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EmergAI

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Enhancing emergency department safety, efficacy and cost-effectiveness by artifi...

Enhancing emergency department safety, efficacy and cost-effectiveness by artificial intelligence * Background: Emergency care costs are increasing in developed societies, both in rates of emergency department (ED) visits per person and in costs per visit, and are growing faster than other areas of healthcare spending. With li... ver más

30/09/2027

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UPPSALA UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-08-30

ERC-2021-ADG: ERC ADVANCED GRANTS Scope:Objectives

I+D

Cerrada

0% 100% 100%

Características del participante

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

Información adicional privada

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

1 Participantes

2.50M€ | Lider

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Duración del proyecto: 61 meses Fecha Inicio: 2022-08-30
Fecha Fin: 2027-09-30

Líder del proyecto

UPPSALA UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto * Background: Emergency care costs are increasing in developed societies, both in rates of emergency department (ED) visits per person and in costs per visit, and are growing faster than other areas of healthcare spending. With limited and unstructured data, ED staff make quick decisions about probabilities for multiple diagnoses and risks. Both underestimation and overestimation of these probabilities lead to increased costs and patient harm. Hence, there is desperate need for clinical decision-support systems in the ED. * Aim: To develop a clinical decision support system for emergency medicine doctors, using sensor data, health records data and patient-reported data, validated in a randomized clinical trial, in order to improve the safety, efficacy and cost-effectiveness of emergency care. * Objectives: We will: Develop machine learning (ML)-powered diagnosis and risk prediction algorithms for common and dangerous conditions based on age, sex, presenting complaints, previous diagnoses, ECGs, and vital parameters; develop and validate a patient-centred technical platform for collecting, storing and sharing patient-reported data and three-dimensional symptom drawings; develop ML-powered diagnosis and risk prediction algorithms for common and dangerous conditions based on patient-reported data and symptom drawings; conduct a large-scale prospective ED data collection for internal and external validation of ML models using a common format for online applications and for further data collection; develop a Bayesian network-powered ED-based clinical decision support system that generates probabilities for diagnoses and 30-day mortality risks and suggestions for the most valuable next step, from data in multiple formats, with visual representation of probabilities, risks and uncertainties and Bayes factors for potential next steps; and conduct a randomized clinical trial investigating the usefulness, effectiveness and safety of the new decision support system.

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