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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8131366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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