CORDIS

EU research results

CORDIS

Meaningful Integration of Data, Analytics and Services

Fact Sheet

Reporting

Results

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

United Kingdom

Objective

The MIDAS consortium is a partnership involving health authorities in five EU countries and the U.S. and technical big data experts from research institutions, MNCs and SMEs. Managing big data for ‘health in all’ is a monumental challenge for policy makers. MIDAS is addressing this challenge by developing and delivering an integrated solution which will liberate knowledge from data silos and unify heterogeneous big data sources to provide evidence-based actionable information and transform the way care is provided.

Despite the urgent need and opportunity, the level of e-health deployment, to share medical data is very low. Indeed 52% of the hospitals do not share any medical information with external GPs electronically .

MIDAS will map, acquire, manage, model, process and exploit this heterogeneous health care, governmental and open data to provide an innovative world leading beyond state of the art solution which will inform risk stratification and long term policy-making decisions, demonstrating a positive impact across the EU and beyond.

MIDAS will:
• Provide a data mapping solution
• Use open, social media and citizen data for high level policy analysis
• Use technology to exploit the value of big data for actionable information
• Disseminate best practice on privacy by design, ethics and governance
• Make Europe a leader in e-health platforms

Policy makers, patients and citizens will be co-creators of the solutions, ensuring that these are user validated, feedback is integrated, results are actionable and knowledge created to show benefit across a range of epidemiology challenges. Major health challenges which will be addressed include: ageing population, obesity and mental health. Stakeholders will work together to make Europe a leader in e-health solutions, stimulating new opportunities for industry in this innovative field.

Field of Science

ehealth

epidemiology

governance

public services

ethics

big data

Programme(s)

H2020-EU.3.1.5. - Methods and data

Topic(s)

SC1-PM-18-2016 - Big Data supporting Public Health policies

Call for proposal

H2020-SC1-2016-CNECT

See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Coordinator

UNIVERSITY OF ULSTER

Address

Cromore Road
Bt52 1sa Coleraine

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 807 970

Website

Contact the organisation

Participants (14)

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DUBLIN CITY UNIVERSITY

Ireland

EU Contribution

€ 418 737,50

KATHOLIEKE UNIVERSITEIT LEUVEN

Belgium

EU Contribution

€ 525 010

FUNDACION CENTRO DE TECNOLOGIAS DE INTERACCION VISUAL Y COMUNICACIONES VICOMTECH

Spain

EU Contribution

€ 480 000

OULUN YLIOPISTO

Finland

EU Contribution

€ 355 750

ANALYTICS ENGINES LIMITED

United Kingdom

EU Contribution

€ 388 250

QUINTELLIGENCE INTELIGENTNO UPRAVLJANJE Z ZNANJEM DOO

Slovenia

EU Contribution

€ 301 875

REGIONAL BUSINESS SERVICES ORGANISATION

United Kingdom

EU Contribution

€ 55 000

Department of Health

United Kingdom

EU Contribution

€ 118 096,25

FUNDACION VASCA DE INNOVACION E INVESTIGACION SANITARIAS

Spain

EU Contribution

€ 137 500

Teknologian tutkimuskeskus VTT Oy

Finland

EU Contribution

€ 499 423,75

SOUTH EASTERN HEALTH AND SOCIAL CARE TRUST NHS

United Kingdom

EU Contribution

€ 112 500

IBM IRELAND LIMITED

Ireland

EU Contribution

€ 273 750

ARIZONA BOARD OF REGENTS

United States

EU Contribution

€ 30 000

TERVEYDEN JA HYVINVOINNIN LAITOS

Finland

EU Contribution

€ 30 000

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

United Kingdom

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

United Kingdom

Periodic Reporting for period 2 - MIDAS (Meaningful Integration of Data, Analytics and Services)

