System Architecture of a European Platform for Health Policy Decision Making: MIDAS

Xi Shi (Corresponding Author), Gorana Nikolic, Scott Fischaber, Michaela Black, Debbie Rankin, Gorka Epelde, Andoni Beristain, Roberto Alvarez, Monica Arrue, Joao Pita Costa, Marko Grobelnik, Luka Stopar, Juha Pajula, Adil Umer, Peter Poliwoda, Jonathan Wallace, Paul Carlin, Jarmo Pääkkönen, Bart De Moor

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number838438
Number of pages13
JournalFrontiers in Public Health
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


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.


  • data visualization
  • decision support system
  • epidemiology
  • machine learning
  • public health
  • Decision Making
  • Public Health
  • Delivery of Health Care
  • Humans
  • Information Storage and Retrieval
  • Health Policy


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