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.
Highlights
Over the past 30 years, more observational health data have become available for use in research due to the digitization of health records and the comprehensive nature of administrative claims [1,2,3]
With this work we present the process by which Data Partners (DPs) chosen for the COVID-19 Rapid Collaboration Call mapped their data to the OMOP CDM
The types of subjects captured within each DP were diverse in type, depth and complexity (e.g., 1 table to 100s of tables)
Summary
Over the past 30 years, more observational health data have become available for use in research due to the digitization of health records and the comprehensive nature of administrative claims [1,2,3] The potential of this data is well known; the non-invasive, passive method of data collection bypasses the ethical concerns of human subjects research and the speed with which it is collected foreshadows a future of near-real time evidence generation [4,5]. Professional organizations such as the American Thoracic Society have publicly announced their intention to use observational studies to inform clinical practice guidelines [6].
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