During the OMOP research, it was evident that standard processes and tools for a data quality assurance system to continuously improve upon the common data model dataset for drug-outcome analysis was needed. It is essential for the future of a national active surveillance system that data quality practices and findings are transparently reported to provide greater confidence to all stakeholders about the reliability of drug safety analysis results. The figure below illustrates OMOP’s data progression process (from health encounters to analyzing research results) and the corresponding validation procedures that can be applied along the data progression.

In order to interpret the results of any analysis on a data source, the characteristics of the data source should be clearly understood. The Observational Source Characteristics Analysis Report (OSCAR) provides a systematic approach for summarizing all observational healthcare data within the OMOP common data model. The Generalized Review of OSCAR Unified Checking (GROUCH) uses data created by OSCAR from multiple data sources to look for differences in data.