Increasing trust in real-world evidence through evaluation of observational data quality

Objective: Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. Materials and Methods: We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall realworld evidence generation and dissemination process to promote transparency and build confidence in the resulting output. Results: The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks. Discussion: Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research. Conclusion: Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.