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Objectives: Presentation of an overview of the reasons why data integration initiatives should be seen as enablers for eective decision support in data-intensive healthcare settings.  Methods: Typical challenges rising from the information requirements of clinical decision support systems are high- lighted. We then propose a methodological solution where several heterogeneous data sources are integrated by the means of a common data model on top of which the DSS is built. Results: We report on two successful case studies based on the DSSs developed in the context of the MobiGuide and Mosaic projects, funded by the European Union in the Seventh Framework Program. The MobiGuide patient guidance system has been success- fully validated during a recent pilot study involving 30 pa- tients (10 with atrial brillation and 20 with gestational diabetes), while Mosaic is currently undergoing a valida- tion phase involving 1000 type 2 Diabetes patients. Conclusions: In the era of big data, eective data integra- tion strategies are an essential need for medical informatics solutions and even more for those intended to support de- cision processes. Building generic DSSs based on a stable (but easily extensible) data model, speci cally designed to meet the information requirements of DSSs and analytics, has proven to be a successful solution in the two presented use cases.

Decision Support through Data Integration: Strategies to Meet the Big Data Challenge