Strategic Perspectives in Analytics, Technology and Operations (SPATO) Seminar Series

Artificial Intelligence and Digital Twins for Capacity Management of Intensive Care Units during Epidemic Respiratory Diseases

The Analytics, Technology and Operations Department invite you to join their next seminar in the series Strategic Perspectives in Analytics, Technology and Operations (SPATO) to be held in person in 1.06 Clarendon Building and on teams (link below). 

Title of the Seminar : Artificial Intelligence and Digital Twins for Capacity Management of Intensive Care Units during Epidemic Respiratory Diseases 

Speaker :  Professor Miguel Angel Ortíz Barrios 

Bio: Professor Miguel Angel Ortíz Barrios is a Full Professor in the Productivity and Innovation Department at Universidad de la Costa CUC (Colombia) and research leader of the Lean Decisions FPI since 2020. He is also a Professor in Centro de Investigación en Gestión e Ingeniería de Producción – CIGIP – at Universitat Politècnica de València (Spain). The Ministry of Sciences, Technology, and Innovation in Colombia categorized him as a Senior Researcher. Miguel received his PhD in Engineering and Industrial Production from the Universitat Politècnica de València (Spain). His research is in Multi-criteria Decision Analysis, Supply Chain Management, Discrete-Event Simulation, Digital Twins, Artificial Intelligence, Six Sigma, and Scheduling, and it is mainly focused on healthcare improvement.

Abstract: Seasonal Respiratory Diseases (SRDs) have caused significant operational disruptions in Intensive Care Units (ICUs) worldwide. The situation has been challenging for policymakers, who have been struggling to manage the SRDs evolution, bed capacity constraints, a wide variety of patient profiles, and imbalances within health supply chains. However, hope is not lost, as this seminar presents an Artificial Intelligence (AI) and Discrete-Event Simulation (DES) combined approach to support ICU bed capacity management during SRDs. The proposed approach was validated in a Spanish hospital chain during the most recent COVID-19 pandemic. By identifying the predictors of ICU admission in COVID-19 patients and using Random Forest (RF) to predict ICU admission likelihood, the approach has enabled decision-makers to evaluate new ICU bed configurations that respond to the patient transfer expected from downstream services. As a result, the median bed waiting time decreased between 32.4 and 48.1 minutes after intervention. Therefore, it is high time for policymakers globally to consider adopting AI-powered solutions to manage ICU bed capacity during SRDs effectively.

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