Transformation of microbiology data into a standardised data representation using OpenEHR
Antje Wulff, Claas Baier, Sarah Ballout, Erik Tute, Kim Katrin Sommer, Martin Kaase, Anneka Sargeant, Cora Drenkhahn, Infection Control Study Group, Patrick Fehling, Sabine Rey, Markus Suhr, Vanessa M. Eichel, Nico T. Mutters, Klaus Heeg, Petra Gastmeier, Michael Behnke, Luis Alberto Peña Diaz, Sylvia Thun, Roland Eils, Alexander Mellmann, Hauke Tönnies, Benedikt Zacher, Tim Eckmanns, Timo Sztyler, Brandon Malone, Angela Merzweiler, Martin Dugas, Michael Storck, Marcel Wunderlich, Tatiana von Landesberger, Benjamin Gebel, Thorsten R. Klingen, Stephan Glöckner, Dirk Schlüter, Michael Marschollek, Simone Scheithauer
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.