Reporting period: 2018-05-01 to 2019-07-31

Summary of the context and overall objectives of the project

WP1 provides the overarching administration, management, and coordination of the MIDAS consortium and project. It chairs and coordinates the MIDAS Policy Board. WP2 aims to deliver a solution addressing ethical and good practice challenges when working with large and varied datasets. WP2 will identify requirements, test systems to engage, inform, assure, and create a solution to maximise the benefit of data used for policy and the public. WP3 develops data ingestion logic, designs and deploys the architecture and develop modules for integration and harmonisation of multi-site heterogeneous data sources. is being developed to ensure data quality. WP4 will develop analytics platform for exploratory data analysis, health policy outcome simulation, and privacy-preserving machine learning models to support policy decision-makers in drafting and evaluating policy. WP4 also develops a framework for using systems approaches, thinking, and dynamics to supplement a data-driven approach to support political decision making. WP5 develops an interactive web-based tool to create decision support dashboards for understanding, finding explanations and making decisions from data. Three key tasks: design, implementation and user experience testing with user feedback gathered continuously inform each development iteration. WP6 leads an internal evaluation of the project, involving: identification of potential project impacts and the integration of the platform. This iterative evaluation process employs structured methods and processes. The interim evaluation results are fed back to partners to increase the likelihood of successful project outcomes. WP7 coordinates MIDAS dissemination, communication and exploitation activities, managing the MIDAS website and social media, organising and participating in events, and creating plans to ensure MIDAS results can be exploited by policymakers across Europe. WP7 focus on developing an integrated Exploitation Plan to allow the MIDAS Platform to evolve into a commercially sustainable solution in the longer term. WP8 ensures compliance with the ethics requirements of the project.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

WP1 coordinate and manage the project, utilising the mechanisms from the first reporting period improved upon periodically. Issues and risks are monitored and resolved in conjunction with the Executive Board and wider consortium. WP1 organise and participate in external and internal events consisting of the MIDAS consortium and beyond. WP2 examined the context in which potential models meet the demands of legislative and good practice requirements for intended use of heterogeneous datasets within MIDAS. MIDAS MyData model has been evaluated, and a technical model established with a series of collaborative workshops with MIDAS policy board members and consortium stakeholders by acknowledging the four different MIDAS pilot aims. WP2 is exploring perceptions and testing an innovative model of engagement using technology, like chatbots and Honest Broker models as templates for Good Practice. WP3 research and describe data sources selected in earlier MIDAS iterations. Multi-Health Site Collaboration Architecture and Logic is deployed at all sites. A functionality to deploy GYDRA prepared datasets were implemented for users to meet decision-makers needs. Upon a review of synthetic data simulation, methods were tested with different datasets, and reproducing an algorithm with no source code and good results continue. WP4 continued development of exploratory data analysis API and new crossfilter API analysis for several pilots. The MeSH classifier has been further developed. Data analysis has been applied and validated on two use cases of obesity and type two diabetes. WP5 develops the MIDAS Dashboard using feedback given from the initial prototype. The MIDAS UI was deployed at the pilot sites from where initial feedback was gathered. The second version of the MIDAS Dashboard UI was developed based on the feedback, and updates from version 1 to version 2 was initiated. WP6 has developed the final logic model and a protocol of realist evaluation. The Q method protocol was developed and the first round of the Q method data collection was completed. First round of interviews were undertaken, while second-round interviews are underway. WP7 set up the MIDAS website, dissemination templates, blog, newsletter, Twitter and YouTube Channel. The Policy Board was trained to develop Digital Stories to disseminate MIDAS, and seven Digital Stories have now been published by consortium partners. MIDAS has held several dissemination events and presented posters and papers in a wide and growing number of scientific conferences and journals. The Exploitation Plan V2 was developed to provide a basis for the sustainable MIDAS Open Source Foundation. WP8 via the established Ethics Board and independent ethics advisor continue to monitor the project and review and approve all activities that may have ethical concerns.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

