Matthias Löbe

Aiming to develop strategies and infrastructure for close collaboration between healthcare and research, the Medical Informatics Initiative (MII) established a common infrastructure connecting German university hospitals and their partners. To foster interoperability across the heterogeneous sites, a health data specification was created, the MII Core Data Set (CDS), standardising the representation of clinical routine data intended for reuse in research. Findable, accessible, interoperable and reusable (FAIR) specifications are applied more efficiently. Exemplified by MII CDS modules, we demonstrate how health data specifications can be made FAIR and discuss benefits and costs involved. As the CDS defines how clinical routine data is exchanged in Germany, we implemented a multi-step FAIRifciation workflow and extended the MII CDS module implementation guide accordingly. We summarised lessons learned in a guideline and propose to reduce the number of FAIR indicators when assessing health data specifications. In summary, our results ease future FAIRification of MII CDS modules and similar health data specifications, thus contributing to the generation of FAIR health research data from those specifications.

Introduction: The European Health Data Space (EHDS) Regulation (EU) 2025/327 requires Member States to establish national health dataset catalogs feeding into the HealthData@EU infrastructure. While the detailed technical specifications for catalog content are currently being developed through HealthDCAT-AP and forthcoming implementing acts, several national and domain-specific health data infrastructures have already established metadata catalogs based on (Health)DCAT-AP and related profiles. These early implementations provide a valuable opportunity to assess emerging convergence, metadata alignment challenges, and identify practical requirements for future harmonization. Objectives: In this paper, we compare a set of independently developed metadata catalog schemas in use across European health data infrastructures to identify areas of structural and semantic convergence and divergence. This aims to provide a basis for alignment consistent with the emerging HealthDCAT-AP specification. Methods: This work was initiated at the SWAT4HCLS 2026 BioHackathon, where representatives of the participating infrastructures jointly manually mapped their schemas, and continued through extended collaborative efforts. A total of eleven schemas, health-related metadata catalogs and application profiles, were compared along three axes: classes, properties and value sets. Results: The study shows that the schemas align well with DCAT-AP, and especially requirements of the HealthDCAT-AP for the health-specific schemas, though there are some divergences and expected extensions. For aligned content, we illustrate the practical payoff of these alignments with federated SPARQL queries enabled at each level. Conclusions: The national metadata schemas provide a solid foundation towards enabling interoperable metadata discovery, while complying with the EHDS regulation. The necessary alignments and suggestions for extensions of studied schemas have been outlined, communicated and are being discussed on the national level. Beyond the technical findings, this work established a cross-community forum for alignment, knowledge exchange, and coordinated evolution of metadata specifications. Maintaining such collaboration will be essential as HealthDCAT-AP and related EHDS requirements continue to mature.

Finding, sharing, and reusing data requires common interoperable vocabularies to adequately describe machine-actionable data policies. Semantic Web standards are a proven way to express such information describing data access and reuse. Narrative descriptions get a stable and agreed meaning by formal definitions of terms that have a globally unique identifier. The Data Privacy Vocabulary (DPV) represents a community standard for privacy, data protection, and related topics. DPV uses modular namespaces to define jurisdictions, such as “EU”, and to represent concepts from laws within that jurisdiction, such as the General Data Protection Regulation (GDPR) and the European Health Data Space (EHDS). In this work, we present the creation of a DPV extension representing the German Health Data Use Act (Gesundheitsdatennutzungsgesetz (GDNG)) that has been set into force in March 2024. The GDNG regulates sharing and using health data for secondary use, such as research purposes and policymaking. It builds the German legal foundation for the future EHDS as the European initiative and regulation providing rules for sharing of health data within Europe. The parallel development of the EHDS and GDNG extensions resulted in many shared concepts, which contributed to an overarching extension: the DPV sector health extension. It sets the foundation of aligned privacy terms for future EHDS member states looking to build a national vocabulary aligned with the EHDS. All extensions were released as draft extensions with the latest DPV version 2.3, released 25th of February 2026.

