INABED Smart Twins

Optimising hospital management through digital twins and AI

INABED Smart Twins enhances hospital management with digital twins, AI, and predictive models. The project addresses challenges in bed occupancy and resource allocation by using the Care Necessity Index (INA), real-time data analysis, and an interactive visualisation platform.

This innovative solution optimises decision-making in hospital management, providing healthcare managers with an advanced tool based on the combination of technology and clinical data.

The project expects to dramatically reduce waiting times for bed assignments and improve resource allocation. The INA Index allows for a more equitable distribution of the healthcare workload, mitigating staff burnout. The system aims to anticipate saturation hours in advance and decrease unnecessary patient stays through the platform’s dashboards, which will improve response times during critical incidents.

Eurecat contributes expertise in healthcare sector digitalisation and computational modelling through its Digital Health unit, which leads the development of the hospital digital twin. The unit is reponsible for designing the system architecture, simulation models, and the INA Index, as well as coordinating validation in pilot hospitals.

Furthermore, it develops predictive models based on machine learning for time-series analysis, patient clustering, and early warning systems. This work is carried out while ensuring information quality, security, and privacy in compliance with the General Data Protection Regulation (GDPR) and international interoperability standards (HL7/FHIR).

The consortium is led by ChangeTheBlock with the participation of Eurecat and leading hospitals in Peru (Hospital Alberto Leonardo Barton Thompson and Hospital Guillermo Kaelin de la Fuente).