Abstract
BackgroundThe current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.MethodsBased on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.ResultsA core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.ConclusionGECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
Highlights
The current COVID-19 pandemic has led to a surge of research activity
To improve interoperability of COVID-19 data, we developed the German Corona Consensus Dataset (GECCO), which uses international health Information Technology (IT) stand‐ ards and terminologies for interoperable data exchange
Selection of data elements An initial dataset was compiled as a working basis by merging data elements and response options of the following projects: the ISARIC-World Health Organization (WHO) Case Report Form (CRF) [8]; the Pa-COVID-19 study [11], which investigates the patho‐ physiology of COVID-19 in a prospective patient cohort; the LEOSS case registry [3], a clinical patient registry for patients infected with SARS-CoV-2 initiated by the ESCMID Emerging Infections Task Force (EITaF), the German Center for Infection Research (DZIF) and the German Society for Infectiology (DGI)
Summary
While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. While this research provides important new insights, the multitude of studies threatens to generate a dangerous fragmenta‐ tion of information. Sass et al BMC Med Inform Decis Mak (2020) 20:341 urgently needed scientific knowledge about SARS-CoV-2 and COVID-19. To avoid this fragmentation of informa‐ tion and make COVID-19 data more comparable and exchangeable across studies and institutions, interoper‐ able datasets are needed. To make data syntactically and semantically interoperable, data elements have to be embedded in standard data structures that can be exchanged across IT systems; they have to use com‐ mon terminologies that unambiguously define the mean‐ ing of clinical concepts
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