In the German Medical Informatics Initiative (MII), Medical Data Integration Centers (MeDICs) make routine care data from German university hospitals available to researchers. The MII common data model is the Core Data Set (CDS) built on HL7 FHIR. Extensive effort is put into providing FHIR profiles with corresponding Implementation Guides (IGs). While the technical specification of IGs is centrally governed, implementation is distributed among all MeDICs. Each MeDICs is to build compatible processes within the diverse landscape of Electronic Health Record (EHR) systems in Germany. Therefore, the characteristics of FHIR resources between MeDICs might differ. Different constraints in underlying processes explain this, yet the central CDS specification needs transparent ways to capture the differences. MeDICs spend considerable effort on localized Data Quality (DQ) checks, like source data validation or plausibility analyses. Local checks do not enable the central CDS specification working group to assess the degree of harmonization of CDS implementation reached between different MeDICs. We propose a FHIR native solution that enables reviewing the harmonization of such a fragmented infrastructure. HL7 Clinical Quality Language (CQL) is utilized to generate DQ Summary Reports (DQ-SRs). We further developed the FHIRwood dashboard that enables MeDICs to also review locally relevant DQ aspects. The lessons learned during the DQ-SR and FHIRwood application at one MeDIC give insights for future IG development.

Background: To be beneficial for empirical health research, a dataset must be fit for use. The quality of a dataset can only be influenced during data collection, yet it is evaluated multiple times during analysis or secondary use by applying quality indicators. Objective: This study aimed to establish an up-to-date set of indicators measuring the quality of datasets in empirical health research. Methods: A total of 3 pillars were combined. First, the 51 indicators of a German guideline from 2014 about the management of data quality were revised. Second, a literature review was performed looking for evidence sources since 2013 that describe, propose, or apply dataset quality indicators. Third, indicators were supplemented by a manual search and other sources. The quality indicators were then integrated into the IDEFIM framework. The IDEFIM framework distinguishes between the categories’ data, metadata, context, and openness quality. In this work, only the categories data and metadata quality, with their 14 dimensions were considered. Results: In total, 69 indicators qualified for the IDEFIM indicator set, 53 related to the category data quality, and 16 to the category metadata quality. A total of 30 indicators originated from the German guideline, 31 from the literature review. Three indicators were added to cover aspects of diversity, equity, and inclusion, and an additional 5 related to specifics of data and metadata quality not addressed so far. Most indicators were found in the dimensions accuracy (data) with 12 measures, completeness (data) with 12 measures, and consistency (data) with 19 measures. According to the number of supporting evidence sources, missing values in data elements (48 evidence sources), contradictions (31), and currentness (26) were the most popular quality indicators. Metadata quality was significantly less frequently addressed. Conclusions: The presented IDEFIM indicator set can be used for the management of data collections as well as for the verification of a dataset’s quality for an intended use. The indicator set should also be considered in the design of a study in empirical health research and the development of software tools supporting the visualization of issues related to the quality of a dataset.

BACKGROUND Generating Findable, Accessible, Interoperable, and Reusable (FAIR) biomedical samples, data, and tools is costly and time-consuming. Thus, transparency about their processing or evolution and reuse, particularly of health data, are highly desirable. Therefore, an appropriate fact-based decision framework to evaluate data (re)usability is required. Provenance information documents the processing or evolution of a data object, thereby providing an essential formal basis for such a (re)usability evaluation. Standardised, this provenance information facilitates better FAIR biomedical data. OBJECTIVE The MInimal Requirements for Automated Provenance Information Enrichment (MIRAPIE) project aims at defining the minimal required provenance information for harmonised documentation of a data objects processing history and to establish the MIRAPIE approach as a community standard to assure interoperability of the collected provenance information. METHODS A hybrid consensus finding method, adjusted from Nominal Group Technique (NGT) and Delphi, has been applied within an international community setting to iteratively implement a minimal data model, an ontology, and an application guideline. The data model is based on the PROV Data Model (PROV-DM), the ontology expands the PROV Ontology (PROV-O). RESULTS With the MIRAPIE question, we defined a harmonising framework for provenance information in biomedicine and presumably beyond. The minimal data model, a respective ontology, and an accompanying guideline facilitate means for standardised and possibly automated provenance documentation. In diverse biomedical usage scenarios their general applicability to data, workflows, models, and even samples is shown. Setting up provenance documentation from scratch is equally supported as linking alternative data schemata and mapping existing provenance documentation. CONCLUSIONS MIRAPIE question, minimal data model, ontology, and guideline together significantly contribute to the advancement of biomedical and especially health research, setting up a basis for a contextual (re)usability evaluation. This fosters traceability of changes applied to data, workflows, tools, and samples and, in consequence, sustainable data usage and reproducibility of scientific results. The generalisation allows to overcome domain-specific differences and local, national, and international boundaries. We invite biomedical research community and health data gathering institutions to create lasting change by establishing MIRAPIE-compliant provenance information for transparent data processing and (re)usability assessment.

