be4you
Title:BE4YOU: Predictive health & wellness analytics platform
Project Code:LISBOA-01-0247-FEDER-046993 and CENTRO-01-0247-FEDER-046993
Main Objective: Strengthen research, technological development and innovation
Financing Operational Program: OP Lisboa and OP Centro
Region of intervention:Lisbon and Center
Beneficiary entity:AgileFactor; INOV INESC; Germano de Sousa; EIA; Segursal
Approval date:2021-12-09
Start date:01-12-2021
End date: 30-06-2023
Eligible Cost:247.331,12€ (Total)
Financial support from European Union – FEDER:707.940,68€ (Total)
Project to collect individual data, treated and processed technologically, in order to calculate the health risk indicators of each person, providing preventive actions through a follow-up plan.
Objective
The evolution of the overall health & wellness picture allows for the development of more assertive and tailored action plans that support behavioral re-education and follow-up/guidance for laboratory tests and exams relevant to each person’s profile.
The be4you project aims to develop an innovative service platform whose primary target audience is adults (45+ years old), and which will work on active prevention and the promotion of appropriate habits and behaviors, thus lowering the overall health risk of the community members.
Impact
To decrease the risk of occurrence of acute clinical episodes, and the inherent reduction of costs associated with healthcare treatment, contributing to the improvement of the health & well-being of each person. Create a be4you health & wellness community based on the positive reinforcement of behaviors and habits that fit each person’s profile.
Monitor and promote the evolution of the community member’s well-being, according to the characteristics of the profile he or she fits into, benefiting from access to services and products provided by community partners. This is an approach in line with the disintermediation trends in the age of digital transformation.
INOV Participation
Implementation of a communication protocol – interaction between the central system and the RAS (Risk-Assessment System); selection and implementation of analytical models, complemented with gamification mechanisms. Apply Machine Learning techniques and combined approaches (Data Analytic and Predictive Modelling) that culminate in a dynamic and proactive service platform with information on risk level. Implementation of an interactive Dashboard-type graphical interface.