Abstract
Any given health system needs to increase efficiency and effectiveness up to the point of requiring a transformation of their current model to ensure their sustainability and continuity. The electronic medical record (EMR) is the main source of knowledge to improve the quality of healthcare, clinical research, epidemiological surveillance, patient empowerment, personalized medicine, and clinical decision-making support systems. There is also a huge amount of available information related to diseases and other medical conditions, such as drugs and therapies, omics data (genetic and proteomic), social networks, and wearable devices. Big Data technologies allow the processing of this data to reach the final goal, which is a learning health system. The great diversity of data, sources, structures, and uses requires a data linkage procedure to integrate and harmonize these data. This generation of knowledge allows the transition from evidence-based medicine, which still prevails, to practice-based medicine. The key points for any Big Data project based on EMRs and other medical information sources are semantic interoperability, data structure and granularity, information quality, patient privacy, legal framework, and bioethics.
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