WP1 work with the consortium towards progressing beyond state of the art by providing management structures and processes. WP2 is investigating the needs of the project within current legislative domains and creating an ethical, pragmatic and deliverable model of use and management. The fundamentals in access management and availability of personal data for policymaking with MyData approach needs a human-centred consent management where the purpose of the data use shall be openly presented for personal level, the concept communicated through the regional decision making, and in the policy board collaboration of activities that impacts EU level discussions (e.g. data donation & MyData.org workshops). The Honest Broker model, that utilises a central control and review system for healthcare-related data access for research purposes may offer transferable frameworks as is used in Northern Ireland and be adopted within MIDAS by the Basque region. WP3 describe and pre-process heterogeneous data sources for policy sites, develop tools to analyse and improve quality to define representation and harmonisation of data sources for comparable co-analysis across regions. The Multi-Health Site Collaboration Architecture and Logic will be deployed and further developed to enable privacy-preserving cross-site analytics. WP4 will develop scalable data analytics that incorporates privacy-preserving data mining tools and health policy outcome simulators exposed via an API interface used by the visualisation layer, and will be developed for analysing open data and social media. WP5 will develop dashboard generators that combine results from heterogeneous data sources and analytics from closed environments and the Internet. The system focuses on the visualisation of results from health data, news, MEDLINE abstracts and social media. WP6 has developed an innovative approach to evaluation, linking logic models and methods of framework analysis. A Q-methodology concourse was developed and deployed to succeed initial user interviews. WP7 continue leading dissemination, communication and exploitation efforts to maximise impact of MIDAS. An ambitious goal of WP7 is to create a partner-wide open-source foundation to support the ongoing exploitation and adoption of the MIDAS Platform as an innovative and integrated technical solution for healthcare providers across Europe. WP8 continues to function via the Ethics Board, overseeing and advising on all ethics issues throughout the project.

MIDAS Enhancing Policy Process

MIDAS Framework

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

United Kingdom

Deliverables

Websites, patent fillings, videos etc. (4)

MIDAS Website

The project website will function as the primary source of information available at all times to all stakeholders providing the following: - Project Activities and outcomes; - Repository for external facing project information, reports and deliverables; - Project consortium details and the Horizon 2020 funding mechanism; - Mechanism for contacting the project team and sign-up to project e-newsletter; - Big Data Challenges; The website will make use of an open source mobile responsive framework, such as Twitter Bootstrap, to deliver a high quality device-agnostic, responsive design, user experience. The website and associated social media platforms for MIDAS will make use of appropriate platform tools for digital analytics to measure and report on digital interaction and allow iterative design based on functional test results.

Brochure, Poster and e-Newsletters 1

Information materials will be developed within the brand platform: logos, social media material, e-newsletters, banners, letter templates, power points.

Brochure, Poster and e-Newsletters 2

Information materials will be developed within the brand platform: logos, social media material, e-newsletters, banners, letter templates, power points.

Brochure, Poster and e-Newsletters 3

Information materials will be developed within the brand platform: logos, social media material, e-newsletters, banners, letter templates, power points

Open Research Data Pilot (1)

Documents, reports (4)

Project Plan

MIDAS Framework User Guide

Framework and model for the use of the MIDAS platform. This will address issues highlighted in D1.1. This framework and model will include guides on implementation, engagement, communication and benefits.

Good Practice Report 1

Good Practice Report 1 describing current legislation, good practice and process in respect of consent, access, storage and use of personal data throughout the consortium/ member states.

Visual analytics tool(s) concept V1

Concepts of visualization and visual analytics tools for different stakeholders are defined, including: • Identification of the different stakeholders (e.g. government, national and regional/local health authorities, individuals) and their visualization and analytics needs. The MIDAS Policy Board will play an important role in supporting this work; • Analysis of the data resources and selection of the data variables and indicators for the visualizations. Definitions of information abstraction levels to be able to explore specific impacts in detail; • Selection of the visualization, analysis and other methods (e.g. simulation) for different stakeholders and purposes; • Definition of most critical scenario’s/use cases (Simulation, Forecasting, Policy, Effectiveness assessment, Feedback); • Visual analytics tool specification, including interfaces to data sources, user interfaces and iterative reasoning loop support.

Other (1)

Synthetic Datasets 1

Synthetic data is known as ‘artificial data’ that is simulated from real data using statistical models in order to represent the population yet avoid any divulgence of actual patient records. This task will involve creating synthetic data from the real population datasets that are made available in this project. This artificial data will be simulated using the SynthPop library inside the R programming environment. The synthetic datasets will be validated with the real data by analysing summary statistics and Gaussian distributions. Whilst they are somewhat representative, synthetic datasets avoid various governance and confidentiality issues since real patient or citizen records are not provided or disclosed.

