Transformation of microbiology data into a standardised data representation using OpenEHR

Abstract 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 reus...

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Autores principales:	Antje Wulff, Claas Baier, Sarah Ballout, Erik Tute, Kim Katrin Sommer, Martin Kaase, Anneka Sargeant, Cora Drenkhahn, Infection Control Study Group, Dirk Schlüter, Michael Marschollek, Simone Scheithauer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f4a5650ac8b4c829b64c91b3e5fc95a
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spelling	oai:doaj.org-article:1f4a5650ac8b4c829b64c91b3e5fc95a2021-12-02T16:51:14ZTransformation of microbiology data into a standardised data representation using OpenEHR10.1038/s41598-021-89796-y2045-2322https://doaj.org/article/1f4a5650ac8b4c829b64c91b3e5fc95a2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89796-yhttps://doaj.org/toc/2045-2322Abstract 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.Antje WulffClaas BaierSarah BalloutErik TuteKim Katrin SommerMartin KaaseAnneka SargeantCora DrenkhahnInfection Control Study GroupDirk SchlüterMichael MarschollekSimone ScheithauerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antje Wulff Claas Baier Sarah Ballout Erik Tute Kim Katrin Sommer Martin Kaase Anneka Sargeant Cora Drenkhahn Infection Control Study Group Dirk Schlüter Michael Marschollek Simone Scheithauer Transformation of microbiology data into a standardised data representation using OpenEHR
description	Abstract 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	article
author	Antje Wulff Claas Baier Sarah Ballout Erik Tute Kim Katrin Sommer Martin Kaase Anneka Sargeant Cora Drenkhahn Infection Control Study Group Dirk Schlüter Michael Marschollek Simone Scheithauer
author_facet	Antje Wulff Claas Baier Sarah Ballout Erik Tute Kim Katrin Sommer Martin Kaase Anneka Sargeant Cora Drenkhahn Infection Control Study Group Dirk Schlüter Michael Marschollek Simone Scheithauer
author_sort	Antje Wulff
title	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_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_sort	transformation of microbiology data into a standardised data representation using openehr
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f4a5650ac8b4c829b64c91b3e5fc95a
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Transformation of microbiology data into a standardised data representation using OpenEHR

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