Person: García de Vicuña Bilbao, Daniel
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García de Vicuña Bilbao
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Daniel
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Estadística, Informática y Matemáticas
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0000-0001-7826-3060
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811267
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Publication Open Access Safely learning intensive care unit management by using a management flight simulator(Elsevier, 2020) García de Vicuña Bilbao, Daniel; Esparza, Laida; Mallor Giménez, Fermín; Institute of Smart Cities - ISCThis paper presents the development of the first management flight simulator of an intensive care unit (ICU). It allows analyzing the physician decision-making related to the admission and discharge of patients and it can be used as a learning–training tool. The discrete event simulation model developed mimics real admission and discharge processes in ICUs, and it recreates the health status of the patients by using real clinical data (instead of using a single value for the length of stay). This flexible tool, which allows recreating ICUs with different characteristics (number of beds, type of patients that arrive, congestion level…), has been used and validated by ICU physicians and nurses of four hospitals. We show through preliminary results the variability among physicians in the decision-making concerning the dilemma of the last bed, which is dealt in a broad sense: it is not only about how the last available ICU bed is assigned but also about how the physician makes decisions about the admission and discharge of patients as the ICU is getting full. The simulator is freely available on the internet to be used by any interested user (https://emi-sstcdapp.unavarra.es/ICU-simulator).Publication Open Access Operations research helps public health services managers planning resources in the COVID-19 crisis(Sociedad de Estadística e Investigación Operativa, 2020) García de Vicuña Bilbao, Daniel; Cildoz Esquíroz, Marta; Gastón Romeo, Martín; Azcárate Camio, Cristina; Mallor Giménez, Fermín; Esparza, Laida; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y MatemáticasThis article presents the usefulness of operational research models tosupport the decision-making in management problems on the COVID-19 pandemic. The work describes a discrete event simulation model combined with population growth models, which has been used to provide daily predictions of the needs of ward and intensive care unit beds during the COVID-19 outbreak in the Autonomous Community of Navarre, in Spain. This work also discusses the use of the simulation model in non-acutephases of the pandemic to support decision-making during the return to the normal operation of health services or as a resource management learning tool for health logistic planners.Publication Open Access A management flight simulator of an intensive care unit(IEEE, 2019) García de Vicuña Bilbao, Daniel; Mallor Giménez, Fermín; Esparza, Laida; Mateo, Pedro; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaManagement Flight Simulators (MFS) supply a simulated environment in which managers can learn from experience in a controlled setting. Although its use is usual in other areas, no such software has been developed to learn about the complexity of the Intensive Care Unit (ICU) management. This paper describes an MFS of ICUs which includes main features that distinguish it from other simulators such as the evolution of patients' health status and the recreation of real discharge and admission processes. The mathematical model is a discrete event simulation model in which different types of patients arrive at the ICU (emergency and scheduled patients). The user manages the simulated ICU by deciding about their admission or diversion and which inpatients are discharged. The analysis of recorded data is used to detect controversial scenarios and to understand how physicians' decisions are made.Publication Open Access Hospital preparedness during epidemics using simulation: the case of COVID-19(Springer, 2021) García de Vicuña Bilbao, Daniel; Esparza, Laida; Mallor Giménez, Fermín; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako GobernuaThis paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (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 modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. 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.