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The increasingly digitized healthcare system requires new skills from all those involved. In order to impart these competencies, appropriate courses must be developed at educational institutions. In view of the rapid development of new aspects of digitization, this presents a challenge; suitable teaching formats must be developed successively. The establishment of cross-location teaching networks is one way to better meet training needs and to make the necessary spectrum of educational content available. As part of the Medical Informatics Initiative, the HiGHmed consortium is establishing such a teaching network, in the field of medical informatics, which covers many topics related to the digitization of the health care system. Various problem areas in the German education system were identified that hinder the development of the teaching network. These problem areas were prioritized firstly according to the urgency of the solution from the point of view of the HiGHmed consortium and secondly according to existing competencies in the participating societies. A workshop on the four most relevant topics was organized with experts from the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Society for Medical Education (GMA) and the HiGHmed consortium. These are:For all subject areas, existing preliminary work was identified on the basis of working questions, and short- and long-term needs for action were formulated. Finally, a need for the redesign of a technologically supported syntactic and semantic interoperability of learning performance recording was identified.

[english] Objective: In 1998 an international contest for medical students called “Benjamin Franklin Contest” was initiated by the former university hospital of Freie Universität Berlin on the occasion of the 30th anniversary of its foundation in 1968. Since then, seven teams from Germany, Austria, and Switzerland have been competing against each other every year. Five medical students per team match up in paper cases, diagnosis of medical images, multiple choice tests, and practical skills.Methods: All participants of the 11th Benjamin Franklin Contest (2009) were asked about their motivation, preparation strategy, and experience with the contest. Results: In total 92% (32 of 35) participated in the evaluation study. We show that improvement of medical knowledge is one of the key motivating factors behind participation in the competition. The teams were trained by self-study, tutorials, and learning groups. Remarkably, students invested 54.2 hours (SD 32 hours) of their free time to prepare for the contest. Conclusions: The Benjamin Franklin Contest is an innovative approach to facilitate self-directed learning and develope medical competencies. [german] Zielsetzung: Im Jahr 1998 wurde am damaligen Universitätsklinikum Benjamin Franklin der Freien Universität (FU) in Berlin der internationale Medizinstudierendenwettbewerb „Benjamin Franklin Contest“ (BFC) initiiert. Anlass war damals der 30. Jahrestag der Gründung der Universitätsmedizin an der FU. Seitdem findet jedes Jahr ein Wettbewerb zwischen sieben Teams von medizinischen Fakultäten aus Deutschland, Österreich und der Schweiz statt. Jeweils fünf Studierende messen sich in den Kategorien Papierfälle, Blickdiagnosen, Auswahlfragen und praktische Fähigkeiten.Methodik: Ziel dieser Arbeit ist die Beschreibung des Projektes BFC und eine erste Evaluation der Erfahrung von Teilnehmenden. Dazu wurden alle Teilnehmenden des 11. BFC (2009) mittels eines selbst entwickelten Fragebogens hinsichtlich ihrer Motivation, Art und Aufwand der eigenen Vorbereitung und der Erfahrung mit dem Wettbewerb befragt. Ergebnisse: Insgesamt beteiligten sich 92% (32/35) der Teilnehmenden im Jahr 2009 an der Evaluation des Wettbewerbs. Es zeigte sich, dass die Erweiterung medizinischen Wissens zu den Hauptmotiven für eine Teilnahme am BFC gehört. Zur Vorbereitung auf den Wettbewerb wurden neben dem Selbststudium insbesondere Tutorien und Lerngruppen eingesetzt. Der Aufwand lag bei durchschnittlich 54,2 Stunden (Standardabweichung 32 Stunden). Schlussfolgerungen: Das Konzept des BFC kann als innovativer Ansatz selbstmotivierten Lernens verstanden werden. Die Teilnahme am BFC hat das Potential, neben medizinischen Kompetenzen auch die Fähigkeit zur Teamarbeit zu fördern.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository, which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation. [ABSTRACT FROM AUTHOR]

Established cardiovascular risk scores are typically based on items from structured clinical data such as age, sex, or smoking status. Cardiovascular risk is also assessed from physiological measurements such as electrocardiography (ECG). Although ECGs are standard diagnostic tools in clinical care, they are scarcely integrated into clinical information systems. To overcome this roadblock, we propose the integration of an automatic workflow for ECG processing using the DICOMweb interface to transfer ECGs in a standardised way. We implemented the workflow using non-commercial software and tested it with about 150,000 resting ECGs acquired in a maximum-care hospital. We employed Orthanc as DICOM server and AcuWave as signal processing application and implemented a fullyautomated workflow which reads the ECG data and computes heart rate-related parameters. The workflow is evaluated on off-the-shelf hardware and results in an average run time of approximately 40 ms for processing a single ECG. [ABSTRACT FROM AUTHOR]

Secondary use of health data has become an emerging topic in medical informatics. Many initiatives focus on clinical routine data, but clinical trial data has complementary strengths regarding highly structured documentation and mandatory data quality (DQ) reviews during the implementation. Clinical imaging trials investigate new imaging methods and procedures. Recently, DQ frameworks for structured data were proposed for harmonized quality assessments (QA). In this article, we investigate the application of these concepts to imaging trials and how a DQ framework could be defined for secondary use scenarios. We conclude that image quality can be assessed through both pixel data and metadata, and the latter can mostly be handled like structured study documentation in QA. For pixel data, typical quality indicators can be mapped to existing frameworks, but require additional image processing. Specific attention needs to be drawn to complete de-identification of imaging data, both on pixel data and metadata level. [ABSTRACT FROM AUTHOR]