A Survey of Informatics Platforms That Enable Distributed Comparative Effectiveness Research Using Multi-Institutional Heterogeneous Clinical Data

School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.
Medical care (Impact Factor: 3.23). 07/2012; 50 Suppl(7):S49-59. DOI: 10.1097/MLR.0b013e318259c02b
Source: PubMed


Comparative effectiveness research (CER) has the potential to transform the current health care delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods, and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for interinstitutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast 6 large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, sociotechnical model of health information technology to help guide our work. We identified 6 generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.

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Available from: Dean Forrest Sittig, May 30, 2015
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    • "Elaborating on the negative effects of mapping, Sittig et al. [4] assert that " Design and development of these 'mapping' applications is one of the biggest challenges in any multi-institutional research project, because it is often the case that different organizations refer to the same activity, condition, or even procedure by different names, and the same names can refer to different things across institutions " . Zasada et al. [5] reached a similar conclusion where they comment on the process of changing data to match the prescribed format (the process they call curation) saying that " the curation stage can be quite labour intensive " . "
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    ABSTRACT: Clinical datasets are kept in diverse and disparate formats that are specific to systems these datasets are created in and might not be related to any known clinical data modelling standard. This makes their reuse or utilisation of clinical information in processes like biomedical research difficult. We conduct case studies of six biomedical research projects, assess data access-related difficulties encountered and reach conclusions on the overall impacts those difficulties had on the projects. We conclude that manual mapping of composite concepts found in clinical datasets has been reported as the most constraining issue and that the affected aspects of the projects were the length, cost and limited validity of project results. We then suggest that composite concepts are annotated using standards-based information models-openEHR Archetypes in particular. We then justify that approach and provide guidelines on the structure of the method that would facilitate its application.
    HINZ Conference 2015; 10/2015
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    • "A variety of platforms have been developed to support research projects involving heterogeneous clinical datasets generated by multiple institutions [ 2 ], [ 3 ], [ 9 – 11 ]. These platforms all provide support for decentralized data management, data sharing, and database federation. "
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    ABSTRACT: Presently, the process of extraction and dissemination of data subsets for research from clinical data warehouses is cumbersome and error prone. Furthermore, large-scale research projects often involve multiple users of the same data extract; each of these users may be authorized to access different data elements and specific subsets of the data extract. Once initial data extraction has been done for a research project, capability to transform the data for individual users and track which data are being accessed by which users in a secure environment is lacking in existing systems. This paper describes several methods that the authors are integrating into a system designed to provide secure, flexible, and auditable support for supplying users with data subsets. The methods implement secure, redactable, and auditable mechanisms for data extraction and dissemination. This paper describes the architecture along with an initial proof-of-concept implementation. Preliminary performance measurements show that the approach manages clinical data in redactable and auditable form with reasonable overheads.
    03/2013; 2013:21-25.
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    ABSTRACT: The Electronic Data Methods (EDM) Forum brings together perspectives from the Prospective Outcome Systems using Patient-specific Electronic data to Compare Tests and therapies (PROSPECT) studies, the Scalable Distributed Research Networks, and the Enhanced Registries projects. This paper discusses challenges faced by the research teams as part of their efforts to develop electronic clinical data (ECD) infrastructure to support comparative effectiveness research (CER). The findings reflect a set of opportunities for transdisciplinary learning, and will ideally enhance the transparency and generalizability of CER using ECD. Findings are based on 6 exploratory site visits conducted under naturalistic inquiry in the spring of 2011. Themes, challenges, and innovations were identified in the visit summaries through coding, keyword searches, and review for complex concepts. : The identified overarching challenges and emerging opportunities include: the substantial level of effort to establish and sustain data sharing partnerships; the importance of understanding the strengths and limitations of clinical informatics tools, platforms, and models that have emerged to enable research with ECD; the need for rigorous methods to assess data validity, quality, and context for multisite studies; and, emerging opportunities to achieve meaningful patient and consumer engagement and work collaboratively with multidisciplinary teams. The new infrastructure must evolve to serve a diverse set of potential users and must scale to address a range of CER or patient-centered outcomes research (PCOR) questions. To achieve this aim-to improve the quality, transparency, and reproducibility of CER and PCOR-a high level of collaboration and support is necessary to foster partnership and best practices as part of the EDM Forum.
    Medical care 07/2012; 50 Suppl(7):S11-8. DOI:10.1097/MLR.0b013e318258530f · 3.23 Impact Factor
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