Richard Boucherie

Richard J. Boucherie is a full professor of Stochastic Operations Research in the department of Applied Mathematics of the University of Twente and chair of the Dutch Platform for Mathematics. He is co-founder and co-chair of the University of Twente Center for Healthcare Operations Improvement and Research (CHOIR) in the area of healthcare logistics, and co-founder of the spin-off company Rhythm, that carries out actual implementations of healthcare logistics solutions in healthcare organisations. 

Richard received M.Sc. degrees in Mathematics (Stochastic Operations Research) and Theoretical Physics (Statistical Physics) from the Universiteit Leiden, and received the Ph.D. degree in Econometrics from the Vrije Universiteit, Amsterdam. His research interests are in queueing theory, and Markov decision theory, with application areas including wireless and sensor networks, road traffic, network intrusion detection and prevention, and healthcare operations research. 

Richard’s ambition is to organise healthcare such that the right care is delivered to the right patient at the right time by the right healthcare practitioner. Mathematical models are key to realise this ambition.

About the talk: Dynamic assignment of capacity and fair balancing of COVID-19 patients over hospitals

We introduce models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU.  

For given number of available beds, we introduce a dynamic load balancing model for assignment of patients to hospitals within a region, and a stochastic program for allocation of patients across regions. Subsequently, we consider optimal up- and downscaling of capacity for COVID-19 patients leaving maximum capacity for regular (non-COVID) patients.

We illustrate our models using data from the second COVID-19 peak from hospitals' data warehouses and regional infection data as recorded in the Netherlands.