The Mappability of Clinical Real-World Data of Patients with Melanoma to Oncological Fast Healthcare Interoperability Resources (FHIR) Profiles: A Single-Center Interoperability Study
Jessica Swoboda,
Moritz Albert,
Catharina Lena Beckmann,
Georg Christian Lodde,
Elisabeth Livingstone,
Felix Nensa,
Dirk Schadendorf,
Britta Böckmann
Affiliations
Jessica Swoboda
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Moritz Albert
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Catharina Lena Beckmann
Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), Emil-Figge-Straße 42, 44227 Dortmund, Germany
Georg Christian Lodde
Department of Dermatology, Venereology and Allergology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Elisabeth Livingstone
Department of Dermatology, Venereology and Allergology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Felix Nensa
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Dirk Schadendorf
Department of Dermatology, Venereology and Allergology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
Britta Böckmann
Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
(1) Background: Tumor-specific standardized data are essential for AI-based progress in research, e.g., for predicting adverse events in patients with melanoma. Although there are oncological Fast Healthcare Interoperability Resources (FHIR) profiles, it is unclear how well these can represent malignant melanoma. (2) Methods: We created a methodology pipeline to assess to what extent an oncological FHIR profile, in combination with a standard FHIR specification, can represent a real-world data set. We extracted Electronic Health Record (EHR) data from a data platform, and identified and validated relevant features. We created a melanoma data model and mapped its features to the oncological HL7 FHIR Basisprofil Onkologie [Basic Profile Oncology] and the standard FHIR specification R4. (3) Results: We identified 216 features. Mapping showed that 45 out of 216 (20.83%) features could be mapped completely or with adjustments using the Basisprofil Onkologie [Basic Profile Oncology], and 129 (60.85%) features could be mapped using the standard FHIR specification. A total of 39 (18.06%) new, non-mappable features could be identified. (4) Conclusions: Our tumor-specific real-world melanoma data could be partially mapped using a combination of an oncological FHIR profile and a standard FHIR specification. However, important data features were lost or had to be mapped with self-defined extensions, resulting in limited interoperability.
