Search

[english] Objective: Present-day students have grown up with considerable knowledge concerning multi-media. The communication modes they use are faster, more spontaneous, and independent of place and time. These new web-based forms of information and communication are used by students, educators, and patients in various ways. Universities which have already used these tools report many positive effects on the learning behaviour of the students. In a systematic literature review, we summarized the manner in which the integration of Social Media and Web 2.0 into education has taken place. Method: A systematic literature search covering the last 5 years using MeSH terms was carried out via PubMed.Result: Among the 20 chosen publications, there was only one German publication. Most of the publications are from the US and Great Britain. The latest publications report on the concrete usage of the tools in education, including social networking, podcasts, blogs, wikis, YouTube, Twitter and Skype.Conclusion: The integration of Web 2.0 and Social Media is the modern form of self-determined learning. It stimulates reflection and actively integrates the students in the construction of their knowledge. With these new tools, the students acquire skills which they need in both their social and professional lives. [german] Zielsetzung: Die Studierenden sind mit einem hohen multimedialen Bezug aufgewachsen. Die von ihnen genutzten Kommunikationswege sind schneller, spontaner und unabhängig von Ort und Zeit geworden. Diese neuen webbasierten Informations- und Kommunikationswege werden von Studierenden, Lehrenden und Patienten in vielfältigen Weisen genutzt. Universitäten, die diese Tools in der Lehre einsetzten, berichten über viele positive Auswirkungen auf das Lernverhalten der Studierenden. In einer systematischen Literaturübersicht wird zusammengestellt, für welche Lehr- und Lernformen Social Media und Web 2.0 Tools in der derzeitigen medizinischen Ausbildung eingesetzt werden.Methode: Es wurde eine systematische Literaturrecherche über die letzten 5 Jahre mit MeSH in PubMed durchgeführt. Ergebnis: Unter den 20 identifizierten Publikationen konnte nur ein deutscher Artikel identifiziert werden. Mehrheitlich stammen die Artikel aus USA und England. Neuere Veröffentlichungen befassen sich mit dem konkreten Einsatz der Tools in der Lehre. Hierzu zählen Social Networking, Podcasts, Blogs, Wikis, YouTube, Twitter und Skype.Schlussfolgerung: Die Einbindung von Web 2.0 und Social Media stellt die heutige Form des selbstbestimmten Lernens dar. Es stimuliert die Reflektion und bindet die Lernenden aktiv, ein Wissen zu konstruieren. Mit diesen neuen Tools lernen Studierende Fertigkeiten, die sie sowohl im sozialen als auch im beruflichen Kontext benötigen.

The department of Medical Informatics of the University Hospital Münster and the Gesakon GmbH (an university offspring) initiated the cooperative development of an electronic health record (EHR) called "akteonline.de" in 2000. From 2001 onwards several clinics of the university hospital have already offered this EHR (within pilot projects) as an additional service to selected subsets of their patients. Based on the experiences of those pilot projects the system architecture and the basic data model underwent several evolutionary enhancements, e.g. implementations of electronic interfaces to other clinical systems (considering for example data interchange methods like the Clinical Document Architecture - standardized within the HL7 group - and also interfacing architectures of German GP systems, such as VCS and D2D). "akteonline.de" in its current structure supports patients as well as health care professionals and aims at providing a collaborative health information system which perfectly supports the clinical workflow even across institutional boundaries and including the patient himself. Since such an EHR needs to strictly fulfill high data security and data protection requirements, a complex authorization and access control component has been included. Furthermore the EHR data are encrypted within the database itself and during their transfer across the internet.

This work introduces a novel approach to facilitate clinical research on secondary clinical data by integrating an LLM-based chatbot within a specialized platform called data hotel. The platform is designed to empower clinical researchers within our institution by enabling the generation of research hypotheses from secondary use patient data sources. Our focus in this work is on the deployment and functionality of the LLM-based chatbot within the data hotel ecosystem. The aim is to aid medical experts in visualizing and analyzing data sourced from the platform but also to enable the seamless storage of the generated code, enhancing the efficiency and reproducibility of the research process. This integration represents a significant advancement in leveraging LLM capabilities to enhance the utility and accessibility of clinical research platforms. [ABSTRACT FROM AUTHOR]

The standardization of data from medical studies and hospital information systems to a common data model such as the Observational Medical Outcomes Partnership (OMOP) model can help make large datasets available for analysis using artificial intelligence approaches. Commonly, automatic mapping without intervention from domain experts delivers poor results. Further challenges arise from the need for translation of non-English medical data. Here, we report the establishment of a mapping approach which automatically translates German data variable names into English and suggests OMOP concepts. The approach was set up using study data from the Hamburg City Health Study. It was evaluated against the current standard, refined, and tested on a separate dataset. Furthermore, different types of graphical user interfaces for the selection of suggested OMOP concepts were created and assessed. Compared to the current standard our approach performs slightly better. Its main advantage lies in the automatic processing of German phrases into English OMOP concept suggestions, operating without the need for human intervention. Challenges still lie in the adequate translation of nonstandard expressions, as well as in the resolution of abbreviations into long names. [ABSTRACT FROM AUTHOR]

Introduction The diagnosis and treatment of Parkinson's disease depend on the assessment of motor symptoms. Wearables and machine learning algorithms have emerged to collect large amounts of data and potentially support clinicians in clinical and ambulant settings. State of the art However, a systematical and reusable data architecture for storage, processing, and analysis of inertial sensor data is not available. Consequently, datasets vary significantly between studies and prevent comparability. Concept To simplify research on the neurodegenerative disorder, we propose an efficient and real-time-optimized architecture compatible with HL7 FHIR backed by a relational database schema. Lessons learned We can verify the adequate performance of the system on an experimental benchmark and in a clinical experiment. However, existing standards need to be further optimized to be fully sufficient for data with high temporal resolution. [ABSTRACT FROM AUTHOR]

Aims: We aim to develop a pragmatic screening tool for heart failure at the general population level. Methods and results: This study was conducted within the Hamburg‐City‐Health‐Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45–75 years. Heart failure was diagnosed per current guidelines. Using only digital electrocardiograms (ECGs), a convolutional neural network (CNN) was built to discriminate participants with and without heart failure. As comparisons, known risk variables for heart failure were fitted into a logistic regression model and a random forest classifier. Of the 5299 individuals included into this study, 318 individuals (6.0%) had heart failure. Using only the digital ECGs instead of several risk variables as an input, the CNN provided a comparable predictive accuracy for heart failure versus the logistic regression model and the random forest classifier [area under the curve (AUC) of 0.75, a sensitivity of 0.67 and a specificity of 0.69 for the CNN; AUC 0.77, a sensitivity of 0.63 and a specificity of 0.76 for the logistic regression; AUC 0.79, a sensitivity of 0.67 and a specificity of 0.72 for the random forest classifier]. Conclusions: Using a CNN build on digital ECGs only and requiring no additional input, we derived a screening tool for heart failure in the general population. This could be perfectly embedded into clinical routine of general practitioners, as it builds on an already established diagnostic tool and does not require additional, time‐consuming input. This could help to alleviate the underdiagnosis of heart failure. [ABSTRACT FROM AUTHOR]

Background:  Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood.Objectives:  We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability.Methods:  The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem.Results:  The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category "Diagnosis and Study" contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria.Conclusion:  Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed. [ABSTRACT FROM AUTHOR]

Copyright of Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions. [ABSTRACT FROM AUTHOR]