION: Archetype-based standards and technologies can be used to create a data warehouse environment that enables data from EHR systems to be reused in clinical research and decision support systems. With this approach, existing EHR data becomes available in a standardized and interoperable format, thus opening a world of possibilities toward semantic or concept-based reuse, query and communication of clinical data.
BACKGROUND: The reuse of data captured during health care delivery is essential to satisfy the demands of clinical research and clinical decision support systems. A main barrier for the reuse is the existence of legacy formats of data and the high granularity of it when stored in an electronic health record (EHR) system. Thus, we need mechanisms to standardize, aggregate, and query data concealed in the EHRs, to allow their reuse whenever they are needed. OBJECTIVE: To create a data warehouse infrastructure using archetype-based technologies, standards and query languages to enable the interoperability needed for data reuse. MATERIALS AND METHODS: The work presented makes use of best of breed archetype-based data transformation and storage technologies to create a workflow for the modeling, extraction, transformation and load of EHR proprietary data into standardized data repositories. We converted legacy data and performed patient-centered aggregations via archetype-based transformations. Later, specific purpose aggregations were performed at a query level for particular use cases. RESULTS: Laboratory test results of a population of 230,000 patients belonging to Troms and Finnmark counties in Norway requested between January 2013 and November 2014 have been standardized. Test records normalization has been performed by defining transformation and aggregation functions between the laboratory records and an archetype. These mappings were used to automatically generate open EHR compliant data. These data were loaded into an archetype-based data warehouse. Once loaded, we defined indicators linked to the data in the warehouse to monitor test activity of Salmonella and Pertussis using the archetype query language. DISCUSSION: Archetype-based standards and technologies can be used to create a data warehouse environment that enables data from EHR systems to be reused in clinical research and decision support systems. With this approach, existing EHR data becomes available in a standardized and interoperable format, thus opening a world of possibilities toward semantic or concept-based reuse, query and communication of clinical data.
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