# Using the Data Quality Dashboard to Improve the EHDEN Network
###### Abstract  
Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Collaboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Fifteen Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance issues but showed less of an impact on completeness and plausibility checks. This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research. data quality; OMOP CDM; EHDEN; healthcare data; real world data; RWD +
Footnote †: journal: Medical Informatics  
0000-0001-9900-0001-9900-0001-9900-00190-9900-00190-9900-00190-9001-99000-01900-00190-90001-9450 million patient records. With the rise of COVID-19, this need for a European-wide federated network of observational health data was thrown into sharp relief as health officials scrambled to understand the natural history of the disease [11]. To aid this effort, EHDEN held a Rapid Collaboration Call inviting institutions across Europe with COVID-19 data to participate [12]. This presented a challenge: how can we be sure the data are of high quality for research while quickly integrating them into the network? EHDEN proposes to solve this problem of data integration by choosing to harmonize the network on the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) [13]. European real-world clinical data is generated from diverse medical record systems, stored in different ways, captured in different languages, and controlled by differing policy restrictions. Conversion to the OMOP CDM allows for interoperability across data sources as it standardizes real world data to both a common structure (data model) and terminology (vocabulary). This then leaves the problem of ensuring these data are of high-quality to support evidence generation. While there is little doubt in the research utility of observational health data, concerns are often voiced about the quality of the data since it is not primarily collected for research purposes. A study published in 2018 by [PERSON] et al. detailed a review of 34 observational health databases representing Northern, Central, and Western Europe [14]. The authors found that only half had published validation studies detailing the extent to which patients' records aligned with national registries or statistics reports [14]. Among databases studied, the approaches taken to assess data quality were observed to be very different. Clinical Practice Research Datalink (CPRD), for example, provides indicators of patient and practice acceptability to researchers [15]. In contrast, the Information System for the Development of Research in Primary Care (SIDIAP) from Catalonia, Spain created a scoring system to assess the completeness of the data provided by primary care practitioners [16].