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      Profiling Clinical Datasets for Data Quality Assessment and Improvement

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      BCS Health Informatics Scotland (HIS) (HIS)

      BCS Health Informatics Scotland (HIS)

      2 - 3 September 2014

      Clinical Datasets, Data Profiling, Data Quality, Translational Research

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          Abstract

          Clinical datasets are the most critical resources or assets in the repository of Electronic Health Records (EHRs) and their quality gains competitive advantages in translational research. Accurate, reliable, and consistent representation of clinical datasets are essential for answering key research questions. However, a major issue with carrying out research on routinely collected primary care datasets is that they are often not fit-for-purpose or research-ready. It often takes months (if not years) for researchers to clean and transform clinical datasets for meaningful translational research. Profiling clinical datasets provides a proactive approach to examining and understanding the content, context and structure of source system data. The objective of this study was to develop a profiling dashboard to monitor, measure, assess, and improve the quality of clinical datasets hosted and maintained by the Health Informatics Centre (HIC) at the University of Dundee. Preliminary results indicated that the dashboard affords the flexibility to perform objective assessment of data quality, in terms of accessibility, accuracy, appropriate amount of data, completeness, and consistency.

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          Most cited references 9

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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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            Beyond Accuracy: What Data Quality Means to Data Consumers

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              Data quality in context

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                Author and article information

                Contributors
                Conference
                September 2014
                September 2014
                : 1-8
                Affiliations
                Health Informatics Centre,

                University of Dundee,

                Scotland, U.K.
                Article
                10.14236/ewic/HIS2014.1
                © Wilfred Bonney et al. Published by BCS Learning and Development Ltd. BCS Health Informatics Scotland (HIS), Glasgow, UK

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                BCS Health Informatics Scotland (HIS)
                HIS
                Glasgow, UK
                2 - 3 September 2014
                Electronic Workshops in Computing (eWiC)
                BCS Health Informatics Scotland (HIS)
                Product
                Product Information: 1477-9358 BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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