Secondary Use and Analysis of Big Data Collected for Patient Care.

Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

Data Integration in Genomic Medicine: Trends and Applications. Contribution of the IMIA Working Group on Informatics in Genomic Medicine.

OBJECTIVES: In a near future, each person will incorporate his/her own sequenced genome in his/her electronic health record. In that precise moment, genomic medicine will be fundamental for clinical practice, as an essential key of personalized medicine. All the genomic data, as well as other 'omics' and clinical data necessary for personalized medicine, are stored in several distributed databases. Research and patient care require each time more biomedical data integration of several distributed heterogeneous datasources. METHODS: This work develops a comprehensive review of the most relevant works in biomedical data integration, specifically in genomic medical data, analyzing the evolution of architecture and integration techniques during the last 20 years, and its usage. CONCLUSION: Most of these solutions, based on cross-linking, data warehouse or federated approaches, are suitable for specific domains. However, none of the models found in the literature is completely appropriate for a general biomedical data integration problem.