Publications by authors named "B Schreiweis"

Background: The national health record (ePA) was introduced January 1, 2021 in Germany and is available to every person insured under statutory health insurance. This study investigated the acceptance and expectations of the national health record in Germany, focusing on consumer perspectives, expectations, barriers, information needs, and communication aspects.

Methods: An exploratory, observational, cross-sectional online survey was conducted one year after ePA introduction, followed by descriptive statistical analysis.

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The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise.

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Introduction: Conducting clinical studies is an integral part of the clinical research repertoire of university hospitals. A wealth of organizational competences must always be available in a central location and in an up-to-date form for appropriate administration. Information such as the number of ongoing studies, and the number of enrolled participants is required for tasks related to e.

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This comparative study examines the transition from isolated registries to a consolidated data-centric approach at University Hospital Schleswig-Holstein, focusing on migrating the Atrioventricular Valve Intervention Registry (AVIR) from REDCap to a Medical Data Integration Center based openEHR registry. Through qualitative analysis, we identify key disparities and strategic decisions guiding this transition. While REDCap has historical utility, its limitations in automated data integration and traceability highlight the advantages of a data-centric approach, which include streamlined data (integration) management at a single-point-of-truth based on e.

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To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework.

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