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There are five pilot studies within the iHelp designed to help test and validate the personalised healthcare solution developed in the project. Pilot settings cover from community based (general population) to hospital based (patients). All pilots provide different aspects in relation to disease prevention, personalised health monitoring and decision support. More specifically, Pilot#1 (UNIMAN) focuses on pancreatic cancer risk prediction and risk mitigation. In the risk mitigation process, biomarkers will be assessed and be used as part of motivational change in health habits. Pilot#2 (FPG) focuses on predict outcomes and toxicity using real-world data (RWD) analysis approach. Data on Patient Reported Experience Measures (PREMs) and Patient-Reported Outcome Measures (PROMs) for pancreatic cancer patients with indication to radiotherapy will be systematically collected using a mobile application connected with Internet of Thing (IoT) devices. Pilot#3 (HDM) sets to investigate lifestyle choices on elevating the Risk Factors for Pancreatic Cancer. In addition, this pilot will employ case-control study design to explore new as well as established risk factors associate with pancreatic cancer. Pilot#4 (MUP) will also investigate factors associated with pancreatic risk to derive personalised recommendations and measures to raise awareness. Finally, Pilot#5 (TMU) will explore factors that predict high-risk individuals towards pancreatic and liver cancer for early-stage management of the disease and further explore the effect of digital therapeutics solutions among these individuals. This deliverable describes details of each pilot in more in-depth.
intervention measures, pancreatic cancer, Coordination of pilot scenarios for personalised healthcare – early risk identification, prevention and intervention measures, iHelp, personalised healthcare, early risk identification
intervention measures, pancreatic cancer, Coordination of pilot scenarios for personalised healthcare – early risk identification, prevention and intervention measures, iHelp, personalised healthcare, early risk identification
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