From Broad Consent to Patient Engagement: A Framework for Consent Management and Study Oversight.

Introduction: The German Medical Informatics Initiative (MII) promotes the use of routine clinical data for research, supported by the broad consent framework to ensure patient engagement. This work proposes a data management process and reference infrastructure to improve transparency by enabling patients to track their consent history and data use in research.

Methods: We analyzed the data provision process at the University Hospital Dresden (UKD) to identify roles and data flows relevant to secondary data use under broad consent.

Systematic Evaluation of Manufacturer Disclosure Statements for Medical Device Security (MDS2) to Strengthen Hospital OT Security Measures - Lessons Learned.

Introduction: The growing number of connected medical devices in hospitals poses serious operational technology (OT) security challenges. Effective countermeasures require a structured analysis of the communication interfaces and security configurations of individual devices.

State Of The Art: Although Manufacturer Disclosure Statements for Medical Device Security (MDS2, Version 2019) offer relevant information, they are rarely integrated into cybersecurity workflows.

Horse owners seeking online health information: a mixed-methods study.

Background: Many people seek health-related information online, not only for themselves but also on behalf of others who cannot articulate their symptoms. This proxy information-seeking behavior is particularly relevant for animal owners, who must interpret their animals' symptoms without direct verbal feedback. While online health information-seeking in the context of one's own health is well-studied, the specific challenges of searching by proxy, especially for animal health information, remain largely unexplored.

User-centered design approach to visualize PROMs for molecular tumor boards.

Integrating Patient-Reported Outcome Measures (PROMs) into Molecular Tumor Boards (MTBs) remains challenging due to the complexity of data visualization and integration into clinical workflows. This work, as part of the German PM4Onco project, aims to identify visualization requirements for PROMs and develop a prototype for PROMs integration in cBioPortal, facilitating broader application within oncology care. We employed a qualitative research approach, including developing personas for MTB stakeholders, conducting a literature-based requirements analysis, organizing a co-design workshop to create low-fidelity prototypes, and evaluating the highest-rated prototype variant through an online survey distributed to MTB physicians across Germany.

Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.

Background: The rapid growth of clinical data, driven by digital technologies and high-resolution sensors, presents significant challenges for health care organizations aiming to support advanced artificial intelligence research and improve patient care. Traditional data management approaches may struggle to handle the large, diverse, and rapidly updating datasets prevalent in modern clinical environments.

Objective: This study aimed to compare 3 clinical data management architectures-clinical data warehouses, clinical data lakes, and clinical data lakehouses-by analyzing their performance using the FAIR (findable, accessible, interoperable, and reusable) principles and the big data 5 V's (volume, variety, velocity, veracity, and value).

Leveraging MDS2 and SBOM data for LLM-assisted vulnerability analysis of medical devices.

This study investigated the use of a semi-automated, Retrieval-Augmented Generation (RAG)-based multi-agent architecture to analyze security-relevant data and assemble specialized exploitation paths targeting medical devices. The input dataset comprised device-specific sources, namely, the Manufacturer Disclosure Statement for Medical Device Security (MDS2) documents and Software Bills of Materials (SBOMs), enriched with public vulnerability databases, including Common Vulnerabilities and Exposures (CVE), Known Exploited Vulnerabilities (KEV), and Metasploit exploit records. The objective was to assess whether a modular, Large Language Model (LLM)-driven agent system could autonomously correlate device metadata with known vulnerabilities and existing exploit information to support structured threat modeling.

From Fragmentation to Integration: Challenges and Solutions for Record Linkage in OMOP CDM.

Secondary use of data for research purposes is becoming increasingly important to improve medical research and thus patient care. In this context, linking different data sources provides a unique opportunity to gain a comprehensive overview of a patient's medical history. The interoperability of different data sets can be ensured by using the standardized data model OMOP.

End-User Participation in Developing an Ophthalmological Dashboard Prototype.

To support treatment of Diabetic Macular Edema (DME), we develop a clinical dashboard prototype for ophthalmologists using a User-Centered Design approach. Results to date include process descriptions, parameter overviews, and derived user requirements, with plans for iterative prototype development to enable enhanced individualized therapy, improved documentation, data generation for research on DME-related biomarkers, and better patient communication.

Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.

: Solid organ transplantation remains a critical life-saving treatment for end-stage organ failure, yet it faces persistent challenges, such as organ scarcity, graft rejection, and postoperative complications. Artificial intelligence (AI) has the potential to address these challenges by revolutionizing transplantation practices. : This review article explores the diverse applications of AI in solid organ transplantation, focusing on its impact on diagnostics, treatment, and the evolving market landscape.

