Large research-organizations like university hospitals face big challenges to efficiently use the enormous amount of data from care and research for analytics. Furthermore, the collaboration in research networks requires organizations to make data FAIR: findable, accessible, interoperable and re-usable.
EHRbase helps to organizations to integrate and normalize data from care and research, provide re-usable and machine-readable information models (through openEHR Archetypes and Templates) and make the data queryable and accessible through the Archetype Query Language and open application programming interfaces.
The value of the EHRbase as research platform grows over time when research databases and clinical registries are natively developed based on openEHR and re-use standardized data models.
As openEHR provides a rich description of the data and is machine-readable, automated transformations to formalisms and tools as i2b2/tranSMART are feasible.