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

Abstract

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.

Journal

Journal ID (nlm-ta): J Am Med Inform Assoc

Journal ID (iso-abbrev): J Am Med Inform Assoc

Journal ID (publisher-id): jamia

Journal ID (hwp): amiajnl

Title: Journal of the American Medical Informatics Association : JAMIA

Publisher: BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )

ISSN (Print): 1067-5027

ISSN (Electronic): 1527-974X

Publication date (Print): Jan-Feb 2013

Publication date PMC-release: 1 January 2013

Volume: 20

Issue: 1

Pages: 144-151

Affiliations

Department of Biomedical Informatics, Columbia University, New York, New York, USA

Author notes

[Correspondence to ] Nicole Gray Weiskopf, Department of Biomedical Informatics, Columbia University, 622 W 168th Street, VC-5, New York, NY 10032, USA; nicole.weiskopf@ 123456dbmi.columbia.edu

Article

Publisher ID: amiajnl-2011-000681

DOI: 10.1136/amiajnl-2011-000681

PMC ID: 3555312

PubMed ID: 22733976

Copyright statement: Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/3.0/ and http://creativecommons.org/licenses/by-nc/3.0/legalcode

Product

Categories

Subject: Focus on Data Sharing

Subject: 1506

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