A multifaceted approach to advancing data quality and fitness standards in multi-institutional networks

To construct a data quality (DQ) system that incorporates combinations of methods to evaluate data characteristics and analytic fitness across research questions for multiple uses.

Drawing from experience of other data quality programs, network data extraction needs, and recurring study requirements, we developed 5 standards to guide development of a modular, multifaceted data quality system. These included annotation and documentation, ability to measure research readiness, reproducibility across networks, flexibility for the user, and interpretability to research and project teams. Implementation of checks based on these principles focused on reusability and interactive visualization of results.

We identified 10 check types producing over 444 check applications and deployed them in 2 multi-institutional networks. Check types span structural conformance to a data model, utility for common research needs, and study-specific customization. All check types are customizable without dependencies between them. A dashboard visualizes results, permitting adjustments based on number of data sources, need for source masking, and the user's focus. All components can be applied as written to any data source using OMOP and are readily modified for other data models.

We have extended previous work through our novel and multifaceted approach to data quality assessment, addressing needs in both network data improvement and research usage. We developed a capable and deployable system rather than tailoring to specific use cases.

Our novel DQ assessment system provides essential components for future standardization and collaboration to improve fitness of clinical data for intended use.