Digital representation of health data to improve disease diagnosis and treatment

Topic description

Scope:

Specific challenge: Digital personalised models, tools and standards with application for some specific clinical targets are currently available. There is however a need for greater integration of patient information, for example of multi-scale and multi-level physiological models with current and historical patient specific data and population specific data, to generate new clinical information for patient management. Any such integrative digital representation (Digital Patient) must also allow meaningful knowledge extraction and decision support.

Scope: Proposals should focus on new decision support systems (DSS) based on a complex integration of heterogeneous data sources and subject-specific computer models. This should enable an integrated data analysis, and should present a highly visual data representation, using user-friendly interactive exploratory interfaces in order to assure usability and acceptability.

Proposals should enable the use of DSS by healthcare professionals for personalised prediction and decision in prevention, diagnosis or treatment and should take into account data protection and ethical considerations, as well as those pertaining to the inherent uncertainties and limits of prediction. The models should be already available, multi-level and multi-scale and will be integrated with the individual and population data relevant for the targeted clinical situations, e.g.  the required molecular and cellular data, including genomics and epi-genomics, in vivo and in vitro imaging data, or data on administration of therapeutics and on nutrition/exposure to environmental factors and will be linked when relevant with computer models of personalised physiology, functional disorders and other diseases. The proposed systems should take advantage of the personal medical data accumulated over time. Proposals should include the standardisation of data formats. The integration of data coming from other new technologies for e.g. key-enabling technologies should be considered. Gender and ethical issues should be duly considered.

The Commission considers that proposals requesting a contribution from the EU of between EUR 3 and 5 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.

Expected impact:

• Better coherent use of health data available for a subject in conjunction with the existing medical knowledge in clinical decision making
• Design of predictive and therapeutic interventions
• Better management of complex clinical situations.
• Enabling use of the same information by different medical services and the other relevant healthcare professionals.
• Better control and inter-service coordination in the management of the patient health.
• Providing a consistent view of a patient's care needs.

Type of action: Research and innovation actions

Cross-cutting Key-Enabling Technologies (KETs)
Gender