INTRODUCTION:Medical services routinely transmit patient data using PDF, even as FHIR emerges as the standard for structured healthcare interoperability. This mismatch reflects a broader fragmentation in digital documentation, where pragmatic workflows often outpace technical ideals. METHODS:We propose embedding FHIR bundles into PDF to enable structured data reuse without disrupting established processes. These hybrid documents can be processed via FHIR Binary endpoints, allowing downstream systems to extract, validate, and map the embedded data to interoperable resources. RESULTS:A proof-of-concept using German emergency medical services records demonstrates that vital parameters and timestamps can be transmitted as PDF while preserving machine-readable structure. CONCLUSION:This approach respects existing transport mechanisms and accommodates heterogeneous IT landscapes. By bridging legacy formats with modern standards, the method offers a scalable migration path toward interoperability-ready to deploy wherever PDF are already in use. Thus, our approach provides a migration pathway for integrating routine data into interoperable research infrastructures, enabling structured reuse without altering clinical workflows.
INTRODUCTION:In Germany, there is growing interest in linking clinical data from routine care available in data integration centres (DIC) with external data, such as medical registries. However, a suitable technical and organisational infrastructure is required for the secondary use and linkage of registry data. This paper presents the concept of a framework for the integration of routine data and registry data using the structures of a DIC. METHODS:The development of the framework followed a step-by-step approach: (1) literature research, (2) derivation of the theoretical foundations of the framework, and (3) design and modelling of the framework. RESULTS:Challenges for data linkage and matching solutions from initiatives such as the Medical Informatics Initiative (MII) or Network of University Medicine (NUM) were identified to create the theoretical basis of the framework. The initial design of the framework on increasingly detailed hierarchical levels includes functional components, processes and support units of a DIC to fulfil specific use cases from registry setup to data linkage. DISCUSSION:The framework was developed to serve as a blueprint for the setup of a DIC as a bidirectional link between registries and routine care. At this stage it represents a theoretical concept as the result of formative research which remains to be systematically evaluated, further specified and tested in pilot projects. Despite the challenges, both registries and DIC could benefit from a future practical implementation of the framework.
INTRODUCTION:Entering the content of manually filled documents in the database of an electronic clinical Trial Master File (eTMF) is a tedious and time-consuming task. METHODS:We report experiments for automatic transcription with an optical document recognition pipeline and a web-based (global) and a local multimodal large language model (LLM). RESULTS:Different approaches are best suited for different column types of the table-based documents. CONCLUSIONS:Drastic time savings compared to manual transcription are possible.
INTRODUCTION:Removal of identifying information from data used in clinical studies protects patient privacy and maintains confidentiality. It ensures compliance with laws like data protection regulations, which safeguard personal data. Medical imaging data may contain various sensitive personal data. An image is composed of pixel data and structured metadata. The de-facto standard for medical imaging, the Digital Imaging and Communication in Medicine (DICOM) format, defines more than 5,000 unique DICOM metadata items, including so-called private tags that can contain arbitrary information. Therefore, de-identification of DICOM objects may become a notable challenge, even without consideration of identifying information in pixel data such as burnt-in identifiers or visible series numbers of implants. To decide which tool best to use for defined requirements, this paper aims to define a workflow for comparative evaluations of DICOM de-identification tools. METHODS:We assessed requirements and performance indicators for DICOM de-identification tools. We employ open data and open-source tools to assess the different requirements in a comparable way, and apply the workflow on the widely used java-based de-identification tool Clinical Trials Processor (CTP) and the current state of a recently developed python-based anonymisation tool, both provided by the Radiological Society of North America (RSNA). RESULTS:The proposed workflow proved helpful in systematically comparing de-identification tools and highlighting key differences in functionality and use case. The comparison of CTP and RSNA Anonymizer (RDA) de-identification tools reveals that both tools effectively remove identifying information from datasets, but with distinct differences in their approaches and compliance with the DICOM standard. CONCLUSION:The evaluation of two DICOM de-identification tools using open data and methods revealed decisive differences in functionality and practical suitability, highlighting the value of a structured comparison. De-identification tools need to be selected based on specific use cases, especially given ongoing challenges with metadata and image-based identifiers.
INTRODUCTION:The lawful processing of health data in medical research necessitates robust mechanisms for managing patient consent and objections, aligning with national and european regulations. While the initial version of the HL7 standard Consent Management" primarily focused on opt-in scenarios, evolving legal landscapes and practical implementation challenges highlight the need for comprehensive solutions encompassing both opt-in and opt-out approaches, including withdrawals and objections. This paper details the systematic revision of the latest HL7 FHIR-based "Consent Management 2.0" standard to address these limitations. METHODS:Our methodology involved a critical assessment of the 2021 standard against three years of practical experience and emerging regulatory requirements. RESULTS:Key improvements include enhanced support for diverse document types (consent, withdrawal, refusal, objection), refined technical specifications for automated conversion of questionnaire responses into machine-readable Consent Resources, and the introduction of a novel "ResultType" category. This new category enables use-case-specific aggregation of consent information, simplifying downstream processing and reducing interpretation ambiguities. Additionally, uniform FHIR search parameters were defined, and comprehensive examples were integrated into the implementation guide. The revised standard successfully underwent the HL7 ballot process in April 2025, with early practical implementations already demonstrating its utility. CONCLUSION:This extended standard significantly enhances the interoperability and legal robustness of consent management in complex research infrastructures, fostering improved patient autonomy and trust in digital health data reuse.