Requirements for an Instrument to Identify Context Factors for Enhancing Context Sensitivity in CDSS.

Background: The acceptance and use of clinical decision support systems are often limited by insufficient contextual adaptation.

Objectives: Identification of barriers to context assessment and requirements for an instrument to capture context factors.

Methods: A questionnaire-based survey investigated requirements for a context assessment instrument.

Standardizing Heat-Related Diagnoses for Predictive Modeling in Healthcare.

Climate change is increasing acute heat events, intensifying health risks and straining healthcare systems. This study aims to support heat-related diagnoses prediction models for Germany by assigning ICD-10-GM codes to relevant conditions identified from the literature. Using the OHDSI mapping tool and clinical validation, 64 heat-related conditions were coded, enhancing data standardization.

Application of Micro Design Patterns in Co-Design Workshops Using PROM Visualizations as an Example.

Background: Co-design workshops can challenge visualization skills.

Objectives: To evaluate how micro design patterns support co-design workshops.

Methods: In a workshop, participants designed low-fidelity prototypes for Patient-Reported Outcome Measures (PROM) visualizations using 12 pre-selected micro design patterns.

Optimizing Clinical Data Enrichment for Intelligent Research.

Enhancing the secondary use of data from routine care through external data enrichment methods can significantly boost its quality. This paper demonstrates a process-driven prototyping approach that separates sensitive and non-sensitive data, empowering medical experts to map medical concepts in free text to standardized terminology codes, all while granting data protection and information security. This approach is based on a prototype-oriented framework developed through discussions in a focus group.

Mapping National Vocabularies to International Standards Using OHDSI Standardized Vocabularies.

Ensuring semantic interoperability in international studies is crucial. In this context, the mapping of national to international vocabularies is necessary. The Standardized Vocabularies of OHDSI provide such a mapping, which forms the basis for semantic interoperability in the standardized data model OMOP CDM.

From Heterogeneity to Uniformity: A Metadata-Driven ETL Process for Transforming FHIR Data into OMOP CDM.

Heterogeneous data formats complicate unified analysis in multisite clinical studies. Standardizing data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) requires Extract-Transform-Load (ETL) processes, which are complex and time-consuming to develop, especially with different source data specifications. The aim of our work is to develop a generalized, metadata-driven ETL process to transform Fast Healthcare Interoperability Resources (FHIR) into OMOP CDM.

Towards mHealth applications for pet animal owners: a comprehensive literature review of requirements.

Background: Veterinarians experience high workloads and stress levels in their daily work, of which they need to be relieved as much as possible. The general public is showing great interest in digital health services. At the same time, animal owners and veterinarians are seeing telehealth services as particularly positive for triage aspects in veterinary medicine.

Context factors in clinical decision-making: a scoping review.

Background: Clinical decision support systems (CDSS) frequently exhibit insufficient contextual adaptation, diminishing user engagement. To enhance the sensitivity of CDSS to contextual conditions, it is crucial first to develop a comprehensive understanding of the context factors influencing the clinical decision-making process. Therefore, this study aims to systematically identify and provide an extensive overview of contextual factors affecting clinical decision-making from the literature, enabling their consideration in the future implementation of CDSS.

Application of Modular Architectures in the Medical Domain - a Scoping Review.

The healthcare sector is notable for its reliance on discrete, self-contained information systems, which are often characterised by the presence of disparate data silos. The growing demands for documentation, quality assurance, and secondary use of medical data for research purposes has underscored the necessity for solutions that are more flexible, straightforward to maintain and interoperable. In this context, modular systems have the potential to act as a catalyst for change, offering the capacity to encapsulate and combine functionalities in an adaptable manner.

Overview of basic design recommendations for user-centered explanation interfaces for AI-based clinical decision support systems: A scoping review.

Objective: The application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users' difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI).

[MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].

Background: Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.

Goal: The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.

From evidence to care delivery: Opportunities and risks in health and science policy. A position paper of the Association of the Scientific Medical Societies in Germany (AWMF).

Objective: After over 25 years of developing clinical practice guidelines, the Association of the Scientific Medical Societies in Germany (AWMF) held a symposium to discuss the following topics in order to improve the way evidence is implemented in the delivery of care: expansion of the data pool for guideline development, the regulatory policy framework for this expansion, the transfer of clinical practice guideline statements to medical practice, the associated opportunities and risks resulting from the European legislation.

Methods: The AWMF held its Berlin Forum on 27 April 2022 where experts from scientific medical societies and national institutions in the healthcare sector reported their experiences and perceptions on the topics mentioned. Three writing groups compiled the key statements from these contributions to and discussions made at the Berlin Forum into a position paper.