Page content

About the Project

Developed countries face common problems in healthcare including supporting the healthcare needs of an older population; managing multi-morbidity, and in general, the integrated care of people with chronic disease; limiting the rise in healthcare costs; and integrating health and social care. Big data has played a limited role in the health sector compared with other areas.

There are several reasons: the data are heterogeneous, and in individual silos; the data are complex, collected in different ways, and using different techniques; there are real data protection and governance issues to overcome; no tools exist to make these data accessible to end-users, nor to support the necessary complex analytics for non-experts.

MIDAS intends to have a real impact on these problems to improve health, and health care delivery. The ambition of MIDAS centres on architecting, building and provisioning an operational big data platform that enables the policy makers in the project to make more informed decisions based on the actionable insights as derived from a plurality of population-based healthcare data and other related data.

This challenge requires multi-disciplinary research and spans across policy thinking, technology, and advances in how data can be usefully deployed in aiding policy revision. In order for the project to deliver advances across these areas, the key technical challenge is in the development of the MIDAS platform, a solution that uses advanced big data technologies for collecting and visualising heterogeneous healthcare data as well as facilitating simulation and forecasting to aid better decision making amongst policy makers.

Projected key outcomes

  • Delivery of a secure, multi-health site, collaboration architecture and business logic using best practice on privacy by design, ethics and governance, that can be replicated at multiple health-sites
  • Delivery of a data mapping solution that can ingest and map multi-source heterogeneous data to enable data harmonisation, curation and integration across diverse sources, combining the best from health care providers, open data and social media
  • Delivery of privacy-preserving implementations of data analytics, to infer the impact of source variables on policy outcomes, to better assess and draft policy
  • Provision of simulations on the outcomes of health policy decisions, to evaluate the impact of policy options on health care expenditure and delivery, population wellbeing, health and socio-economic inequalities
  • Development of a dashboard for the visualisation of policy models enabling policy makers to use visual analytics to assess the impact of changing variables and indicators on policy decisions