Publications

Conference proceedings (23)

Application of Q-Methodology & Mixed Methods Design to Explore the Utility of a Data Analytic Framework

Author(s): A Boilson, S Gauttier, R Connolly, P Davis, J Connolly, D Weston, A Staines

Published in: Journal of the Operational Research Society, 2019

"""MyData"" potential in public health decisions"

Author(s): Gomes, Julius Francis., Pääkkönen, Jarmo., Iivari, Marika., Kemppainen, Laura., Pikkarainen, Minna.

Published in: 9th Annual EDSI Conference, Issue 3 - 6 June, 2018, 2018

A topological data analysis approach to influenza-like-illness

Author(s): Joao Pita Costa, Primož Škraba, Daniela Paolotti and Ricardo Mexia

Published in: KDD Healthday 2018, 2018

System dynamics modelling in policy level innovation orchestration

Author(s): Pikkarainen, M., Iivari, M., Gomes, J.F., Ranta, J., Ylén, P. and Hurmelinna-Laukkanen, P.

Published in: ISPIM Innovation Symposium, Issue December 2017, 2017

Qualitative Evaluation of a Multi-Level Framework of Technology Acceptance & Use for a European Health Data Analytic Platform

Author(s): A Boilson, A Staines, J Connolly, R Connolly, P Davis, D Weston

Published in: International Journal of Informatics, 2019

Digitalisation of healthcare: Use of data in policy making

Author(s): Iivari, Marika., Gomes, Julius Francis., Pikkarainen, Minna., Häikiö, Juha., Ylén, Peter.

Published in: XXVIII ISPIM Innovation Conference, Issue 18-21 June 2017, 2018

Evaluating Impact of an Emerging Big Health Data Platform: A Logic Model and Q-Methodology Approach

Author(s): Connolly, J., Staines, A., Connolly, R., Boilson, A., Davis, P., Weston, D., Bloodworth, N.

Published in: Entrenova, 2018

Text mining tools to explore the news in the context of public health

Author(s): Joao Pita Costa, Flavio Fuart, Marko Grobelnik, Gregor Leban, Luka Stopar and Paul Carlin

Published in: Slovenian Conference on Data Mining and Data Warehouses (SIKDD 2017), 2017

Digitalisation of healthcare: Use of data in policy making.

Author(s): Iivari, M., Gomes, J.F., Pikkarainen, M., Häikiö, J. and Ylén, P.

Published in: ISPIM Innovation Symposium, Issue June 2017, 2017

BOTter than the real thing

Author(s): Peter Poliwoda, Juha Pajula

Published in: OpenTech AI workshop, Issue 06/05/2019, 2019

"Mining MEDLINE for the visualisation of a global perspective on biomedical knowledge"""

Author(s): Joao Pita Costa, Luka Stopar, Flavio Fuart, Marko Grobelnik, Raghu Santanam, Chen Lu, Paul Carlin, Michaela Black, Johnathan Wallace

Published in: KDD Project Showcase Track, 2018

System dynamics modelling in policy level innovation orchestration

Author(s): Pikkarainen, Minna., Iivari, Marika., Gomes, Julius Francis., Ranta, Jukka., Ylén, Peter., Hurmelinna-Laukkanen, Pia.

Published in: ISPIM Innovation Summit 2017, Issue 10 - 13 December 2017, 2018

Logic Model Early Stage Evaluation of a European Public Health Data Analytic Framework

Author(s): A Boilson, A Staines, R Connolly, P Davis, J Connolly, D Weston

Published in: Journal Informatics, Special Issue (Open Access Journal by MDPI), 2019

Meaningful Integration of Data, Analytics and Services of Computer-Based Medical Systems: The MIDAS Touch

Author(s): Michaela Black, Jonathan Wallace, Debbie Rankin, Paul Carlin, Raymond Bond, Maurice Mulvenna, Brian Cleland, Scott Fischaber, Gorka Epelde, Gorana Nikolic, Juha Pajula, Regina Connolly

