# Using the Data Quality Dashboard to Improve the EHDEN Network
## 3 Results  
At the time of this writing, 15 of the 25 DPs completed their DQD review, 8 had not completed, and 2 were not considered, as they did not run DQD for various reasons. Table 1 lists, in alphabetical order, the 15 DPs that contributed data for this work. These 15 DPs represented 10 countries, primarily were electronic medical record (EHR) data, and ranged in size from 766 subjects to 2.8 M subjects. The types of subjects captured within each DP were diverse in type (e.g., general practitioner data versus hospital data), depth (e.g., data only about COVID-19 care to data on all clinical care) and complexity (e.g., 1 table to 100s of tables). These DPs also represented diverse technology platforms that include several types of database management systems (the most popular being PostgreSQL, Microsoft SQL Server, and Amazon Redshift), differing infrastructure set up (e.g., on premise versus in the cloud), and differing number of processors (1 to 56) and memory sizes (8 GB to 1.5 TB) at their disposal. Additional information about each DP can be found at [[https://www.ehden.eu/datapartners/](https://www.ehden.eu/datapartners/)]([https://www.ehden.eu/datapartners/](https://www.ehden.eu/datapartners/)) (accessed on 1 October 2021). For the rest of this paper, these DPs will be anonymized. For each of the 15 DPs, the minimum number of DQD runs was 2 times, maximum 33, with a median of 4 times. The total execution time on the first run of the DQD ranged from 0.02 h to 10.5 h with a median of 0.7 h. The total execution time on the last run of the DQD ranged from 0.02 h to 25.0 h with a median of 0.7 h. These ranges were heavily dependent on the size of the database, the size of the server the DP used, and the amount of compute available. The duration of time over which the first and last DQD were run ranged from 10 days to 190 days, with a median of 90 days. The number of DQD runs and time to completion were impacted by many things such as the number of issues identified by DQD that needed to be fixed, desire to fix all issues identified by DQD, the amount of time a specific DP could afford to spend working on their ETL conversion, and experience with ETL conversions and the ETL technology required to do so (thus higher number of subjects did not necessarily equate to more DQD issues as there are many factors at play). After limiting the comparison to the checks that were run both at first and at last, all DPs had a higher percent passing in the last run of the DQD compared to the first (Table 2), with a median of 883 total checks evaluated. We also quantified the number of checks that saw a reduction in the percent of failing rows even though they still failed the DQD check. Most DPs only had 1 or 2 checks that were labeled as failures though they showed improvement, except for DP8 and DP9, which had 12 and 8 respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Partner** & **Data Type** & **Country** & **Total No of Subjects** \\ \hline Amsterdam University Medical & & & \\ Centers—COVID-19 ICU Decision Support & EHR & The Netherlands & 1.8 K \\ (Amsterdam UMC) * & & & \\ Clinical Practice Research Datalink & & & \\ (CPRD) AURUM & EHR & United Kingdom & 409 K ** \\ Danish Colorectal Cancer Group (DCCG) & & & \\ with Center for Surgical Science & & & \\ Fondazione IRCCS Istituto Neurologico & & & \\ Carlo Besta COVID-19 Database (FINCB) * & & & \\ Health Data Warehouse of Assistance & & & \\ Publique—Hopitaux de Marseille (AP-HM) & & & \\ Health Informatics Centre (HIC) & & & \\ Heliant—University Clinical Center of Serbia & & & \\  & & & \\  & & & \\ Information System for Research in Primary & & & \\ Care—Hospitalization Linked Data (SIDIAP) & & & \\  & & & \\  & & & \\ Istanbul University—Faculty of & & & \\ Medicine (IU) * & & & \\  & & & \\  & & & \\  & & & \\  & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 1: Data Partners (DPs) that contributed data (alphabetical order).