Abstract
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.
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 language | English |
|---|---|
| Article number | 838438 |
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Frontiers in public health |
| Volume | 10 |
| Early online date | 31 Mar 2022 |
| DOIs | |
| Publication status | Published online - 31 Mar 2022 |
Bibliographical note
Publisher Copyright:Copyright © 2022 Shi, Nikolic, Fischaber, Black, Rankin, Epelde, Beristain, Alvarez, Arrue, Pita Costa, Grobelnik, Stopar, Pajula, Umer, Poliwoda, Wallace, Carlin, Pääkkönen and De Moor.
Keywords
- Public health
- Decision support system
- Epidemiology
- Data Visualization
- Machine learning
- epidemiology
- decision support system
- data visualization
- machine learning
- public health
- Decision Making
- Public Health
- Humans
- Information Storage and Retrieval
- Delivery of Health Care
- Health Policy
