Article
How FAIR are frameworks for data quality measures in clinical research?
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Authors
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Dagmar Waltemath
- Universitätsmedizin Greifswald, Greifswald, Germany
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Esther Inau
- Universitätsmedizin Greifswald, Greifswald, Germany
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Atinkut Alamirrew Zeleke
- Universität Greifswald, Greifswald, Germany
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Carsten Oliver Schmidt
- Universität Greifswald, Greifswald, Germany
| Published: | February 26, 2021 |
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Outline
Text
Background: A fundamental component of digital clinical information systems (CIS) are Electronic Health Records [1]. Efforts such as the Medical Informatics Initiative (https://www.medizininformatik-initiative.de) strive to harmonise the data kept in CIS, to develop procedures for data pseudonymization and broad consent, and to integrate clinical research data across national hospitals through the establishment of data integration centres at all sites. Among the key requirements for effective health information exchange is the use of standardized data of high quality [2], [3]. Hence it is interesting – and necessary – to ask: How FAIR is clinical research data? Can it be savely reused? Data quality frameworks target issues relating to these questions.
The FAIR (findability, accessibility, interoperability, and reusability) data principles are a set of widely applicable ‘permissive guidelines’ offering a basis for developing flexible community standards for healthcare data community [4]. Maturity Indicators (MIs) detail facets of FAIRness that can be objectively evaluated by a machine, based on human curation by the community [5].
Methods: We conducted a literature search in the PubMed database for articles published since 2014 to identify research on data quality frameworks that investigates the FAIRness of health data. Based on the identified literature, three domain experts discussed key concepts, frameworks, and gaps in research and practice. Based on an established data quality framework in the related field of cohort study research [6] and incorporating ongoing discussions of FAIR indicators, the authors then analysed which additional measures could be applied to data quality evaluation in EHR data.
Results: From the identified articles, for this presentation, we concentrate on the two most recent data quality assessment frameworks by Kahn et al. [7] and Weiskopf et al. [8], where multidisciplinary experts developed and tested data quality assessment frameworks for EHR data. We explain the underlying conceptual basis of data quality measures and discuss their use in the contexts of EHR data. Moreover, we highlight possibilities and challenges of the real-world implementation of these tools when working in the context of clinical data. In our talk, we furthermore summarise the relevance of data quality frameworks towards FAIR medical data and discuss which of the proposed maturity indicators may be useful in measuring the FAIRness of clinical research data obtained from health care systems, particularly from EHRs. One possibility to deal with the lack of quality measures and FAIRness is to apply measures from related fields. Here we discuss the applicability of established guidelines for cohort studies. We conclude that even if conceptually similar aspects exist (such as data completeness or correctness), methods to compute data quality indicators may differ due to the different data production processes in EHR and research data. Focusing on the German landscape, we summarise our talk with an outline of the upcoming challenges in making EHR data FAIR.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
References
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