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RADAR-CNS

financed

Remote Assessment of Disease and Relapse in Central Nervous System Disorders

Background: Long term conditions require monitoring of patients, traditionally conducted in the clinic, to monitor treatment effects, adverse events and disease course. This can be inefficient and cumbersome – clinic visits may be... see more

31/03/2022

KINGS COLLEGE LOND...

26M€

Project Budget: 26M€

Project leader

KINGS COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

Ver 32 Participantes

Financing granted El organismo H2020 notifico la concesión del proyecto The day 2022-03-31

IMI2-2015-03-01: Remote Assessment of Disease and Re... Scope:Specific Challenge

I+D

Cerrada does 10 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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32 Participantes

KINGS COLLEGE LONDON

4.08M€ | Leader

ECOSISTEMAS 2000

223.56K€ | participant

EMPRESA PROVINCIAL DE AGUAS...

DEPURACION Y TRATAMIENTOS

937.51K€ | participant

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Project duration: 72 months Date Start: 2016-03-31
End date: 2022-03-31

Project leader

KINGS COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 26M€

participation deadline Sin fecha límite de participación.

Project description Background: Long term conditions require monitoring of patients, traditionally conducted in the clinic, to monitor treatment effects, adverse events and disease course. This can be inefficient and cumbersome – clinic visits may be too infrequent to identify individuals at risk of significant changes in disease state (e.g. relapse) and place unnecessary burden on patients and providers. Smartphone and wearable technologies have led to an exponential growth in the amount of information which can be collected on patients unobtrusively and at low cost. Sensors collect data passively, and active monitoring, using experience sampling, provides information on multiple parameters. Such technologies could monitor long term outcomes of patients, at scale, and provide fine-grained information on outcomes, available in real time and at low-cost, enabling services to offer more responsive and efficient care. Whilst there is growing interest in the application of RMT in health, the field is in its infancy. The private-public partnership fostered by IMI2 is an ideal way to overcome the inherent challenges of this field. Our ultimate goal is to improve patient outcomes through remote assessment. To achieve this we will create a pipeline for developing, testing and implementing RMT in depression, multiple sclerosis and epilepsy. The pipeline will include a data management and modelling infrastructure applicable to other disorders and with the flexibility of design to be future-proofed against further technological innovation. We will provide data on implementation barriers and facilitators gleaned from patients, clinicians, regulator and payers which will optimise the pathways for regulatory approval and uptake. Program of Work: In order to test the feasibility and predictive power of RMT four main components are required: (1) Excellent project oversight, management, and a dissemination and exploitation strategy;

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