A cross-sectional study of data linkage and artificial intelligence practices across European countries

Abstract Background The availability of different data sources is increasing with the possibility to link them with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the use of data linkage and artificial intelligence (AI) in routine public health activities and the constraints to linking different data sources. Methods A cross-sectional survey was performed across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results In Europe, the use of data linkage and AI at national institutes of public health, health information and statistics varies. The use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes is common in majority of European countries. The use of AI to estimate health indicators is not frequent among these institutes. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as main constraints to routine data linkage for public health surveillance and research. Conclusions Our study showed that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system allowing to link different data sources are essential to contribute to public health research. Moreover, it supports evidence-informed health policy making processes with an overview of various aspects affecting population health and may contribute to European pandemic preparedness.