Abstract
Recent snapshots of the European progress on big data in health care and precision medicine reveal diverse perceptions of experts and the public, leading to the impression that algorithmic issues have the largest share among the challenges all health systems are faced with. Yet, from a comparison of different countries it is evident that the adaption and integration of heterogeneous data sources have a major impact on the advancement of precision medicine. Legal regulations for implementation and operation of healthcare networking are actively discussed in the public and gradually implemented in several countries. Based on a unified documentation, they are a perfect precondition for integrating distributed healthcare data to a big data platform with a reliable fact representation. Now, basic and clinical scientists have to be motivated to share their work with these data platforms. In this work, we aim to provide an overview on the common issues in big healthcare data applications and address the challenges for the involved scientific, clinical and administrative partners. We propose a possible strategy for a comprehensive data integration by iterating data harmonization, semantic enrichment and data analysis processes.
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This study was supported by grants from the German Science Foundation (SFB 1074, Project Z1), the Federal Ministry of Education and Research (BMBF, Gerontosys II, Forschungskern SyStaR, Project ID 0315894A and e:Med, SYMBOL-HF, ID 01ZX1407A), and the European Community’s Seventh Framework Programme (FP7/2007–2013 under Grant Agreement No. 602783) all to HAK, and also supported by the BMBF within the Medical informatics initiative (concept phase support).
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Kraus, J.M., Lausser, L., Kuhn, P. et al. Big data and precision medicine: challenges and strategies with healthcare data. Int J Data Sci Anal 6, 241–249 (2018). https://doi.org/10.1007/s41060-018-0095-0
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DOI: https://doi.org/10.1007/s41060-018-0095-0