The predictive and preventative mindset of the concept is an opportunity to personalize healthcare delivery in Europe. The integration of personal data donation together with big data analytical tools offers technological capabilities to empower and engage people in their own health and life, while simultaneously introducing market potentials for digital health solutions.
Increasing sophistication and volume of collected healthcare data provides an opportunity for earlier identification of disease. Specifically, patterns in routinely collected healthcare data can be revealed to find and estimate risk of disease for undiagnosed patients. The use of past patient data, such as medical and prescription claims, to drive future diagnoses can help a greater number of patients receive proper care and treatment. This type of patient disease modelling and diagnostic prediction is made possible by artificial intelligence and machine learning.
Integration of validated models based on big data technologies for prediction, prevention and intervention would use multiple available data resources in personalized health and care pathways. This, delivered at the point of care, would enable the healthcare provider to evaluate the right course of action in real time and empower individuals to actively contribute to risk mitigation, prevention and targeted intervention, improving outcomes and reducing healthcare costs. FEMaLE will develop three CDS (clinical decision support) tools to be tested in various clinical settings to demonstrate the power, potential, scope and utility of the Scalable Multi-Omics Platform (SMOP), facilitating precision medicine. Such CDS tools will be more predictive of individual disease risks and likely response to therapy, therefore having higher clinical utility.
FEMaLE will develop and demonstrate the Scalable Multi-Omics Platform (SMOP) that converts multi-omics person population datasets into a personalized predictive model to improve intervention along the continuum of care for people with endometriosis.