Tessa Ohlsen, M.Sc.

Introduction: As part of the German Medical Informatics Initiative (MII) and Network University Medicine (NUM), a central research terminology service (TS) is provided by the Service Unit Terminology Services (SU-TermServ). This HL7 FHIR-based service depends on the timely and comprehensive availability of FHIR terminology resources to provide the necessary interactions for the distributed MII/NUM infrastructure. While German legislation has recently instituted a national terminology service for medical classifications and terminologies, the scope of the MII and NUM extends beyond routine patient care, encompassing the need for supplementary or specialized services and terminologies that are not commonly utilized elsewhere.

Methods: The SU-TermServ’s processes are based on established FHIR principles and the recently-proposed Canonical Resources Management Infrastructure Implementation Guide, which are outlined in this paper.

Results: The strategy and processes implemented within the project can deliver the needed resources both to the central FHIR terminology service, but also to the local data integration centers, in a transparent and consistent fashion. The service currently provides approximately 7000 resources to users via the standardized FHIR API.

Conclusion: The professionalized distribution and maintenance of these terminological resources and the provision of a powerful TS implementation aids both the development of the Core Data Set and the data integration centers, and ultimately biomedical researchers requesting access to this rich data.

Introduction: The ongoing development of medical coding systems such as ICD-10-GM and OPS presents challenges in ensuring compatibility across versions. This paper introduces Covhisto, a web application for visualizing and analysing changes across multiple versions.

Methods: Covhisto uses crosswalk tables published by the German Federal Institute for Drugs and Medical Devices (BfArM) and data processing pipeline to track changes between ICD-10-GM and OPS versions. Key features include methods for following individual code histories and analyzing structural changes in the coding systems.

Results: Covhisto allows users to trace code evolution and identify changes not automatically captured. Unlike existing solutions, Covhisto creates a ConceptMap across all versions, offering a comprehensive view of changes over time.

Conclusion: Finally, Covhisto complements existing solutions by making complex code changes more transparent and easier to understand, supporting healthcare professionals and researchers in their analyses and promoting interoperability of healthcare data.

Despite the existence of ICD-O for tumor classification, the broader ICD-10 system is often used in practice. While OncoTree is significant in research and molecular tumor boards, it provides a more detailed classification based on molecular and histological characteristics, crucial for clinical trial enrollment and data comparison. Therefore, a mapping between ICD-10 and OncoTree was developed. The mapping uses SNOMED CT as an intermediary step because both ICD-10 and OncoTree are structured differently. During the mapping process, some challenges arose, such as differences in the structure of the coding systems and inaccurate mappings. Despite this, the approach achieved an accuracy rate of 86.18%, which is considered satisfactory. Future efforts will focus on refining the mapping process to enhance its integration into production systems.

During the 68th annual conference of the German Association for Medical Informatics, Biometry and Informatics, the Working Group Medical Terminologies and Classifications held a terminology server challenge, investigating the boundary between general-purpose FHIR servers and purpose-built FHIR terminology servers. While direct comparisons between the implementations were not the goal of this challenge, it showed that such a boundary exists: General-purpose FHIR servers need to consider many different domains of the FHIR standard, and generally aren’t optimized for the very different terminology use cases.

The mapping from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to the 11th Revision (ICD-11), initiated by the World Health Organization (WHO), presents a challenge for healthcare systems, most of which currently rely on extensive ICD-10 coded data for billing purposes. This paper introduces a methodology to generate a FHIR (Fast Healthcare Interoperability Resources) ConceptMap from the WHO-provided ICD-10 to ICD-11 mapping tables. The resulting ConceptMap allows healthcare organizations to automate the mapping process, facilitating the integration of ICD-11. The final ConceptMap includes ICD-11 mappings for 12,952 ICD-10 codes. This approach prepares healthcare systems for the transition to ICD-11.

Background: The digitalization of health care has increased the demand for efficient data exchange, emphasizing semantic interoperability. SNOMED Clinical Terms (SNOMED CT), a comprehensive terminology with over 360,000 medical concepts, supports this need. However, it cannot cover all medical scenarios, particularly in complex cases. To address this, SNOMED CT allows postcoordination, where users combine precoordinated concepts with new expressions. Despite SNOMED CT’s potential, the creation and validation of postcoordinated expressions (PCEs) remain challenging due to complex syntactic and semantic rules.

Objective: This work aims to develop a tool that validates postcoordinated SNOMED CT expressions, focusing on providing users with detailed, automated correction instructions for syntactic and semantic errors. The goal is not just validation, but also offering user-friendly, actionable suggestions for improving PCEs. Methods: A tool was created using the Fast Healthcare Interoperability Resource (FHIR) service $validate-code and the terminology server Ontoserver to check the correctness of PCEs. When errors are detected, the tool processes the SNOMED CT Concept Model in JSON format and applies predefined error categories. For each error type, specific correction suggestions are generated and displayed to users. The key added value of the tool is in generating specific correction suggestions for each identified error, which are displayed to the users. The tool was integrated into a web application, where users can validate individual PCEs or bulk-upload files. The tool was tested with real existing PCEs, which were used as input and validated. In the event of errors, appropriate error messages were generated as output.

