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.Materials and 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.Discussion: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.Conclusion: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.

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

“…25 Over 11,000 DQ checks from six participating organizations were received, nearly all of which were successfully mapped to the harmonized DQ terminology categories. These findings provide validation for the harmonized DQA terminology, highlighting its ability to successfully represent a robust sample of DQ checks across highly diverse data networks.…”

“…25 This harmonized DQA terminology describes a set of categories that operate within two DQA evaluation contexts, where confirmation of expectations about aspects of the data are based on comparisons to local knowledge, prespecified metadata (verification), or to external benchmarks and gold standards (validation). Within each of these data contexts there are three categories and eight subcategories (referred to as "harmonized DQA categories"): The harmonized DQA terminology encompasses only those DQA categories considered to be "intrinsic" (i.e., dimensions pertaining to the data values themselves) 26 to the data.…”

A Framework for Leveraging “Big Data” to Advance Epidemiology and Improve Quality: Design of the VA Colonoscopy Collaborative

“…We have also implemented a number of statistical techniques to assess data quality, including conformance, completeness and plausibility [9]. For categorical variables (e.g., race/ethnicity), extent of missing data were evaluated.…”

Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence

“…Kahn and colleagues have developed a pragmatic framework for data quality assessment in EHRs. Their framework assesses “fitness for use” in health care QI and comparative effectiveness research (CER) [71316]. The National Institutes of Health (NIH) National Healthcare Research Systems Collaboratory has recently added a data quality review criterion for its research projects [17].…”