{"id":29854,"date":"2025-11-24T09:00:00","date_gmt":"2025-11-24T09:00:00","guid":{"rendered":"https:\/\/cit.upc.edu\/portfolio-item\/prepare-resposta-proactiva-planificacio-eficient-ia-emergencies-hospitalaries-resilients\/"},"modified":"2025-11-25T12:36:08","modified_gmt":"2025-11-25T12:36:08","slug":"prepare-proactive-response-efficient-planning-ai-resilient-hospital-emergencies","status":"publish","type":"portfolio","link":"https:\/\/cit.upc.edu\/en\/portfolio-item\/prepare-proactive-response-efficient-planning-ai-resilient-hospital-emergencies\/","title":{"rendered":"PREPARE: Proactive Response and Efficient Planning with AI for Resilient Emergencies in hospitals"},"content":{"rendered":"\n

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<\/span>–\/–\/2025
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The BIOsignal Analysis for Rehabilitation and Therapy (BIOART) research group at the Universitat Polit\u00e8cnica de Catalunya \u2013 BarcelonaTech (UPC) coordinates the Proactive Response and Efficient Planning with AI for Resilient Emergencies in hospitals (PREPARE) project, which aims to optimise hospital preparedness during emergencies through dynamic capacity and risk assessment using artificial intelligence (AI).<\/h4>
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This project stems from the need to improve the coordination of hospital resources in pandemic situations, enabling the <\/span>anticipation of demand for human resources, equipment and consumables through AI.<\/span><\/p>

The main objective of PREPARE is the real-time prediction of the need for ICU beds and ventilators to enable proactive resource allocation. To achieve this, the following tools have been developed:<\/span><\/p>



Predictive models for ICU demand, mortality risk and patient stratification.<\/span>

Machine-learning models adapted to hospital <\/span>workflows.<\/span>

Real-time <\/span>data integration.<\/span>

Integration with smart hospitals to achieve digital maturity.<\/span>



The models have been validated with datasets from the Khorshid COVID Cohort (KCC) and the Bellvitge University Hospital, ensuring their reliability in real clinical environments. Furthermore, they have been integrated with Hospital Information Systems (HIS) to facilitate their implementation in hospital management. The results have shown high accuracy in predicting COVID-19 mortality risk, with an AUC (Area Under the Curve) of 0.872.<\/span><\/p>

The technology has reached a high level of technological maturity, and its effectiveness has been validated using real-world datasets, confirming its applicability in hospital settings.<\/span><\/p>

Impact<\/h4>

The impact of this innovation is reflected in improved hospital preparedness, better clinical outcomes for patients, and scalable integration with Hospital Information Systems (HIS).<\/span><\/p>

The next steps include full integration with HIS, expanding validation with additional datasets and hospitals, pilot implementation in healthcare environments and ensuring regulatory compliance for deployment.<\/span><\/p>

A patentability study has confirmed its legal and technological feasibility, with a patent application submitted to the EPO.<\/span><\/p>

Budget and Funding<\/h4>

The project involves various stakeholders, including hospital administrators, healthcare professionals, information technology specialists and patients, all of whom benefit from improved hospital management.<\/span><\/p>

PREPARE is funded by the Ag\u00e8ncia de Gesti\u00f3 d\u2019Ajuts Universitaris i de Recerca (AGAUR), with a budget of \u20ac 20,000 and had a duration of 6 months (December 2024 \u2013 June 2025).<\/span><\/p>
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Technology<\/h5>

Digital Transformation<\/a><\/p>
Tecnolog\u00eda
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Health Sector<\/a><\/p>
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Topic<\/h5>

Artificial Intelligence<\/a><\/p>
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