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RADAR-CNS

Financiado

Cerrado

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... ver más

31/03/2022

KINGS COLLEGE LOND...

26M€

Presupuesto del proyecto: 26M€

Líder del proyecto

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

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 32 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-03-31

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

I+D

Cerrada hace 9 años

0% 43% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

32 Participantes

KINGS COLLEGE LONDON

4.08M€ | Lider

ECOSISTEMAS 2000

223.56K€ | Participante

EMPRESA PROVINCIAL DE AGUAS...

DEPURACION Y TRATAMIENTOS

937.51K€ | Participante

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Duración del proyecto: 72 meses Fecha Inicio: 2016-03-31
Fecha Fin: 2022-03-31

Líder del proyecto

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

TRL 4-5

Presupuesto del proyecto 26M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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