CogniTwinED: Transforming Emergency Care Through Cognitive Digital Twins

CogniTwinED is an innovative research project integrating the emerging Cognitive Digital Twin (CDT) technology with AI to transform emergency care from reactive to proactive practice.

The research develops, integrates, and validates a Cognitive Digital Twin (CDT) framework that dynamically models, analyses, and predicts complex patient flow and resource utilisation in Emergency Departments (ED), to reduce waiting times and enhance patient safety and operational efficiency. By creating an intelligent virtual twin of ED environments that continuously learns and reacts, the system enables near real-time monitoring, simulation, predictive analytics, and decision-making support. This fusion of cognitive intelligence and digital twin in ED settings represents a transformative step toward smarter, adaptive, and resilient emergency care systems.

The research continuously harnesses data from heterogeneous sources, such as Electronic Health Records, BIM-based space data, Building Management System outputs, hybrid simulation models, and learned experiential data from patient flow and operational flow patterns. The continuous learning and reasoning mechanism aims to identify bottlenecks in patient flow before they occur within the dynamic complexity of operational flows, thereby providing a solution to the growing problem of overcrowding and waiting times in EDs, causing a higher risk of patient mortality. CogniTwinED also supports scenario-based decision testing, helping hospitals evaluate strategies before real-world implementation.

Project Team: Northumbria University, Newcastle University, OKANA, Tektology, Newcastle Hospitals NHS Foundation Trust, Royal Victoria Infirmary Emergency Department.