IMPROVE Living Lab

Lead: Universidad Politécnica de Madrid August 2024

D3.3 Scientific, policies and practices development V1

D3.3 sets out the foundations for the trackers that keep the Knowledge Warehouse current. Led by UPM, it gives practical guidance on how the scientific, policies and practices trackers are developed, executed and maintained, and defines the structure the later versions build on. It closes by pointing forward to the technical detail and the automation that subsequent deliverables in the series take up.

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Lead: Universidad Politécnica de Madrid December 2024

D3.5 Data Dashboard V1

The first version of the dashboard deliverable defines and conceptualises the IMPROVE dashboard: the core tool for putting value-based healthcare principles into practice and monitoring them across very different use cases. It sets out a structured methodology for making sure the design answers the project’s objectives, the requirements stakeholders stated, and the specific needs of each clinical setting where the dashboard will actually be used.

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Lead: Universidad Politécnica de Madrid July 2025

D3.8 Data Dashboard V2

The IMPROVE project has taken another important step towards enabling value-based healthcare through digital innovation. D3.8 presents the latest progress in the design and development of the IMPROVE dashboard — a central tool for collecting, analysing and visualising patient-generated health data. Building on D3.5, this second version reflects iterative, user-centred development with the partners running the use cases in oncology, ophthalmology, cardiovascular disease, neurology and chronic inflammation.

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Lead: PredictBy December 2024

D3.7 Scientific, policies and practices development V2

The second iteration of the tracker work adds technical depth and opens the question of automation. It describes how artificial intelligence and machine learning — in particular large language models — could be used to identify current scientific methods, outcomes and policies worldwide, and keep the trackers up to date without manual re-screening. It is equally clear that preliminary analysis is needed to establish the right frameworks before any model is trained.

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