Objetivo
Safest, reliable, individualised care of patients at-risk of deterioration needs patients themselves to play an active role in their care whenever possible: late detection or escalation of deterioration causes avoidable harms, and deaths. In this project we will challenge industry to develop robust monitoring and communications systems that connect patients, carers and health professionals, provide early warning of acute deterioration in and out of hospital, and learn and adapt to different individuals in different situations. Wearable sensor technology allows dynamic monitoring of vital signs that indicate health status, while bidirectional video communication allows interaction with the patient and in depth assessment. Self-learning adaptive algorithms interfaced with Electronic Medical Records can provide reliable early warning with few false alarms; and data about individual responses to different therapies.We will first target known at-risk patients such as those on general hospital floors after discharge from Intensive Care or following major surgery, and the frail elderly. This will also enable the safe care of many patients at home, e.g. patients seen in the Emergency Room but judged not to need hospital admission, or those with serious chronic conditions. Reliable, robust monitoring and communication systems will improve patient safety in hospital and after discharge, will decrease avoidable harms and deaths, reduce length of stay and readmissions, and help maintain patient’s independence; providing reassurance of wellness and early warning of deterioration. Analysis of collected ‘big data’ will increase understanding of treatment of specific patient groups, and provide spinoffs such as eHealth applications for chronic conditions. Once mature and integrated in European health care systems, the procured technology can truly transform healthcare by engaging with and empowering all at-risk patients, and enabling their connection with health professionals
Ámbito científico
- medical and health scienceshealth scienceshealth care serviceseHealth
- natural sciencescomputer and information sciencesinternetinternet of things
- medical and health sciencesclinical medicinesurgery
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SC1-2016-CNECT
Régimen de financiación
PCP - Pre-Commercial ProcurementCoordinador
3584 CX Utrecht
Países Bajos