gms | German Medical Science

Introduction: During the COVID-19 pandemic many individual apps have been developed to collect and track healthcare data and make them accessible to researchers. A common interoperable format is necessary to analyze them efficiently [1]. The NUM-COMPASS project is part of the German COVID-19 Research Network of University Medicine (NUM) [2]. It builds a coordination/technology platform as starting point enabling researchers to collect data compliant with the German Corona Consensus Dataset (GECCO) [3], assisting with regulatory frameworks [4] in developing pandemic apps. This abstract highlights the GECCO interoperability recommendations by NUM-COMPASS.

Methods: We analyzed the process of data standardization in pandemic apps and developed training material for researchers/developers with poor knowledge/awareness on the importance of semantic and syntactic interoperability. It offers a how-to and a conformity checkpoint for data models. GECCO was predefined as reference to use. Focus was laid onto identifying weaknesses and breakpoints for interoperable data collection. Developers of other COVID-19 projects were interviewed to understand their needs. To be considered and accepted by executives, an executive summary was formulated to raise awareness of the existence and possibilities of NUM-COMPASS.

A First Contact Package (FCP) was devised to inform researchers/developers. This aims to break down the larger concept of interoperable data capturing, guiding researchers/developers through the FAIR [5], [6] principles and the GECCO Implementation Guide [7].

Results: In existing pandemic apps [8], [9], [10], an overlap with GECCO was apparent, yet no complete compliance. A Late Mapping Scheme (LMS) was developed to allow such projects to participate with as much data as possible. Five equivalence categories were defined for variable mapping (identical, matching, transformable, similar, different; based on [[11]). The mapping is documented in a spreadsheet, with data types, units, answer options and annotation of the most important GECCO concepts (based on GECCO in ART-DECOR^® [12] and the REDCap implementation [13]). For FHIR-experienced persons, resource types and variable IDs were added. For more visual, FHIR-”naive” developers, the MDM Portal’s version can be used for a potential layout overview [14].

Theoretical structures to prove the conformity of the data model with GECCO have been developed with a seal to ensure the interoperability of the collected data.

Discussion: It is of paramount importance that collected data around COVID-19 and long COVID can be federated to be interpreted collectively [1]. The GECCO Dataset is a good start to collect COVID-19 related data. GECCO PLUS [12] and the MII core data set [15] can capture data beyond the COVID-19 context providing researchers with more holistic interoperable data for further uses. An ongoing effort for NUM-COMPASS is to assure and include standardization in development and reduce the burden of unstandardized data in future pandemics. Community building around NUM-COMPASS allows the growth of awareness and foster education for its platform [2].

Conclusion: The pandemic catalyzed efforts for standardization. It should be used consistently and routinely from now on, with a focus on initial interoperability as described in FCP, so LMS is needed less in future projects. However, for data federation appropriate consent and usage licenses are equally important.

Funding: NUM-COMPASS is part of the Research Network of University Medicine (“Netzwerk Universitätsmedizin”), funded by the German Federal Ministry of Education and Research (funding reference 01KX2021).

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.