In this report, we presented the GECCO dataset, a core collection of data elements for acquiring and exchanging information about COVID-19 patients. By using standardized data structures (HL7 FHIR profiles) and international terminologies, the GECCO dataset is an important step towards interoperability of COVID-19 research data. It can facilitate harmonized data collection and analysis across institutions and IT systems, for example in clinical studies, registries or digital health applications.
A key factor to the successful application of standard datasets like GECCO is a close collaboration with the scientific community. To ensure a high acceptance of the dataset, the development of GECCO therefore included clinicians from a wide variety of medical disciplines and professional associations as well as experts in digital health, standardization and clinical terminologies. GECCO also collaborates closely with standards developing organizations such as HL7 and Integrating the Healthcare Enterprise (IHE) as well as other initiatives aiming to improve health data interoperability, such as the Medical Informatics Initiative , NFDI4Health  and the Corona Component Standards (cocos) .
For the successful application of standard datasets like GECCO, it is also important that these datasets are embedded in larger infrastructures for secure and interoperable data sharing across institutions. Initiatives like, for example, the National COVID Cohort Collaborative (N3C) in the US , OpenSAFELY in the UK  or the international project Secure Collective Research (SCOR)  are developing platforms for a secure, cross-institutional analysis of COVID-19 data. Similarly, GECCO is part of the German COVID-19 Research Network of University Medicine , which aims to bundle the resources of German university hospitals to improve diagnostics and treatment of COVID-19 patients. The network also includes a research data infrastructure for the secure and interoperable data exchange across university hospitals , for which GECCO provides a standard data structure. For example, projects such as NAPKON, a national project for collecting research data during pandemics , will collect their data according to the specifications of the GECCO dataset.
Note that the GECCO dataset was originally developed for German university medicine. However, we used international standards and terminologies to ensure that the GECCO dataset can be extended to use cases beyond university medicine and also be applied in international contexts. When defining the FHIR profiles, we also took into account international work (for example the IPS ) to make sure that the GECCO dataset can provide interoperability when used internationally.
Although the GECCO dataset was designed to be as compact and manageable as possible, acquiring and recording the information for all data elements still requires time (for example, when entering the information in an electronic case report form). Moreover, manual documentation is prone to transcription errors. Conversely, manually abstracted and structured information from unstructured health records may provide relevant insights for care-providers and improve their understanding of risk and outcome. For some of the data items, it is therefore desirable to automatically exchange data between a GECCO-based study database and existing IT systems, such as hospital information systems or clinical trial software. This requires standard interfaces between these systems. The FHIR profiles of the GECCO dataset provide an interoperable, machine-readable data structure that can facilitate this data exchange across IT systems. For example, LOINC-coded information about patients’ laboratory values could be directly transferred from the hospital information system to the GECCO dataset. For electronic data capture (EDC) systems used in clinical studies, converters are currently being developed that transform the underlying software formats into the GECCO HL7 FHIR format for interoperable data exchange. Independently of the work presented here, the GECCO dataset has also been converted to the CDISC Operational Data Model (ODM) and is published on the Portal of Medical Data Models (MDM) of the University of Münster .
The aim of the GECCO dataset was to define a compact set of core data elements for which most COVID-19 studies (particularly the studies conducted at German university hospitals) can provide full information. However, if not explicitly required, studies that want to use the GECCO dataset may use subsets of the dataset if they are unable to provide full information for all data elements.
Scientific knowledge about COVID-19 and SARS-CoV-2 is changing fast, which may necessitate modifications to the GECCO dataset in the future. Furthermore, the use of the GECCO dataset in clinical research projects will provide practical experience that may also motivate changes to the dataset. To incorporate new knowledge into the dataset, the COVID-19 Research Network of University Medicine  will put a governance framework in place that will coordinate revisions and extensions to the dataset. Domain-specific extension modules are already in preparation, which include many of the data elements that were not considered essential for the core dataset. Extension modules currently planned are: laboratory, diagnostics, immunology, gynecology and pregnancy, epidemiology, pediatrics, intensive care, oncology, radiology, virology, psychiatry and neurology (these extension modules are also made accessible on the ART-DECOR platform ).