Universität zu Köln

Sass, Julian, Bartschke, Alexander, Lehne, Moritz, Essenwanger, Andrea, Rinaldi, Eugenia, Rudolph, Stefanie, Heitmann, Kai U., Vehreschild, Joerg J., von Kalle, Christof and Thun, Sylvia ORCID: 0000-0002-3346-6806 (2020). The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond. BMC Med. Inform. Decis. Mak., 20 (1). LONDON: BMC. ISSN 1472-6947

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Abstract

Background The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the German Corona Consensus Dataset (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. Methods Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. Results A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. Conclusion GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Sass, Julian UNSPECIFIED UNSPECIFIED UNSPECIFIED Bartschke, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Lehne, Moritz UNSPECIFIED UNSPECIFIED UNSPECIFIED Essenwanger, Andrea UNSPECIFIED UNSPECIFIED UNSPECIFIED Rinaldi, Eugenia UNSPECIFIED UNSPECIFIED UNSPECIFIED Rudolph, Stefanie UNSPECIFIED UNSPECIFIED UNSPECIFIED Heitmann, Kai U. UNSPECIFIED UNSPECIFIED UNSPECIFIED Vehreschild, Joerg J. UNSPECIFIED UNSPECIFIED UNSPECIFIED von Kalle, Christof UNSPECIFIED UNSPECIFIED UNSPECIFIED Thun, Sylvia UNSPECIFIED orcid.org/0000-0002-3346-6806 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-307628
DOI:	10.1186/s12911-020-01374-w
Journal or Publication Title:	BMC Med. Inform. Decis. Mak.
Volume:	20
Number:	1
Date:	2020
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1472-6947
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Medical Informatics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/30762