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. 2020 Jun 10:78:55.
doi: 10.1186/s13690-020-00436-9. eCollection 2020.

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

Romana Haneef  1 , Marie Delnord  2 , Michel Vernay  1 , Emmanuelle Bauchet  1 , Rita Gaidelyte  3 , Herman Van Oyen  2   4 , Zeynep Or  5 , Beatriz Pérez-Gómez  6 , Luigi Palmieri  7 , Peter Achterberg  8 , Mariken Tijhuis  8 , Metka Zaletel  9 , Stefan Mathis-Edenhofer  10 , Ondřej Májek  11   12 , Håkon Haaheim  13 , Hanna Tolonen  14 , Anne Gallay  1
Affiliations

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

Romana Haneef et al. Arch Public Health. .

Abstract

Background: The availability of data generated from different sources is increasing with the possibility to link these data sources 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 current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources.

Method: We performed a survey 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: The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries 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. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research.

Conclusions: Our results highlight 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 and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.

Keywords: Artificial intelligence; Health indicators; Health information; Health status monitoring; Innovation; Linked data; Machine learning technique; Public health surveillance.

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Conflict of interest statement

Competing interestsH. Van Oyen is one of the co-authors of this paper and the editor of “Archives of Public Health”. All other authors declare that they have no competing interests related to the work.

Figures

Fig. 1
Fig. 1
Flow diagram of studies using linked data and artificial intelligence for health status monitoring to develop a survey on identifying various data linkage practices across European countries in 2019. *To be more specific for AI techniques, we looked for studies using machine learning techniques (i.e., one type of AI technique) more often used for health status monitoring
See this image and copyright information in PMC

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