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Transformation of microbiology data into a standardised data representation using OpenEHR

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability a...

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Autores principales: Wulff, Antje, Baier, Claas, Ballout, Sarah, Tute, Erik, Sommer, Kim Katrin, Kaase, Martin, Sargeant, Anneka, Drenkhahn, Cora, Schlüter, Dirk, Marschollek, Michael, Scheithauer, Simone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131366/
https://www.ncbi.nlm.nih.gov/pubmed/34006956
http://dx.doi.org/10.1038/s41598-021-89796-y
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author Wulff, Antje
Baier, Claas
Ballout, Sarah
Tute, Erik
Sommer, Kim Katrin
Kaase, Martin
Sargeant, Anneka
Drenkhahn, Cora
Schlüter, Dirk
Marschollek, Michael
Scheithauer, Simone
author_facet Wulff, Antje
Baier, Claas
Ballout, Sarah
Tute, Erik
Sommer, Kim Katrin
Kaase, Martin
Sargeant, Anneka
Drenkhahn, Cora
Schlüter, Dirk
Marschollek, Michael
Scheithauer, Simone
author_sort Wulff, Antje
collection PubMed
description The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.
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spelling pubmed-81313662021-05-19 Transformation of microbiology data into a standardised data representation using OpenEHR Wulff, Antje Baier, Claas Ballout, Sarah Tute, Erik Sommer, Kim Katrin Kaase, Martin Sargeant, Anneka Drenkhahn, Cora Schlüter, Dirk Marschollek, Michael Scheithauer, Simone Sci Rep Article The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems. Nature Publishing Group UK 2021-05-18 /pmc/articles/PMC8131366/ /pubmed/34006956 http://dx.doi.org/10.1038/s41598-021-89796-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wulff, Antje
Baier, Claas
Ballout, Sarah
Tute, Erik
Sommer, Kim Katrin
Kaase, Martin
Sargeant, Anneka
Drenkhahn, Cora
Schlüter, Dirk
Marschollek, Michael
Scheithauer, Simone
Transformation of microbiology data into a standardised data representation using OpenEHR
title Transformation of microbiology data into a standardised data representation using OpenEHR
title_full Transformation of microbiology data into a standardised data representation using OpenEHR
title_fullStr Transformation of microbiology data into a standardised data representation using OpenEHR
title_full_unstemmed Transformation of microbiology data into a standardised data representation using OpenEHR
title_short Transformation of microbiology data into a standardised data representation using OpenEHR
title_sort transformation of microbiology data into a standardised data representation using openehr
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131366/
https://www.ncbi.nlm.nih.gov/pubmed/34006956
http://dx.doi.org/10.1038/s41598-021-89796-y
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