Header image for Vitalis 2023
Profile image for Fitness for use of Anatomical Therapeutic Classification for real world data research

Fitness for use of Anatomical Therapeutic Classification for real world data research Passed

Tuesday May 23, 2023 09:00 - 09:15 G1

Lecturers: Ines Reinecke, Yuan Peng, Elisa Henke

Track: MIE: Knowledge and Information representation and modeling

Language

English

Seminar type

On site only

Level of knowledge

Advanced

Conference

MIE

Authors

Ines Reinecke, Martin Sedlmayr, Yuan Peng, Elisa Henke, Franziska Bathelt

Lecturers

Profile image for Ines Reinecke

Ines Reinecke Lecturer

Research Associate
Technische Universität Dresden

As head of the Data Integration Center at Dresden University Hospital, Ines works with her team to make routine treatment data useful for medical research. She oversees the merging, processing and quality assurance of data, as well as compliance with data protection and interoperability standards. As Community Lead of OHDSI Germany, she promotes international research on real world data by building a German OHDSI research community. Ines is also a research associate at the Technical University of Dresden.

Profile image for Yuan Peng

Yuan Peng Lecturer

Research associate
C. G. C. Faculty of Medicine, TU Dresden

Yuan Peng is a research associate at TU Dresden. She focuses on the data harmonization and the interoperability between standards regarding FHIR and OMOP CDM. She also participate in the OHDSI Germany Community.

Profile image for Elisa Henke

Elisa Henke Lecturer

Research associate
C. G. C. Faculty of Medicine, TU Dresden

Elisa is a research associate at the Technische Universität Dresden. As head of the interoperability group, Elisa works with her team to establish syntactic, semantic, technical, and organizational interoperability to realize medical research between German university hospitals. In this context, Elisa is working on the harmonization of clinical and claims data into international research data repositories such as OMOP CDM to promote international research on real world data.