In order to reuse data for clinical research it is then necessary to overcome two main challenges – to formalize data sources and to increase the portability. Once the challenge is resolved, it then will allow research applications to reuse clinical data. In this paper, three data models such as entity-attribute-value, ontological and data-driven are described. Their further implementation at University Hospitals of Geneva (HUG) in the data integration methodologies for operational healthcare data sources of the European projects such as DebugIT and EHR4CR and national project the Swiss Transplant Cohort Study are explained. In these methodologies the clinical data are either aligned according to standardised terminologies using different processing techniques or transformed and loaded directly to data models. Then these models are compared and discussed based on the quality criteria. The comparison shows that the described data models are strongly dependent on the objectives of the projects.
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