Towards Implementation of OMOP in a German University Hospital Consortium

Area of Science:

• Health Informatics
• Translational Research
• Data Science

Background:

• German Federal Ministry of Education and Research initiative for data integration and sharing.
• Utilized Observational Medical Outcomes Partnership (OMOP) common data model and Observational Health Data Sciences and Informatics (OHDSI) tools.
• Aimed to improve reuse of patient care and translational research data.

Purpose of the Study:

• Pilot OMOP/OHDSI implementation in a consortium of eight German university hospitals.
• Evaluate data harmonization and sharing applicability.
• Identify requirements for enhancing OMOP/OHDSI.

Main Methods:

• Prepared vocabularies and terminological mapping for data import.
• Designed data import processes from source files.
• Provided preconfigured virtual machines with OMOP database and OHDSI tools for eight hospitals.
• Executed federated/distributed queries for analysis.

Main Results:

• Successfully mapped 98.8% of ICD-10 German Modification diagnoses; procedures required terminology extension.
• Imported data for 3 million inpatients (26 million conditions, 21 million procedures, 23 million observations).
• Successfully executed a federated query for colorectal cancer patients (16,701 cases).

Conclusions:

• OMOP/OHDSI is a viable open-source solution for data integration in German research consortia.
• Terminology mapping challenges necessitate extensions to standard terminologies.
• Researchers can leverage the active OHDSI community for future development.