Julian Sass

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 Medical Informatics Initiative enables the secondary use of clinical routine data in German university medicine through a federated FHIR-based infrastructure, with implementation guides defining the underlying data representation. Reliable validation of FHIR resources is required to ensure conformance with these specifications. However, validation in federated environments remains challenging due to heterogeneous infrastructures, limited terminology availability, and difficulties in establishing reproducible validation setups. Methods: This technical case report describes the design and implementation of a locally deployable FHIR validation service in the MII context. The system combines the HL7-maintained FHIR Validator with a configurable terminology service and a containerized deployment model. Structural and terminology validation are separated by design. Implementation guides and terminology resources are provisioned in a reproducible manner. The system supports both local terminology deployment and integration with existing institutional terminology services. Results: The proposed solution enables automated conformance testing of FHIR resource instances against MII implementation guides in local and federated environments. The service is provided as a reusable containerized validation component that can be integrated into development and data processing workflows, improving reproducibility, and reducing configuration effort across sites. Discussion: The results demonstrate that reproducible FHIR validation can be achieved through standardized deployment and controlled terminology provisioning. Remaining limitations are primarily related to incomplete or unavailable terminology content. Further work is required to evaluate the approach in production settings across data integration centers and to assess integration into data processing pipelines, including the handling and interpretation of validation results.

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.

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.

While Research Electronic Data Capture (REDCap) is widely adopted in rare disease research, its unconstrained data format often lacks native interoperability with global health standards, limiting secondary use. We developed RareLink , an open-source framework implementing our published ontology-based rare disease common data model. It enables standardised data exchange between REDCap, international registries, and downstream analysis tools by linking Global Alliance for Genomics and Health Phenopackets and Health Level 7 Fast Healthcare Interoperability Resources (FHIR) instances conforming to International Patient Summary and Genomics Reporting profiles. RareLink was developed in three phases across Germany, Canada, South Africa, and Japan for registry and data analysis purposes. We defined a simulated Kabuki syndrome cohort and demonstrated data export to Phenopackets and FHIR. RareLink can enhance the clinical utility of REDCap through its global applicability, supporting equitable rare disease research. Broader adoption and coordination with international entities are thus essential to realise its full potential.

Introduction: The Medical Informatics Initiative (MII) aims to enable cross-site secondary use of clinical data in Germany using a FHIR-based Core Data Set (CDS). However, current FHIR Implementation Guides (IG) often lack actor-specific guidance, leading to inconsistent interpretations and implementations. Methods: This technical case report explores the use of FHIR Implementation Obligations to clarify responsibilities and expected system behavior within the MII infrastructure. Obligations were modeled using the FHIR obligation extension and ActorDefinition resources, applied to the Patient profile from the CDS Person module. A prototype IG was generated using the HL7 FHIR IG publisher tooling. Results: Obligations were defined and rendered for multiple actors – such as Data Integration Centers (DIC) and the Health Research Data Portal (FDPG) – across selected Patient profile elements. Obligations were also linked to specific operations, enabling precise workflow targeting. The implementation improved the explicitness of responsibilities that were previously only implied. Discussion: The study demonstrates that obligations enhance the clarity of FHIR IGs. However, limitations remain: the MII’s current IG tooling does not yet support obligations, and conformance testing was not addressed. Further work is needed to standardize ActorDefinition resources, align obligations across modules, and develop validation tooling to realize the full potential of obligation-driven specifications.

Summary: While Research Electronic Data Capture (REDCap) has been widely adopted in rare disease research, its unconstrained data format often leads to implementations that lack native interoperability with global health data standards, limiting secondary data use. To address this, we developed and validated RareLink , an open-source framework implementing our previously-published ontology-based rare disease common data model, enabling standardised data exchange between REDCap, international registries, and downstream analysis tools. Its preconfigured pipelines interact with the local REDCap application programming interface and enable semi-automatic import or export of data to the Global Alliance for Genomics and Health (GA4GH) Phenopackets and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) instances, conforming to the HL7 International Patient Summary and Genomics Reporting profiles. The framework was developed in three iterative phases using retrospective and prospective clinical data from patients with various rare metabolic and neuromuscular disorders, as well as inborn errors of immunity. Phase one involved deployment across four German university hospitals for registry and data analysis purposes. Phase two integrated RareLink with the Canadian Inborn Errors of Immunity National Registry, enhancing extensibility. Phase three focuses on international implementation in South Africa and Japan to assess global scalability. Implementation feedback was continuously incorporated to validate outputs and improve usability. For evaluation purposes, we defined a simulated Kabuki syndrome cohort based on published cases and demonstrated data export to both Phenopackets and FHIR instances. RareLink can enhance the clinical utility of REDCap by enabling structured data analysis and interoperability. Its global applicability and open-source nature can support equitable rare disease research with the ultimate goal to improve patient care. Broader adoption and coordination with entities such as HL7 and the European Reference Networks are thus essential to realise its full potential. The framework and its documentation are freely available through GitHub and Read the Docs , respectively.

