Article
Collaborative metadata management in a hospital-based cancer registry: An example using the OHDSI standardized vocabularies framework
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Authors
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Jasmin Carus
- Universitäres Cancer Center Hamburg, Hubertus Wald Tumorzentrum, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany; Institute for Applied Medical Informatics (IAM), Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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Christopher Gundler
- Institute for Applied Medical Informatics (IAM), Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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Maximilian Ataian
- Institute for Applied Medical Informatics (IAM), Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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Stefan Bartels
- Universitäres Cancer Center Hamburg, Hubertus Wald Tumorzentrum, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| Published: | September 6, 2024 |
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Outline
Text
The requirements for a modern cancer registry increased rapidly in recent years. It is becoming apparent that highly semantical interoperable data modeling solutions play a key role when it comes to deriving real-world evidence from cancer registries. Through a high standardization of medical entries, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) of the Observational Health Data Sciences and Informatics (OHDSI) is one such highly interoperable solution. Nevertheless, data elements that are not part of the OMOP CDM, such as data that arises in the area of cancer registration in Germany, must be mapped manually to the OMOP CDM. This reduces semantic interoperability, as it cannot be guaranteed that the data elements that are similar/same at the locations are mapped to the same standard in the OMOP model. Furthermore, in the current approach, the mapping is carried out in tables. Since the clarity of the actual manually mapping workflow is considerably reduced with many mappings or collaborative mappings, this work examines the extent to which the manually mapped data elements can be integrated into a graphical user interface in order to simplify the manually mapping workflow. Therefore, it will be investigated how a metadata repository is suitable for collaborative work/editing of mappings to close this gap. Furthermore this study explors a data pipeline for automated data extraction from REST interface in tabular manner, to ensure easy integration of mapped metadata into relational backend architectures, as OMOP CDM is a relational data model.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
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