title: A Data Quality Ontology for the Secondary Use of EHR Data.
abstract: The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the important terms used to describe data quality concepts and harmonized into an ontology. Four high-level data quality dimensions ("correctness", "consistency", "completeness" and "currency") categorize 19 lower level measures. The ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases. A detailed example is presented to demonstrate its utility. The DQ ontology can make data validation more common and reproducible.
authors.affiliation: University of Minnesota, Institute for Health Informatics.
authors.forename: Steven G
authors.initials: SG
authors.lastname: Johnson
authors.affiliation: University of Minnesota, Institute for Health Informatics.
authors.forename: Stuart
authors.initials: S
authors.lastname: Speedie
authors.affiliation: University of Minnesota, Institute for Health Informatics.
authors.forename: Gyorgy
authors.initials: G
authors.lastname: Simon
authors.affiliation: University of Minnesota, Department of Computer Science.
authors.forename: Vipin
authors.initials: V
authors.lastname: Kumar
authors.affiliation: University of Minnesota, Institute for Health Informatics; University of Minnesota, School of Nursing.
authors.forename: Bonnie L
authors.initials: BL
authors.lastname: Westra
mesh_terms: D064229:Biological Ontologies; D000068598:Data Accuracy; D057286:Electronic Health Records; D006801:Humans