title: A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.
abstract: OBJECTIVE
  Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is 'fit' for specific uses.
  
  
  METHODS
  DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework's inclusiveness was evaluated against ten published DQ terminologies.
  
  
  RESULTS
  Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.
  
  
  CONCLUSIONS
  Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.
  
  
  CONCLUSIONS
  A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.
authors.affiliation: University of Colorado Anschutz Medical Campus.
authors.forename: Michael G
authors.initials: MG
authors.lastname: Kahn
authors.affiliation: University of Colorado Anschutz Medical Campus.
authors.forename: Tiffany J
authors.initials: TJ
authors.lastname: Callahan
authors.affiliation: University of Colorado Anschutz Medical Campus.
authors.forename: Juliana
authors.initials: J
authors.lastname: Barnard
authors.affiliation: Kaiser Permanente Northwest.
authors.forename: Alan E
authors.initials: AE
authors.lastname: Bauck
authors.affiliation: Harvard Pilgrim Health Care Institute.
authors.forename: Jeff
authors.initials: J
authors.lastname: Brown
authors.affiliation: Hoag Memorial Hospital Presbyterian.
authors.forename: Bruce N
authors.initials: BN
authors.lastname: Davidson
authors.affiliation: University of Washington, Institute of Translational Health Sciences.
authors.forename: Hossein
authors.initials: H
authors.lastname: Estiri
authors.affiliation: University of Colorado Anschutz Medical Campus.
authors.forename: Carsten
authors.initials: C
authors.lastname: Goerg
authors.affiliation: AcademyHealth.
authors.forename: Erin
authors.initials: E
authors.lastname: Holve
authors.affiliation: University of Minnesota.
authors.forename: Steven G
authors.initials: SG
authors.lastname: Johnson
authors.affiliation: UNSW School of Public Health & Community Medicine.
authors.forename: Siaw-Teng
authors.initials: ST
authors.lastname: Liaw
authors.affiliation: National Academy of Sciences.
authors.forename: Marianne
authors.initials: M
authors.lastname: Hamilton-Lopez
authors.affiliation: University of Southern California.
authors.forename: Daniella
authors.initials: D
authors.lastname: Meeker
authors.affiliation: National Academy of Sciences.
authors.forename: Toan C
authors.initials: TC
authors.lastname: Ong
authors.affiliation: Janssen Research and Development.
authors.forename: Patrick
authors.initials: P
authors.lastname: Ryan
authors.affiliation: Columbia University.
authors.forename: Ning
authors.initials: N
authors.lastname: Shang
authors.affiliation: Oregon Health & Science University.
authors.forename: Nicole G
authors.initials: NG
authors.lastname: Weiskopf
authors.affiliation: Columbia University.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
authors.affiliation: University of Arkansas for Medical Sciences.
authors.forename: Meredith N
authors.initials: MN
authors.lastname: Zozus
authors.affiliation: National Academy of Sciences.
authors.forename: Lisa
authors.initials: L
authors.lastname: Schilling
keywords: data completeness; data use & quality; electronic health records