The European Health Data Space (EHDS) regulation sets high requirements on the reporting of datasets’ quality. The regulation’s wording used for categories and for the definition of elements seems unfamiliar at first glance. This gives rise to the fears of negative side effects for empirical health research. A comparison between the QUANTUM proposal of a datasets’ quality and utility label with the IDEFIM framework, ISO/IEC 25012 and the FAIR Guiding Principles should therefore evaluate common and distinguishing features. On the one hand, the comparison revealed relevant discrepancies on how to organize requirements and how to evaluate their fulfilment. On the other hand, the QUANTUM project offers a bridge from the confusing approach of the EHDS regulation to a broad consent about dimensions of datasets found in ISO/IEC 25012 and the IDEFIM framework. It is of utmost importance for empirical health research that the further elaboration of the respective EHDS regulation strives at the best possible convergence with established standards and quasi-standards including the FAIR Guiding Principles.

Semantic annotation of study metadata elements with concept codes from medical terminologies is essential in order to make biomedical data FAIR (Findable, Accessible, Interoperable, and Reusable) and to enable cross-border data exchange as envisioned within the forthcoming European Health Data Space (EHDS). Existing annotation tools are often detached from everyday data-capture workflows, limiting their practical uptake by researchers and data managers. REDCap Ontology annotations Made Easy (ROME) addresses this gap by embedding ontology-based annotation directly into REDCap’s Online Designer. REDCap is a widely utilized electronic data collection framework in clinical and epidemiological research. As an external REDCap module, ROME allows users to import minimal datasets, search large ontologies such as SNOMED CT, map answer options, and harmonize projects through reusable semantic mappings. Metadata and annotations are stored in structured JSON to ensure machine-readability and interoperability. Future developments include dedicated interfaces for major ontologies, best-practice frameworks for managing minimal datasets, and community-driven harmonization efforts. ROME thus represents a concrete step towards bridging routine data capture and FAIR, EHDS-aligned semantic data stewardship by embedding ontology annotation directly into the design workflow.

The data quality terminology framework provided by Kahn et al. differentiates between an internal Verification and an external Validation context. While solutions within the Verification context arise, multi-site networks such as the German MII or the European EHDS drive demand for solutions that address Validation. Our work explores if standardized data quality reports can be specified as HL7 FHIR resources to address this need. We define constraints that reports utilized in this context need to fulfil. We refer to such reports as data quality summary reports, DQ-SR in short. We then provide an example for DQ-SRs based on the German census data for age and gender distributions and a prototypical implementation of these DQ-SRs as FHIR R4 MeasureReports. While these example DQ-SRs are not backed by a medical use case, we hope that the comprehensive specification provided as an Implementation Guide can inspire further standardizations of data quality reports.

The broad registration of research studies in international registries would give the research community a good oversight of ongoing projects, but would also serve the information needs of study participants. However, there is currently a lack of widely accepted transparency registers as well as harmonized interfaces for populating and managing them. The German Medical Informatics Initiative (MII) makes healthcare data from German university hospitals available for a variety of research purposes. As a complement to the MII core dataset, an extension module for representing research studies was specified and implemented in HL7 FHIR. This allows both local research projects at the sites and cross-site projects to be represented. One focus was on modeling the criteria for the inclusion or exclusion of subjects and the connection of patients to study participants. This initial version will be supplemented with additional functionalities in the future to enable a semi-automatic synchronization with transparency registers such as the German Portal for Medical Research Data (FDPG).