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

J 2020

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 et. al.

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

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30304 Public and environmental health

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

Abstract

V originále

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.

Citovat

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.

@article{1670464,
   author = {Haneef, R. and Delnord, M. and Vernay, M. and Bauchet, E. and Gaidelyte, R. and Van Oyen, H. and Or, Z. and PerezandGomez, B. and Palmieri, L. and Achterberg, P. and Tijhuis, M. and Zaletel, M. and MathisandEdenhofer, S. and Májek, Ondřej and Haaheim, H. and Tolonen, H. and Gallay, A.},
   article_location = {LONDON},
   article_number = {1},
   doi = {http://dx.doi.org/10.1186/s13690-020-00436-9},
   keywords = {Innovation; Linked data; Artificial intelligence; Machine learning technique; Health status monitoring; Public health surveillance; Health information; Health indicators},
   language = {eng},
   issn = {0778-7367},
   journal = {ARCHIVES OF PUBLIC HEALTH},
   title = {Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries},
   url = {https://archpublichealth.biomedcentral.com/track/pdf/10.1186/s13690-020-00436-9},
   volume = {78},
   year = {2020}
}

TY  - JOUR
ID  - 1670464
AU  - Haneef, R. - Delnord, M. - Vernay, M. - Bauchet, E. - Gaidelyte, R. - Van Oyen, H. - Or, Z. - Perez-Gomez, B. - Palmieri, L. - Achterberg, P. - Tijhuis, M. - Zaletel, M. - Mathis-Edenhofer, S. - Májek, Ondřej - Haaheim, H. - Tolonen, H. - Gallay, A.
PY  - 2020
TI  - Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries
JF  - ARCHIVES OF PUBLIC HEALTH
VL  - 78
IS  - 1
SP  - 1-11
EP  - 1-11
PB  - BIOMED CENTRAL LTD
SN  - 07787367
KW  - Innovation
KW  - Linked data
KW  - Artificial intelligence
KW  - Machine learning technique
KW  - Health status monitoring
KW  - Public health surveillance
KW  - Health information
KW  - Health indicators
UR  - https://archpublichealth.biomedcentral.com/track/pdf/10.1186/s13690-020-00436-9
L2  - https://archpublichealth.biomedcentral.com/track/pdf/10.1186/s13690-020-00436-9
N2  - 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.
ER  -

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. \textit{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.

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