Integrating clinical research with the Healthcare Enterprise: From the RE-USE project to the EHR4CR platform

INSERM, UMR_S 872 eq20, 15 rue de l'école de médecine, 75006 Paris, France.
Journal of Biomedical Informatics (Impact Factor: 2.48). 08/2011; 44 Suppl 1:S94-102. DOI: 10.1016/j.jbi.2011.07.007
Source: PubMed

ABSTRACT There are different approaches for repurposing clinical data collected in the Electronic Healthcare Record (EHR) for use in clinical research. Semantic integration of “siloed” applications across domain boundaries is the raison d’être of the standards-based profiles developed by the Integrating the Healthcare Enterprise (IHE) initiative – an initiative by healthcare professionals and industry promoting the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs in support of optimal patient care. In particular, the combination of two IHE profiles – the integration profile “Retrieve Form for Data Capture” (RFD), and the IHE content profile “Clinical Research Document” (CRD) – offers a straightforward approach to repurposing EHR data by enabling the pre-population of the case report forms (eCRF) used for clinical research data capture by Clinical Data Management Systems (CDMS) with previously collected EHR data.

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Available from: Pierre-Yves Lastic, Mar 12, 2014
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    • "Former single source projects such as [16] just used locally developed standards to connect their system instead of standardized terminology, whereas newer projects such as [17] [18] use ontologies together with a controlled vocabulary. In comparison, our approach focuses on the facilitation of the mapping and on the implementation of an ontological representation of the semantic annotation for use by client applications. "
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    ABSTRACT: Objective: Reusing EPR data for secondary purposes often requires mapping to classifications and vocabularies such as ICD, LOINC or NCI thesaurus. We aimed for a common architecture which supports the use of different vocabularies and mapping tools. Methods: We integrated the components clinical data warehouse, vocabulary resources and mapping tools with the EPR and client applications. Results: In two projects we used this architecture to map laboratory parameters from the LIS to LOINC, and to map clinical data elements from the Soarian EPR to the cancer registry system using the NCI-Thesaurus®. Conclusion: The approach was successful in both projects. The reference architecture does not resolve the mapping task, but provides reusable integration links between the different components and thus facilitates further mapping activities.
    Studies in health technology and informatics 01/2014; 198:40-6.
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    • "The CAT research portal is an institute-specific data warehouse that can also be used to share data in a privacy-preserving and semantic-interoperable manner using internationally accepted data exchange standards and ontologies such as described in an accompanying study [28] [29], addressing the growing need for standardized data exchange between medical centres [1] [3] [12]. "
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    ABSTRACT: INTRODUCTION: Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field. MATERIAL AND METHODS: In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method. RESULTS: The average time per case to collect the NSCLC data manually was 10.4±2.1min and 4.3±1.1min when using the automated method (p<0.001). For rectal cancer, these times were 13.5±4.1 and 6.8±2.4min, respectively (p<0.001). In 3.2% of the data collected for NSCLC and 5.3% for rectal cancer, there was a discrepancy between the manual and automated method. CONCLUSIONS: Aggregating multiple data sources in a data warehouse combined with tools for extraction of relevant parameters is beneficial for data collection times and offers the ability to improve data quality. The initial investments in digitizing the data are expected to be compensated due to the flexibility of the data analysis. Furthermore, successive investigations can easily select trial candidates and extract new parameters from the existing databases.
    Radiotherapy and Oncology 02/2013; 108(1). DOI:10.1016/j.radonc.2012.09.019 · 4.86 Impact Factor
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    • "However, the limitation is that once the prepopulation templates are modified due to emerging requirements, new mappings are needed. We addressed this shortcoming in a previous work proposing a dynamic mapping mechanism supported by the use of SNOMED CT as the " pivot terminology " to facilitate mappings [3]. We argue that integrating patient care and clinical research domains requires a standard-based expressive and scalable semantic interoperability framework, allowing dynamic mappings between data structures and semantics of varying data sources. "
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    ABSTRACT: A major barrier to repurposing routinely collected data for clinical research is the heterogeneity of healthcare information systems. Electronic Healthcare Record for Clinical Research (EHR4CR) is a European platform designed to improve the efficiency of conducting clinical trials. In this paper, we propose an initial architecture of the EHR4CR Semantic Interoperability Framework. We used a model-driven engineering approach to build a reference HL7-based multidimensional model bound to a set of reference clinical terminologies acting as a global as view model. We then conducted an evaluation of its expressiveness for patient eligibility. The EHR4CR information model consists in one fact table dedicated to clinical statement and 4 dimensions. The EHR4CR terminology integrates reference terminologies used in patient care (e.g LOINC, ICD-10, SNOMED CT, etc). We used the Object Constraint Language (OCL) to represent patterns of eligibility criteria as constraints on the EHR4CR model to be further transformed in SQL statements executed on different clinical data warehouses.
    Studies in health technology and informatics 01/2012; 180:534-8.
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