Results: In the validation of 136 PCEs from 304 FHIR Questionnaires, 18 (13.2%) PCEs were invalid, with the most common errors being invalid attribute values. Additionally, 868 OncoTree codes were evaluated, resulting in 161 (20.9%) PCEs containing inactive concepts, which were successfully replaced with valid alternatives. A user survey reflects a favorable evaluation of the tool’s functionality. Participants found the error categorization and correction suggestions to be precise, offering clear guidance for addressing issues. However, there is potential for enhancement, particularly regarding the level of detail in the error messages.

Conclusions: The validation tool significantly improves the accuracy of postcoordinated SNOMED CT expressions by not only identifying errors but also offering detailed correction instructions. This approach supports health care professionals in ensuring that their PCEs are syntactically and semantically valid, enhancing data quality and interoperability across systems.

Background: To ensure interoperability, both structural and semantic standards must be followed. For exchanging medical data between information systems, the structural standard FHIR (Fast Healthcare Interoperability Resources) has recently gained popularity. Regarding semantic interoperability, the reference terminology SNOMED Clinical Terms (SNOMED CT), as a semantic standard, allows for postcoordination, offering advantages over many other vocabularies. These postcoordinated expressions (PCEs) make SNOMED CT an expressive and flexible interlingua, allowing for precise coding of medical facts. However, this comes at the cost of increased complexity, as well as challenges in storage and processing. Additionally, the boundary between semantic (terminology) and structural (information model) standards becomes blurred, leading to what is known as the TermInfo problem. Although often viewed critically, the TermInfo overlap can also be explored for its potential benefits, such as enabling flexible transformation of parts of PCEs.

Objective: In this paper, an alternative solution for storing PCEs is presented, which involves combining them with the FHIR data model. Ultimately, all components of a PCE should be expressible solely through precoordinated concepts that are linked to the appropriate elements of the information model.

Methods: The approach involves storing PCEs decomposed into their components in alignment with FHIR resources. By utilizing the Web Ontology Language (OWL) to generate an OWL ClassExpression, and combining it with an external reasoner and semantic similarity measures, a precoordinated SNOMED CT concept that most accurately describes the PCE is identified as a Superconcept. In addition, the nonmatching attribute relationships between the Superconcept and the PCE are identified as the “Delta.” Once SNOMED CT attributes are manually mapped to FHIR elements, FHIRPath expressions can be defined for both the Superconcept and the Delta, allowing the identified precoordinated codes to be stored within FHIR resources.

Results: A web application called PCEtoFHIR was developed to implement this approach. In a validation process with 600 randomly selected precoordinated concepts, the formal correctness of the generated OWL ClassExpressions was verified. Additionally, 33 PCEs were used for two separate validation tests. Based on these validations, it was demonstrated that a previously proposed semantic similarity calculation is suitable for determining the Superconcept. Additionally, the 33 PCEs were used to confirm the correct functioning of the entire approach. Furthermore, the FHIR StructureMaps were reviewed and deemed meaningful by FHIR experts. Conclusions: PCEtoFHIR offers services to decompose PCEs for storage within FHIR resources. When creating structure mappings for specific subdomains of SNOMED CT concepts (eg, allergies) to desired FHIR profiles, the use of SNOMED CT Expression Templates has proven highly effective. Domain experts can create templates with appropriate mappings, which can then be easily reused in a constrained manner by end users.

The International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) is internationally used for coding diagnoses, with the ICD-10 German Modification (GM) being prescribed for morbidity coding in Germany. ICD-10-GM is subject to annual revisions. This can lead to backward compatibility issues leading to undesirable consequences for cross-version data analysis. A study of annual crosswalk-tables concerning 21 ICD-10-GM versions showed that the ratio of difficult transitions from an older to a newer version (0.89 %) and vice versa (0.48 %) is not particularly significant but should nevertheless not be neglected. In this paper we present two solutions (Neo4J database and FHIR ConceptMaps) for the automated handling of different ICD-10-GM versions.

Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED-CT-coded data but also for flexibly augmenting its 350,000 concepts by allowing a controlled combination of these. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides users through the creation of postcoordinated expressions (PCEs) while ensuring adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of the PCEs created with it.

Around 500,000 oncological diseases are diagnosed in Germany every year which are documented using the International Classification of Diseases for Oncology (ICD-O). Apart from this, another classification for oncology, OncoTree, is often used for the integration of new research findings in oncology. For this purpose, a semi-automatic mapping of ICD-O tuples to OncoTree codes was developed. The implementation uses a FHIR terminology server, pre-coordinated or post-coordinated SNOMED CT expressions, and subsumption testing. Various validations have been applied. The results were compared with reference data of scientific papers and manually evaluated by a senior pathologist, confirming the applicability of SNOMED CT in general and its post-coordinated expressions in particular as a viable intermediate mapping step. Resulting in an agreement of 84,00 percent between the newly developed approach and the manual mapping, it becomes obvious that the present approach has the potential to be used in everyday medical practice.