We verified our solution based on an independently developed test patient. Results: In the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. We enriched our ontology with the necessary information to display it in a UI. In this process, we reduced the complex profiles to code value pairs for improved usability. We analyzed the profiles and identified search criteria for the visual representation. Methods: We used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries.

Objective: This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible.

The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible.

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