Published in: 32nd IEEE CBMS International Symposium on Computer-Based Medical Systems`, 2019

Advancing Open Innovation in Data-Driven Preventive Healthcare

Author(s): Iivari, M. Pikkarainen, M. Francis Gomes, J, Ylen, P. Ranta, J

Published in: 4th World Open Innovation Conference, Issue Dec 2017, 2017

Text mining MEDLINE to support public health

Author(s): Joao Pita Costa, Luka Stopar, Flavio Fuart, Marko Grobelnik, Raghu Santanam, Chen Lu, Paul Carlin, Michaela Black, Johnathan Wallace

Published in: SiKDD 2018, 2018

Q-Method Evaluation of a European Health Data Analytic End User Framework

Author(s): A Boilson, S Gauttier, R Connolly, P Davis, J Connolly, D Weston, A Staines

Published in: ENTRENOVA Conference Proceedings, 2019

Towards the Integration of Prescription Analytics into Health Policy and General Practice

Author(s): Cleland, B., Wallace, J., Bond, R., Black, M., Mulvenna, M., Rankin, D. and Tanney, A.

Published in: International Conference on Innovative Techniques and Applications of Artificial Intelligence, Issue November 2017, 2017, Page(s) 193-206

Q-Method and the Bootstrap - Visualizations

Author(s): A Staines, S Gauttier, R Connolly, P Davis, J Connolly, D Weston, A Boilson

Published in: The International Journal of Q Methodology, 2019

Improving European Healthcare Systems through the Development of a Realist Evaluation Framework for a European Public Health Data Analytic Project

Author(s): A Boilson, R Connolly, A Staines, P Davis, J Connolly, D. Weston

Published in: Biomed Central (BMC) Implementation Science Journal, 2019

Text mining open datasets to support public health

Author(s): Joao Pita Costa, Flavio Fuart, Marko Grobelnik, Gregor Leban, Luka Stopar and Paul Carlin

Published in: Workshop on Information Technologies and Systems (WITS 2017), 2017

Health news bias, public health and digital epidemiology

Author(s): J. Pita Costa, F. Fuart, L. Stopar, M. Grobelnik, D. Mladenić, A. Košmerlj, Evgenia Belayeva, Gregor Leban

Published in: Data and Algorithms Bias, 2018

Q-Methodology Evaluation of a European Health Data Analytic End User Framework

Author(s): A Boilson, S Gauttier, R Connolly, P Davis, J Connolly, D Weston, A Staines

Published in: Abstract Supplement to the European Journal of Public Health, 2019

Peer reviewed articles (1)

Insights into Antidepressant Prescribing Using Open Health Data

Author(s): Brian Cleland, Jonathan Wallace, Raymond Bond, Michaela Black, Maurice Mulvenna, Deborah Rankin, Austin Tanney

Published in: Big Data Research, 2018, ISSN 2214-5796

DOI: 10.1016/j.bdr.2018.02.002

Other (2)

Meaningful Integration of Data, Analytics and Services – Finnish Pilot (Poster)

Author(s): Juha Pajula, Mark van Gils

Published in: EMBEC 2017, Issue June 2017, 2017

The MIDAS platform: facilitating the utilisation of healthcare big data in Northern Ireland and beyond (Poster)

Author(s): Rankin, D., Black, M., Mulvenna, M., Bond, R., Cleland, B.

Published in: 8th Translational Medicine Conference (TMED8), Issue September 2017, 2017

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

United Kingdom

Project information

MIDAS

Grant agreement ID: 727721

Project website

Status

Ongoing project

Start date

1 November 2016
End date

29 February 2020

Funded under:

H2020-EU.3.1.5.

Overall budget:

€ 4 533 862,50
EU contribution

€ 4 533 862,50

Coordinated by:

UNIVERSITY OF ULSTER

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Last update: 19 November 2019

Record number: 205919

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Participants (14)

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Periodic Reporting for period 2 - MIDAS (Meaningful Integration of Data, Analytics and Services)

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