ZDIN-Zahlen im Detail

To develop a decision support system for pediatric cardiology case conferences, the anonymization of 4,000 freetext medical case reports is required. This paper presents an anonymization strategy using LLM-AIx, a tool for structured information extraction based on large language models (LLM). The three-step process involves automatic extraction of personally identifiable information (PII) from the reports, evaluation of the results against a manually annotated ground truth, and replacement of identified PII with surrogate values, including controlled date shifting. Initial tests with six example reports revealed challenges regarding handling multiple attribute occurrences and consistent replacements. Future work will focus on full pipeline implementation and mapping clinical information to standardized terminologies such as SNOMED CT.

Die Nutzung elektronischer Patientenakten (ePA) zur Datenspende bietet großes Potenzial für die medizinische Forschung, stellt jedoch hohe Anforderungen an Information, Kommunikation und Akzeptanz in der Bevölkerung. Im Rahmen eines interdisziplinären Workshops anlässlich der 69. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. diskutierten in Dresden Expert:innen aus Medizinischer Informatik, Wissenschaftskommunikation und Patientenvertretung gemeinsam mit dem Publikum zentrale Fragestellungen zur Kommunikation der Datenspende an Bürger:innen. 

Ziel war es, Bedingungen zu identifizieren, unter denen eine informierte und selbstbestimmte Entscheidung über die freiwillige Nutzung von Gesundheitsdaten für Forschungszwecke möglich ist. Im Fokus standen dabei die Fragen, wer über die Datenspende informiert, wie Vertrauen in Institutionen gestärkt, Nutzen und Risiken verständlich vermittelt und Zielgruppen angemessen erreicht werden können. Impulsvorträge und Diskussionen zeigten, dass es einer Vielzahl niedrigschwelliger, transparenter und zielgruppengerechter Informationsangebote durch glaubwürdige Akteure bedarf. Dabei gilt es, digitale Gesundheitskompetenz zu fördern, Zugangshürden abzubauen und Bürger:innen nicht nur technisch, sondern auch kommunikativ zu befähigen. 

Der Beitrag verdeutlicht, dass eine wirksame Kommunikation zur Datenspende eine zentrale Voraussetzung für gesellschaftliche Akzeptanz und eine gerechte Teilhabe an digitaler Gesundheitsforschung darstellt.

The 24th Special Topic Conference (STC 2025) of the European Federation for Medical Informatics (EFMI) was held in Osnabrück, Germany from 20–22 October 2025. The Conference was co-hosted by Osnabrück University of Applied Sciences, the German Society of Medical Informatics, Biometry and Epidemiology (GMDS) and EFMI.
The theme of STC 2025 was “Good Evaluation – Better Digital Health”. In an era of rapid digital transformation, the conference highlighted that rigorous evaluation is essential to ensure the safety, effectiveness and sustainability of health IT innovations. While evaluation is often carried out at the very end of the development process, good evaluation starts at the outset of a project with an investigation of the context within which the implementation will take place and its determinants. In the best case, the evaluation will follow a longitudinal approach to capture the full impact of the implementation regardless of whether it is positive or negative. With the advent of powerful digital systems, including those making use of AI technology, evaluation is needed more than ever to prevent harm and ensure effectiveness and meaningfulness on a micro, meso and macro level. As digital interventions have multiple determinants and implications, evaluation methods have to consider the full spectrum of dimensions, ranging from technical and organisational aspects to human, social, ethical and
educational perspectives.
These proceedings present current scientific trends in the evaluation of digital health and AI-enabled systems in patient care. Some contributions address methodological topics such as evaluation paradigms and techniques, research models and frameworks that constitute the foundation of reliable and valid results. Others point to critical areas of investigation such as technical quality, including interoperability, as well as data protection and security. Non-technical areas of paramount importance, first and foremost ethics, are equally addressed. Many papers take the perspective of human and organisational factors combined with social and political aspects in their dual role as potential determinants and outcomes. Finally, educational aspects are covered by a number of papers addressing competencies and tools for enhancing education in the era of AI.
While these topics have been scientifically investigated by biomedical and health informatics for many decades, the latest AI developments impose a new imperative on the evaluation of digital health. These proceedings reflect the current debates and developments from a scientific point of view with the goal of having an impact on practice.
The proceedings are published by IOS Press in the series Studies in Health Technology and Informatics. Volumes in this series are submitted (for evaluation) for indexing by MEDLINE/PubMed; Web of Science: Conference Proceedings Citation Index – Science (CPCI-S) and Book Citation Index – Science (BKCI-S); Google Scholar; Scopus; and EMCare.

The implementation of evidence-based practice continues to pose significant challenges across healthcare settings. This study investigated how factors influencing the use of an intranet folder system (IFS) providing evidence-based guidelines change during the transition to a Clinical Decision Support System (CDSS). A two-wave survey was conducted among nursing staff at a Swiss hospital, assessing system utilisation, Evidence-Based Nursing (EBN) related factors, selected variables from the Unified Theory of Acceptance and Use of Technology (UTAUT), and demographic information. Structural equation modelling was applied to identify key determinants of utilisation. Facilitating Conditions and Social Influence significantly influenced utilisation in both systems. EBN factors, specifically Trust and Knowledge, impacted Effort Expectancy only in the CDSS model. These results suggest that while organisational and social factors consistently promote system use, the successful adoption of a CDSS additionally requires the development of new trust in the technology and expanded knowledge of its functions to reduce perceived effort and enhance utilisation. The findings highlight the need for structural support, social endorsement, and targeted training to facilitate the effective implementation of CDSS in clinical practice.

KI zur Extraktion präanalytischer Variabilitätsdaten aus biomedizinischer Literatur: Machbarkeit und Validierung