Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for federated Feasibility Queries in the context of COVID-19: An automated approach (Preprint)

Abstract

The COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Data Set (GECCO) specifies how data for COVID-19 patients shall be standardized in FHIR Profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden while allowing feasibility queries.

This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface and how a mapping created together with the ontology can translate user input into FHIR queries.

We used the FHIR profiles from the GECCO dataset combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a user interface. We developed an intermediate query language to transform the queries from the user interface to federated FHIR requests. Separation of concerns results in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping is created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process.

In the scope of this project, 82 of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in six cases due to different versions used to generate the test data and the UI-Profiles, the support for specific code systems, and the evaluation of post-coordinated SNOMED codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions.

We developed an automatic process to generate ontology and mapping files for FHIR formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that an automatic ontology generation on FHIR profiles is feasible.