Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research
| Autor: | Chunhua Weng, Nicole G. Weiskopf |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Research design
Quality Control Correctness 020205 medical informatics Computer science MEDLINE clinical research informatics Health Informatics 02 engineering and technology Reuse knowledge representations Clinical research 03 medical and health sciences methods for integration of information from disparate sources 0302 clinical medicine knowledge bases 0202 electrical engineering electronic engineering information engineering data quality Humans Generalizability theory 030212 general & internal medicine Focus on Data Sharing Data element Data collection Information Dissemination Data Collection secondary use Reproducibility of Results Data science 3. Good health knowledge acquisition electronic health records Research Design Data quality knowledge acquisition and knowledge management |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA |
| ISSN: | 1527-974X 1067-5027 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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