The use of big data derived from electronic health records in primary care (PC) research has increased in recent years, and this trend is expected to continue to grow. Both the large amount of data available and their longitudinal nature bring many opportunities when performing analyses based on big data, machine learning and artificial intelligence (AI), such as those related to phenotyping, identification of health patterns and trajectories or integration with data from other modalities such as omics or medical imaging data. However, there are also challenges, such as those associated with data quality, harmonization and privacy, or the ability to generalize the results obtained. In this context, our interest is to work at the intersection of PC big data, data science and AI to contribute to the advancement of PC research by increasing the quality, access and scope of health data in the field of PC research, while ensuring a dialogue between the different lines of the group as well as between groups, institutions and key agents of the ecosystem. The research team is formed by technical and clinical professionals from IDIAP Jordi Gol and ICS with diverse training that covers the skills necessary to develop the research lines 1) machine learning and artificial intelligence, 2) HCE data mining and analysis and integration with heterogeneous data and 3) data quality, harmonization and privacy.
