University of Oulu

Shi X, Nikolic G, Fischaber S, Black M, Rankin D, Epelde G, Beristain A, Alvarez R, Arrue M, Pita Costa J, Grobelnik M, Stopar L, Pajula J, Umer A, Poliwoda P, Wallace J, Carlin P, Pääkkönen J and De Moor B (2022) System Architecture of a European Platform for Health Policy Decision Making: MIDAS. Front. Public Health 10:838438. doi: 10.3389/fpubh.2022.838438

System architecture of a European platform for health policy decision making: : MIDAS

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Author: Shi, Xi1,2; Nikolic, Gorana1; Fischaber, Scott3;
Organizations: 1Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
2Vlerick Business School, Leuven, Belgium
3Analytics Engines, Belfast, United Kingdom
4School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
5Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
6EHealth Group, Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
7Quintelligence, Ljubljana, Slovenia
8AI Lab, Institute Jozef Stefan, Ljubljana, Slovenia
9Data-Driven Solutions, Smart Health, VTT Technical Research Centre of Finland, Tampere, Finland
10IBM Ireland Lab, Innovation Exchange, International Business Machines Corporation, Dublin, Ireland
11School of Computing, Ulster University, Jordanstown, United Kingdom
12Faculty of Wellbeing, Education and Language Studies, Open University, Belfast, United Kingdom
13Centre for Health and Technology, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.6 MB)
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Language: English
Published: Frontiers Media, 2022
Publish Date: 2022-06-10


Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner.

Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources.

Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics.

Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

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Series: Frontiers in public health
ISSN: 2296-2565
ISSN-E: 2296-2565
ISSN-L: 2296-2565
Volume: 10
Article number: 838438
DOI: 10.3389/fpubh.2022.838438
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by KU Leuven: Research Fund (projects C16/15/059, C3/19/053, C32/16/013, and C24/18/022), Industrial Research Fund (Fellowship 13-0260), and several Leuven Research and Development bilateral industrial projects, Flemish Government Agencies: FWO [EOS Project no 30468160 (SeLMA), SBO project S005319N, Infrastructure project I013218N, TBM Project T001919N; Ph.D. Grants (SB/1SA1319N, SB/1S93918, SB/151622)]. This research received funding from the Flemish Government (AI Research Program). BD and XS are affiliated to Leuven.AI–KU Leuven institute for AI, B-3000, Leuven, Belgium. VLAIO [City of Things (COT.2018.018), Ph.D. grants: Baekeland (HBC.20192204) and Innovation mandate (HBC.2019.2209), Industrial Projects (HBC.2018.0405)], European Commission: This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No 885682), (EU H2020-SC1-2016-2017 Grant Agreement No.727721: MIDAS), and KOTK foundation.
EU Grant Number: (727721) MIDAS - Meaningful Integration of Data, Analytics and Services
Copyright information: © 2022 Shi, Nikolic, Fischaber, Black, Rankin, Epelde, Beristain, Alvarez, Arrue, Pita Costa, Grobelnik, Stopar, Pajula, Umer, Poliwoda, Wallace, Carlin, Pääkkönen and De Moor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.