# Using the Data Quality Dashboard to Improve the EHDEN Network
## 2 Materials and Methods  
### Data Conversion Process and DQD Collection  
Twenty-five DPs were awarded a COVID-19 Rapid Collaboration Call grant and these DPs covered 11 different countries and represented over 1 million COVID-19 patients [12]. The data sources themselves ranged from small COVID-19 registries to large medical records systems that covered lives for nearly an entire country. Once a DP was notified that they would receive a grant, they were assigned an EHDEN COVID-19 Taskforce. This taskforce consisted of a small team of technical experts with the collective skills and experience to walk DPs through the Data Conversion to Analysis Pipeline shown in Figure 1.  
This Data Conversion to Analysis Pipeline begins with WhiteRabbit and Rabbit-In-A-Hat [21], two tools maintained by EHDEN and used by the global OHDSI community. These tools facilitate the structural standardization of data by first revealing what tables, fields, and values are in a source data set (WhiteRabbit) and then allowing users to interactively map those source tables and fields to the CDM by way of a graphical user interface (Rabbit-In-A-Hat). Once the structural design specification is complete, standardization of the medical terminologies present in a source dataset can be done in two ways. First, one can leverage existing translations of common source terminologies (e.g., ICD10, Read, etc.) to standard concepts using the OMDP Vocabularies [22; 23]. If the source data contains codes not found in the OMDP Vocabularies, a tool called Usagi [24] is used to suggest potential standard concept mappings, followed by manual review and correction. After designing the structural and semantic standardization, Rabbit-In-A-Hat produces a document containing the complete instructions to convert the source data to an OMDP CDM instance. DPs then implement these specifications in an ETL (Extract, Transform, & Load) program utilizing the hardware and technologies they have access to within their organization. Typically, this takes the form of SQL written in the database environment they already have. Once data has been standardized, the next part of the Data Conversion to Analysis Pipeline is quality assessment, the cornerstone of which within EHDEN is the DQD [19; 20]. DQD is an open-source R package supporting OMDP CDM versions 5.2.2 and 5.3.1 that uses a systematic approach to run data quality checks against an OMDP CDM instance. It works with multiple database management systems, including PostgreSQL, Microsoft SQL Server, and Amazon Redshift. This tool applies, to a given database, over 3300 data quality checks (Table A1) organized into categories first described by [PERSON] et al. [25]. These are _conformance_ (requiring data to adhere to specified standards and formats), _completeness_ (ensuring that data values are present), and _plausibility_ (ensuring that the data values are believable). Using the MEASUREMT table and the MEASUREMT_CONCEPT_ID column as a motivating example, a _conformance_ check ensures the column exists, a _completeness_ check ensures it is non-zero above a given threshold, and a _plausibility_ check for a given measurement (e.g., Calcium; total) and given unit (e.g., milligram per deciliter) ensures the value is biologically plausible. The quality checks in the DQD tool can be described at a high-level by their check type. There are 20 types in total, the full list of which can be found in Appendix A. Each type represents a different data quality idea which is then automatically resolved and applied to the relevant tables and fields with data available as the DQD is run. These check types range in complexity from checking relational constraints to evaluating plausible values for measurement-unit pairs. For example, _cdmField_, a _conformance_ quality check, is a check type  
Figure 1: The Data Conversion to Analysis Pipeline to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) used by the European Health Data and Evidence Network (EHDEN).