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. 2022 Apr 19;19(9):4950.
doi: 10.3390/ijerph19094950.

Challenges and Drawbacks of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems' Digitalization

Alexandra-Mădălina Țăran  1 , Lavinia Mustea  2 , Sorana Vătavu  2 , Oana-Ramona Lobonț  2 , Magda-Mihaela Luca  3
Affiliations

Challenges and Drawbacks of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems' Digitalization

Alexandra-Mădălina Țăran et al. Int J Environ Res Public Health. .

Abstract

The COVID-19 pandemic and the digitalization of medical services present significant challenges for the medical sector of the European Union, with profound implications for health systems and the provision of high-performance public health services. The sustainability and resilience of health systems are based on the introduction of information and communication technology in health processes and services, eliminating the vulnerability that can have significant consequences for health, social cohesion, and economic progress. This research aims to assess the impact of digitalization on several dimensions of health, introducing specific implications of the COVID-19 pandemic. The research methodology consists of three procedures: cluster analysis performed through vector quantization, agglomerative clustering, and an analytical approach consisting of data mapping. The main results highlight the importance of effective national responses and provide recommendations, various priorities, and objectives to strengthen health systems at the European level. Finally, the results reveal the need to reduce the gaps between the EU member states and a new approach to policy, governance, investment, health spending, and the performing provision of digital services.

Keywords: COVID-19; European Union; data mapping; digitalization; health; method of vector quantization.

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Conflict of interest statement

The authors declare no conflict of interest. The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Health in EU-27, 2020: (a) life expectancy; (b) live births rate; (c) median age; (d) total fertility; (e) health spendings. Source: own process in Stata 17.
Figure 2
Figure 2
Digitalization in EU-27, 2020: (a) Human capital (by Internet user skills); (b) connectivity (by mobile broadband); (c) integration of digital technology (by digital intensity); (d) digital public services (by e-government) (e) DESI overall index. Source: own process in Stata 17.
Figure 3
Figure 3
COVID-19 in EU-27, 2020 (31 December): (a) daily new cases; (b) daily new deaths; (c) daily positive tests; (d) daily share of the population receiving a first COVID-19 vaccine dose. Source: own process in Stata 17.
Figure 4
Figure 4
Research framework analysis techniques. Source: authors’ compilation.
Figure 5
Figure 5
Research framework methods techniques. Source: authors’ compilation.
Figure 6
Figure 6
Representative elements (nodes) of a workflow in KNIME. (Node 1) spatial data; (Node 2) k-Means algorithm—perform clustering; (Node 3) assign colors to clusters; (Node 4) replace input data or create new row; (Node 5) create scatter plot; (Node 6) generate the table with afferent clusters; (Node 7) assign a shape to clusters; (Node 8) hierarchical—dendrogram. Source: authors’ compilation in KNIME analytics platform.
Figure 7
Figure 7
KNIME software elements (nodes) in the workflow regarding health indicators. Source: authors’ compilation in KNIME Analytics Platform.
Figure 8
Figure 8
Countries displayed by clusters, based on health indicators. Source: authors’ compilation in KNIME analytics platform.
Figure 9
Figure 9
Dendrogram of clusters. Source: authors’ compilation in KNIME analytics platform.
Figure 10
Figure 10
The workflow of KNIME with nodes regarding digitalization indicators. Source: authors’ compilation in KNIME analytics platform.
Figure 11
Figure 11
Distribution of countries based on digitalization indicators. Source: authors’ compilation in KNIME analytics platform.
Figure 12
Figure 12
KNIME workflow with elements regarding COVID-19. Source: authors’ compilation in KNIME analytics platform.
Figure 13
Figure 13
Distribution of countries into clusters, based on COVID-19. Source: authors’ compilation in KNIME analytics platform.
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