Towards Implementation of OMOP in a German University Hospital Consortium

Research output: Contribution to journalResearch articleContributedpeer-review


  • C Maier - (Author)
  • L Lang - (Author)
  • H Storf - (Author)
  • P Vormstein - (Author)
  • R Bieber - (Author)
  • J Bernarding - (Author)
  • T Herrmann - (Author)
  • C Haverkamp - (Author)
  • P Horki - (Author)
  • J Laufer - (Author)
  • F Berger - (Author)
  • G Höning - (Author)
  • H W Fritsch - (Author)
  • J Schüttler - (Author)
  • T Ganslandt - (Author)
  • H U Prokosch - (Author)
  • M Sedlmayr - , Institute for Medical Informatics and Biometry (Author)


BACKGROUND: In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions.

OBJECTIVE: To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements.

METHODS: The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed.

RESULTS: While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made.Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported.A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot.

CONCLUSION: OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.


Original languageEnglish
Pages (from-to)54-61
Number of pages8
JournalApplied clinical informatics
Issue number1
Publication statusPublished - Jan 2018

External IDs

PubMedCentral PMC5801887
Scopus 85052638381
ORCID /0000-0002-9888-8460/work/142254092


Sustainable Development Goals


  • Cooperative Behavior, Germany, Health Plan Implementation, Hospitals, University, Humans, Outcome Assessment, Health Care, Surveys and Questionnaires, Vocabulary