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85 Publications visible to you, out of a total of 85
Pseudonymization of PHI items in German clinical reports
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the adaptation of a non-clinical pseudonymization system, originally developed for a German email corpus, for clinical use. This tool replaces previously identified Protected Health Information … (PHI) items as carriers of privacy-sensitive information (original names for people, organizations, places, etc.) with semantic type-conformant, yet, fictitious surrogates. We evaluate the generated substitutes for grammatical correctness, semantic and medical plausibility and find particularly low numbers of error instances (less than 1%) on all of these dimensions.

Authors: Christina Lohr, Elisabeth Eder, Udo Hahn

Date Published: 1st May 2021

Publication Type: InCollection

Health Informatics learning objectives on an interoperable, collaborative platform
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Catalogues of learning objectives for Biomedical and Health Informatics are relevant prerequisites for systematic and effective qualification. Catalogue management needs to integrate different catalogues … and support collaborative revisioning. The Health Informatics Learning Objectives Navigator (HI-LONa) offers an open, interoperable platform based on Semantic Web Technology. At present HI-LONa contains 983 learning objectives of three relevant catalogues. HI-LONa successfully supported a multiprofessional consensus process.

Authors: Cord Spreckelsen, Ulrike Schemmann, Lo An Phan-Vogtmann, André Scherag, Alfred Winter, Birgit Schneider

Date Published: 1st May 2021

Publication Type: Journal article

Ontological modelling and execution of phenotypic queries in the Leipzig Health Atlas
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles … are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.

Authors: Alexandr Uciteli, Christoph Beger, Jonas Wagner, Alexander Kiel, Frank A Meineke, Sebastian Stäubert, Matthias Löbe, René Hänsel, Judith Schuster, Toralf Kirsten, Heinrich Herre

Date Published: 1st May 2021

Publication Type: InCollection

Ontological modelling and FHIR Search based representation of basic eligibility criteria
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Planning clinical studies to check medical hypotheses requires the specification of eligibility criteria in order to identify potential study participants. Electronically available patient data allows … to support the recruitment of patients for studies. The Smart Medical Information Technology for Healthcare (SMITH) consortium aims to establish data integration centres to enable the innovative use of available healthcare data for research and treatment optimization. The data from the electronic health record of patients in the participating hospitals is integrated into a Health Data Storage based on the Fast Healthcare Interoperability Resources standard (FHIR), developed by HL7. In SMITH, FHIR Search is used to query the integrated data. An investigation has shown the advantages and disadvantages of using FHIR Search for specifying eligibility criteria. This paper presents an approach for modelling eligibility criteria as well as for generating and executing FHIR Search queries. Our solution is based on the Phenotype Manager, a general ontological phenotyping framework to model and calculate phenotypes using the Core Ontology of Phenotypes.

Authors: A. Uciteli, C. Beger, J. Wagner, T. Kirsten, F. A. Meineke, S. Staubert, M. Lobe, H. Herre

Date Published: 26th Apr 2021

Publication Type: Journal article

Integration of allergy documentation into an interoperable archiving and communication platform to improve patient care and clinical research at the Jena University Hospital
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

As part of the digitalization strategy for medical documentation, we introduced a vendor-neutral archive (VNA) at the Jena University Hospital. The VNA provides data based on interoperable formats for … all clinical systems and for communication with other actors as well as for secondary data use. HL7 FHIR is used as a standard for structuring individual data. Using the example of allergy documentation, the paper describes objectives, work status, and experiences from several sub-projects currently being pursued in Jena to establish FHIR. Challenges in information modelling, semantic annotation, connection of clinical sub-systems and integration of FHIR resources in archiving structures and processes are discussed and the necessary cooperation for profiling technical standards is indicated.

Authors: Henner M. Kruse, Alexander Helhorn, Lo An Phan-Vogtmann, Julia Palm, Eric Thomas, Andreas Iffland, Andrew Heidel, Susanne Müller, Kutaiba Saleh, Karsten Krohn, Martin Specht, Michael Hartmann, Katrin Farker, Cord Spreckelsen, Andreas Henkel, Andre Scherag, Danny Ammon

Date Published: 26th Apr 2021

Publication Type: Journal article

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

INTRODUCTION: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic … measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS: In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00014330.

Authors: Gernot Marx, Johannes Bickenbach, Sebastian Johannes Fritsch, Julian Benedict Kunze, Oliver Maassen, Saskia Deffge, Jennifer Kistermann, Silke Haferkamp, Irina Lutz, Nora Kristiana Voellm, Volker Lowitsch, Richard Polzin, Konstantin Sharafutdinov, Hannah Mayer, Lars Kuepfer, Rolf Burghaus, Walter Schmitt, Joerg Lippert, Morris Riedel, Chadi Barakat, André Stollenwerk, Simon Fonck, Christian Putensen, Sven Zenker, Felix Erdfelder, Daniel Grigutsch, Rainer Kram, Susanne Beyer, Knut Kampe, Jan Erik Gewehr, Friederike Salman, Patrick Juers, Stefan Kluge, Daniel Tiller, Emilia Wisotzki, Sebastian Gross, Lorenz Homeister, Frank Bloos, André Scherag, Danny Ammon, Susanne Mueller, Julia Palm, Philipp Simon, Nora Jahn, Markus Loeffler, Thomas Wendt, Tobias Schuerholz, Petra Groeber, Andreas Schuppert

Date Published: 1st Apr 2021

Publication Type: Journal article

Future medical artificial intelligence application requirements and expectations of physicians in German university hospitals: Web-based survey
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. … AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE: This study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS: The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

Authors: Oliver Maassen, Sebastian Fritsch, Julia Palm, Saskia Deffge, Julian Kunze, Gernot Marx, Morris Riedel, Andreas Schuppert, Johannes Bickenbach

Date Published: 1st Mar 2021

Publication Type: Journal article

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The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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