Background: Medication errors represent a widespread, hazardous, and costly challenge in health care settings. The lack of interoperable medication data within and across hospitals not only creates an administrative burden through redundant data entry but also increases the risk of errors due to human mistakes, imprecise data transformations, and misinterpretations. While digital solutions exist, fragmented systems and nonstandardized data hinder effective medication management. Objective: This study aimed to assess medication data available across the multiple systems of a large university hospital, identify a minimum dataset with the most relevant information, and propose a standard interoperable FHIR-based solution that can import and transfer information from a standardized drug master database to various target systems. Methods: Medication data from all relevant departments of a large German hospital were thoroughly analyzed. To ensure interoperability, data elements for developing a minimum dataset were defined based on relevant medication identifiers, the Health Level 7 Fast Health Interoperability Resources (HL7 FHIR) standard, and the German Medical Informatics Initiative (MII) specifications. To enhance medication identification accuracy, the dataset was further enriched with information from Germany’s most comprehensive drug database and European Standard Drug Terms (EDQM) to further enrich medication identification accuracy. Finally, data on 60 frequently used medications in the institution were systematically extracted from multiple medication systems used in the institution and integrated into a new structured, dedicated database. Results: The analysis of all the available medication datasets within the institution identified 7964 drugs. However, limited interoperability was observed due to a fragmented local IT infrastructure and challenges in medication data standardization. Data integrated and available in the new structured medication dataset with key elements to ensure data identification accuracy and interoperability, successfully enabled the generation of medication order messages, ensuring medication interoperability, and standardized data exchange. Conclusions: Our approach addresses the lack of interoperability in medication data and the need for standardized data exchange. We propose a minimum set of data elements aligned with German and international coding systems to be used in combination with the FHIR standard for processes such as the digital transfer of discharge medication prescriptions from intensive care units to general wards, which can help to reduce medication errors and enhance patient safety.

This work presents the Fast Healthcare Interoperability Resources (FHIR®) specification of the NFDI4Health Metadata schema based on FHIR Version 4: We created 16 profiles to facilitate the integration of clinical, epidemiological, and public health study data. Despite challenges arising from the extensive MDS as well as missing concepts in semantic standards, it marks a significant advance in applying information technology standards to health research.

BACKGROUND Medication errors represent a widespread, hazardous, and costly challenge in health care settings. The lack of interoperable medication data within and across hospitals not only creates an administrative burden through redundant data entry but also increases the risk of errors due to human mistakes, imprecise data transformations, and misinterpretations. While digital solutions exist, fragmented systems and nonstandardized data hinder effective medication management. OBJECTIVE This study aimed to assess medication data available across the multiple systems of a large university hospital, identify a minimum dataset with the most relevant information, and propose a standard interoperable FHIR-based solution that can import and transfer information from a standardized drug master database to various target systems. METHODS Medication data from all relevant departments of a large German hospital were thoroughly analyzed. To ensure interoperability, data elements for developing a minimum dataset were defined based on relevant medication identifiers, the Health Level 7 Fast Health Interoperability Resources (HL7 FHIR) standard, and the German Medical Informatics Initiative (MII) specifications. To enhance medication identification accuracy, the dataset was further enriched with information from Germany’s most comprehensive drug database and European Standard Drug Terms (EDQM) to further enrich medication identification accuracy. Finally, data on 60 frequently used medications in the institution were systematically extracted from multiple medication systems used in the institution and integrated into a new structured, dedicated database. RESULTS The analysis of all the available medication datasets within the institution identified 7964 drugs. However, limited interoperability was observed due to a fragmented local IT infrastructure and challenges in medication data standardization. Data integrated and available in the new structured medication dataset with key elements to ensure data identification accuracy and interoperability, successfully enabled the generation of medication order messages, ensuring medication interoperability, and standardized data exchange. CONCLUSIONS Our approach addresses the lack of interoperability in medication data and the need for standardized data exchange. We propose a minimum set of data elements aligned with German and international coding systems to be used in combination with the FHIR standard for processes such as the digital transfer of discharge medication prescriptions from intensive care units to general wards, which can help to reduce medication errors and enhance patient safety. CLINICALTRIAL