gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

OHDSI Germany – Join the Journey: A Workshop

Meeting Abstract

  • Ines Reinecke - Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
  • Michele Zoch - Institut für Medizinische Informatik und BiometrieMedizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
  • Christian Reich - IQVIA, Cambridge, United States; Observational Health Data Sciences and Informatics (OHDSI), New York, United States
  • Michael Kallfelz - Odysseus Data Services GmbH,, Berlin, Germany
  • Nikolai Grewe - IQVIA, Cambridge, United States
  • Martin Sedlmayr - Institut für Medizinische Informatik und BiometrieMedizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 170

doi: 10.3205/21gmds031, urn:nbn:de:0183-21gmds0317

Veröffentlicht: 24. September 2021

© 2021 Reinecke et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

The Observational Health Data Science and Informatics (OHDSI) is an international collaboration that enables researchers to conduct observational studies around the globe based on standardized data and methods that was founded in 2014 [1]. OHDSI provides a common data model (OMOP CDM) including Standardized Vocabularies for data standardization and harmonization. The OHDSI community also developed guidelines and a rich open-source software framework and tools for data transfer (ETL) and analytics. The OHDSI community is a fast growing community already present in more than 19 countries and with more than 2500 collaborators worldwide [2].

In 2021, an OHDSI Germany Workgroup was initiated, led by Ines Reinecke and Michele Zoch from Technische Universität Dresden, Carl Gustav Carus Faculty of Medicine, Institute for Medical Informatics and Biometry (IMB) in cooperation with the OHDSI Community. Its goal is to foster the adoption of OHDSI in Germany, e.g. by providing tools for the use of the core dataset of the German Medical Informatics Initiative (MII) in OMOP. As such, German data holders will be enabled to not only use the OHDSI ecosystem for analysis, but also to participate in international studies and projects (e.g. European Health Data Evidence Network (EHDEN)).

This workshop aims to answer the following question: How can we use inpatient data from university hospitals (core dataset of MII) to participate in international, retrospective observational studies? First, it will introduce into OHDSI Germany, the ongoing projects with the European Medicines Agency (EMA) and the MII consortium Medical Informatics in Research and Medicine (MIRACUM). Second, the audience will be introduced to best practices using the OHDSI tools and methods framework by means of a hands-on session so that the participants work together and create their first study cohort. An overview of the future roadmap of OHDSI Germany including work done so far and next steps will be discussed. Possibilities to join the journey become a member of the OHDSI Germany chapter will close the session. The agenda is shown in Table 1 [Tab. 1].

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


References

1.
Hripcsak G, Ryan PB, Duke JD, Shah NH, Park RW, Huser V, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci. 2016;113(27):7329.
2.
Observational Health Data Sciences and Informatics (OHDSI). The Book of OHDSI [Internet]. Independently published; 2019 [cited 2020 Feb 10]. Available from: https://ohdsi.github.io/TheBookOfOhdsi/ Externer Link