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      A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

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          Abstract

          Objective:

          Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses.

          Materials and Methods:

          DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies.

          Results:

          Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.

          Discussion:

          Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.

          Conclusion:

          A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

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          Most cited references65

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          Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

          Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.
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            Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).

            Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.
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              Limitations of the randomized controlled trial in evaluating population-based health interventions.

              Population- and systems-based interventions need evaluation, but the randomized controlled trial (RCT) research design has significant limitations when applied to their complexity. After some years of being largely dismissed in the ranking of evidence in medicine, alternatives to the RCT have been debated recently in public health and related population and social service fields to identify the trade-offs in their use when randomization is impractical or unethical. This review summarizes recent debates and considers the pragmatic and economic issues associated with evaluating whole-population interventions while maintaining scientific validity and credibility.
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                Author and article information

                Journal
                EGEMS (Wash DC)
                EGEMS (Wash DC)
                eGEMs
                eGEMs
                AcademyHealth
                2327-9214
                2016
                11 September 2016
                : 4
                : 1
                : 1244
                Affiliations
                [i ]University of Colorado Anschutz Medical Campus
                [ii ]Kaiser Permanente Northwest
                [iii ]Harvard Pilgrim Health Care Institute
                [iv ]Hoag Memorial Hospital Presbyterian
                [v ]University of Washington, Institute of Translational Health Sciences
                [vi ]AcademyHealth
                [vii ]University of Minnesota
                [viii ]UNSW School of Public Health & Community Medicine
                [ix ]National Academy of Sciences
                [x ]University of Southern California
                [xi ]University of Colorado, Denver
                [xii ]Janssen Research and Development
                [xiii ]Columbia University
                [xiv ]Oregon Health & Science University
                [xv ]University of Arkansas for Medical Sciences
                Article
                egems1244
                10.13063/2327-9214.1244
                5051581
                27713905
                c829fc34-3b88-4dee-b631-2e8b4d67b26e
                Copyright @ 2016

                All eGEMs publications are licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License http://creativecommons.org/licenses/by-nc-nd/3.0/

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                Categories
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                electronic health records,data use & quality,data completeness

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