Simulation models to support Intensive Care Unit decision-making in pandemic and non-pandemic times
Authors:   , García de Vicuña Bilbao Daniel 
Published: March 2022

The aim of this thesis is the construction of simulation models to analyse and improve patient admission and inpatient discharge decisions in an Intensive Care Unit (ICU). These decisions are especially relevant in situations of high ICU occupancy because they can lead to the early discharge of an admitted patient or the redirection of a newcomer. Exceptional circumstances, such as the global COVID-19 pandemic that broke out in 2019, increase the need for ICU beds, making this type of study even more relevant. The development of two interactive simulators has enabled us to understand and support ICU decision-making, both in and out of pandemics. The first part of this thesis describes the development of the first ICU Management Flight Simulator, which enables the analysis of physician decision-making in relation to patient admission and discharge issues, while also serving as a useful learning-training tool. The developed Discrete Event Simulation (DES) model mimics real ICU admission and discharge processes, and recreates patients' health status from real clinical data instead of using a single length of stay (LoS) value. This flexible tool, which enables the recreation of ICUs with different characteristics (numbers of beds, patient arrival types, congestion levels...), has been used and validated by ICU physicians and nurses in four hospitals. Preliminary results reveal variability in the decision-making of physicians faced with the dilemma of the last bed, an issue which is addressed in broad terms, paying attention not only to the allocation of the last available ICU bed, but also to the evolving decision-making process surrounding patient admission and discharge as ICU occupancy increases. The simulator is freely available on the internet to be used by anyone interested (https://icusimulator.unavarra.es); only the username (ICU-simulator) and the password (ICU_S1mulat0r*) are required in order to access it. The sequence of decisions made by the simulator users constitute a new type of data for which new distance measures were developed to enable the characterization and clustering of users based on their ICU management approach. The second part of this thesis presents a DES model to support decision-making for the short-term planning of hospital resource needs, especially ICU beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modelling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables representation of the exponential growth in patient arrivals caused by the initial spread of the virus, followed by a period during which the arrival rate peaks before gradually declining until the wave subsides. An empirical study showed that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and is superior in prediction capacity to other sigmoid models based on Richards, Logistic, and Stannard functions. We also propose a new parameterization of the Gompertz growth model to facilitate the creation of patient arrival scenarios in a pandemic simulation. A methodology is proposed for the simulation of future patient arrivals. Patient flow modelling considers different pathways and dynamic LoS estimation in several healthcare stages using patient-level data. We propose an estimation method based on an Expectation-Maximisation algorithm using data from all patients admitted to the hospital to date. Different pandemic waves are simulated in order to compare the performance of this method with that of two other statistical estimators that work only with complete data. Results collected to measure the accuracy of the parameter estimates and their influence when forecasting healthcare resource needs for pandemic patients reveal the superior performance of the new estimation method. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.