Publications

218 Publications visible to you, out of a total of 218

Abstract (Expand)

Business informatics and medical informatics adopt and adapt methods and knowledge from computer science and further develop appropriate methods for the particular needs in their application domains. A panel discussion at the 2018 conference of the German Society for Medical Informatics, Biometry and Epidemiology (GMDS) analyzed the relationship between business informatics, medical informatics and computer science. Five questions guided the discussion: What are the basic goals of these disciplines? To what extent does practical application of results shape the disciplines? Do medicine and economy demand for particular methods in in - for - mat - ics and computer science? How important is foundation by theory and evidence? Can the disciplines learn from each other? The analysis made clear that business informatics, medical informatics and computer science would gain profit from a more systematic mutual exchange. The \grqqLearning Healthcare System” could provide a useful framework. Wirtschaftsinformatik und Medizinische Informatik gehören zu den sogenannten Bindestrich-Informatik-Fächern, die sich mit der Anwendung der Methoden und Erkenntnisse der Informatik, aber auch mit der Weiterentwicklung solcher Methoden und Erkenntnisse für gewisse Anwendungsgebiete befassen. Auf einer Podiumsdiskussion der Jahrestagung 2018 der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS) wurde für Wirtschaftsinformatik, Medizinische Informatik und Informatik analysiert wie sie zueinander stehen. Die Analyse erfolgte anhand von fünf Fragen: Welche grundlegenden Ziele bestimmen die jeweilige wissenschaftliche Arbeit? Wie ist der Praxisbezug ausgeprägt? Inwieweit sind Besonderheiten von Medizin bzw. Ökonomie prägend für die jeweilige wissenschaftliche Arbeit? Welche Rolle spielen Theoriefundierung und Evidenz? Was können Wirtschaftsinformatik und Informatik von Medizinischer Informatik und Medizin lernen – und umgekehrt? Die Analyse zeigt, dass die drei Disziplinen von einem systematischen wechselseitigen Austausch profitieren können. Das \glqqLernende Gesundheitssystem\grqq bietet Ansätze für einen entsprechenden Rahmen.

Authors: Alfred Winter, Reinhold Haux, Barbara Paech, Frank Teuteberg, Ursula Hübner

Date Published: 2019

Publication Type: Journal article

Abstract

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Authors: Franziska Jahn, Konrad Höffner, Birgit Schneider, Anna Lörke, Thomas Pause, Elske Ammenwerth, Alfred Winter

Date Published: 2019

Publication Type: InCollection

Abstract

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Authors: Rainer Alt, Jan Fabian Ehmke, Alfred Winter, et al.

Date Published: 2019

Publication Type: Journal article

Abstract (Expand)

Introduction: Evidence-based health informatics needs easy access to published health IT evaluation studies. The indexing of health IT evaluation studies by using MeSH terms is not specific enough which makes retrieval difficult [ref:1]. To solve this problem, we want to support the retrieval[for full text, please go to the a.m. URL]

Authors: Verena Dornauer, Maryam Ghalandari, Konrad Höffner, Franziska Jahn, Alfred Winter, Elske Ammenwerth

Date Published: 2019

Publication Type: Misc

Abstract

Not specified

Authors: Konrad Höffner, Franziska Jahn, Anna Lörke, Thomas Pause, Birgit Schneider, Elske Ammenwerth, Alfred Winter

Date Published: 2019

Publication Type: InCollection

Abstract (Expand)

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. "Smart Medical Information Technology for Healthcare (SMITH)" is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH's goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. OBJECTIVES: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. GOVERNANCE AND POLICIES: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals' Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. METHODOLOGY AND ARCHITECTURAL FRAMEWORK: To share medical and research data, SMITH's information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM(2) for enterprise architecture modeling supports a consistent development process.The DIC reference architecture determines the services, applications and the standardsbased communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. USE CASES: The methodological use case "Phenotype Pipeline" (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case "Algorithmic Surveillance of ICU Patients" (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a "hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections" (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. DISCUSSION: SMITH's strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals' information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

Authors: A. Winter, S. Staubert, D. Ammon, S. Aiche, O. Beyan, V. Bischoff, P. Daumke, S. Decker, G. Funkat, J. E. Gewehr, A. de Greiff, S. Haferkamp, U. Hahn, A. Henkel, T. Kirsten, T. Kloss, J. Lippert, M. Lobe, V. Lowitsch, O. Maassen, J. Maschmann, S. Meister, R. Mikolajczyk, M. Nuchter, M. W. Pletz, E. Rahm, M. Riedel, K. Saleh, A. Schuppert, S. Smers, A. Stollenwerk, S. Uhlig, T. Wendt, S. Zenker, W. Fleig, G. Marx, A. Scherag, M. Loffler

Date Published: 18th Jul 2018

Publication Type: Journal article

Abstract (Expand)

BACKGROUND: Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. OBJECTIVES: This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals. METHOD: Based on the inclusion/exclusion criteria of 5 sample studies and a text corpus consisting of 212 doctor's letters and medical follow-up documentation from a university cancer center, a prototype was developed and technically evaluated using NLP procedures (UIMA) for the extraction of facts from medical free texts. RESULTS: It was found that although the extracted entities are not always correct (precision between 23% and 96%), they provide a decisive indication as to which patient file should be read preferentially. CONCLUSION: The prototype presented here demonstrates the technical feasibility. In order to find available, lucrative phenotypes, an in-depth evaluation is required.

Authors: M. Lobe, S. Staubert, C. Goldberg, I. Haffner, A. Winter

Date Published: 5th May 2018

Publication Type: Journal article

Human Diseases: breast cancer

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