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

HANEEF, R., M. DELNORD, M. VERNAY, E. BAUCHET, R. GAIDELYTE, H. VAN OYEN, Z. OR, B. PEREZ-GOMEZ, L. PALMIERI, P. ACHTERBERG, M. TIJHUIS, M. ZALETEL, S. MATHIS-EDENHOFER, Ondřej MÁJEK, H. HAAHEIM, H. TOLONEN and A. GALLAY. Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries. ARCHIVES OF PUBLIC HEALTH. LONDON: BIOMED CENTRAL LTD, 2020, vol. 78, No 1, p. 1-11. ISSN 0778-7367. Available from: https://dx.doi.org/10.1186/s13690-020-00436-9.

Basic information

• Original name: Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries
• Authors: HANEEF, R. (250 France, guarantor), M. DELNORD (56 Belgium), M. VERNAY (250 France), E. BAUCHET (250 France), R. GAIDELYTE (440 Lithuania), H. VAN OYEN (56 Belgium), Z. OR (250 France), B. PEREZ-GOMEZ (724 Spain), L. PALMIERI (380 Italy), P. ACHTERBERG (528 Netherlands), M. TIJHUIS (528 Netherlands), M. ZALETEL (705 Slovenia), S. MATHIS-EDENHOFER (40 Austria), Ondřej MÁJEK (203 Czech Republic, belonging to the institution), H. HAAHEIM (578 Norway), H. TOLONEN (246 Finland) and A. GALLAY (250 France).
• Edition: ARCHIVES OF PUBLIC HEALTH, LONDON, BIOMED CENTRAL LTD, 2020, 0778-7367.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30304 Public and environmental health
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 2.589
• RIV identification code: RIV/00216224:14110/20:00116003
• Organization unit: Faculty of Medicine
• Doi: http://dx.doi.org/10.1186/s13690-020-00436-9
• UT WoS: 000542574800001
• Keywords in English: Innovation; Linked data; Artificial intelligence; Machine learning technique; Health status monitoring; Public health surveillance; Health information; Health indicators
• Tags: 14119612, rivok
• Tags: International impact, Reviewed

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.