Open Access
Assessing the impact of physicians' behavior variability on performance indicators in emergency departments: an agent-based model
(IEEE, 2025-01-20) Baigorri Iguzquiaguirre, Miguel; Cildoz Esquíroz, Marta; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
In emergency departments (EDs), traditional simulation models often overlook the variability in physician practice, assuming uniform service provision. Our study introduces a hybrid agent-based discrete-event simulation (AB-DES) model to capture this variability. Through simulation scenarios based on real ED data, we assess the impact of physician behavior on key performance indicators such as patient waiting times and physician stress levels. Results show significant variability in both individual physician performance and average metrics across scenarios. By integrating physician agent modeling, informed by literature from medical and workplace psychology, our approach offers a more nuanced representation of ED dynamics. This model serves as a foundation for future developments towards digital twins, facilitating real-time ED management. Our findings emphasize the importance of considering physician behavior for accurate performance assessment and optimization.
Open Access
A GRASP-based algorithm for solving the emergency room physician scheduling problem
(Elsevier, 2021) Cildoz Esquíroz, Marta; Mallor Giménez, Fermín; Mateo, Pedro; Institute of Smart Cities - ISC
This paper addresses a physician scheduling problem in an Emergency Room (ER) requiring a long-term work calendar to allocate work days and types of shift among all the doctors. The mathematical model is created without simplifications, using the real calendar, including holidays. This precludes the possibility of cyclic-type solutions, and involves numerous and varied constraints (demand, workload, ergonomics, fairness, etc.). An effective solution to this very difficult practical problem cannot be obtained, for large instances, with exact solution methods. We formulate a mathematical representation of a real-world ER physician scheduling problem featuring a hybrid algorithm combining continuous linear programming with a greedy randomized adaptive search procedure (GRASP). Linear programming is used to model a general physician-demand covering problem, where the solution is used to guide the construction phase of the GRASP, to obtain initial full schedules for subsequent improvement by iterative application of Variable Neighborhood Descent Search (VNDS) and Network Flow Optimization (NFO). A computational study shows the superiority of our approach over the Integer Linear Programming method in a set of instances of varying size and difficulty inspired by a real setting. The methodology is embedded in a software tool for generating one-year-ahead physician schedules for a local ER. These solutions, which are now in use, outperform the manually-created schedules used previously. © 2021 Elsevier B.V.
Open Access
The problem of the last bed: contextualization and a new simulation framework for analyzing physician decisions
(Elsevier, 2019) Azcárate Camio, Cristina; Esparza, Laida; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Faced with a full Intensive Care Unit (ICU), physicians need to decide between turning away a new patient in need of critical care and creating a vacancy by prematurely discharging a current occupant. This dilemma is widely discussed in the medical literature, where the influencing factors are identified, the patient discharge process described and the patient health consequences analyzed. Nevertheless, the existing mathematical models of ICU management practices overlook many of the factors considered by physicians in real-world triage decisions. This paper offers a review of the medical and mathematical literature on patient discharge decisions, and a proposal for a new simulation framework to enable more realistic mathematical modeling of the real-world patient discharge process. Our model includes a) the times at which discharge decisions are made and setup times for patient transfer from the ICU to a general ward and preparation of an ICU bed for an incoming patient, in order to capture the impossibility of an immediate switch of patients; b) advance notice of the number of patients due to arrive from elective surgery requiring intensive postoperative care and potentially triggering the need for early discharges to avoid surgery cancelations; and c) patient health status (to reflect the dependency of physicians’ discharge decisions on health indicators) by modeling length of stay with a phase-type distribution in which a medical meaning is assigned to each state. A simulation-based optimization method is also proposed as a means to obtain optimal discharge decisions as a function of the health status of current patients, the bed occupancy level and the number of planned arrivals from elective surgery over the following days. Optimal decisions should strike a balance between patient rejection and LoS reduction. This new simulation framework generates an optimal discharge policy, which closely resembles real decision-making under a cautious discharge policy, where the frequency of early discharge increases with the ICU occupancy level. This is a contrast with previous simulation models, which consider only the triage of the last bed, disregarding the pressures on physicians faced with high bed occupancy levels.
Open Access
The optimal container selection problem for parts transportation in the automotive sector
(Elsevier, 2024) Cildoz Esquíroz, Marta; Mateo, Pedro; Alonso, María Teresa; Parreño, Francisco; Alvarez-Valdes, Ramon; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Today's automotive factories are essentially assembly plants that receive parts from a vast network of suppliers around the world. Transporting thousands of part types over very long distances is a major logistic problem whose solution is a critical factor in the factory management. In this study we have developed a statistical and optimization methodology implemented in a software tool to help the decision makers select the most appropriate container for each part. A key element is to determine the number of parts that fit in a given container. Two optimization procedures have been developed, depending on the type of part, and used to calculate costs of each container. These costs include not only transporting parts from supplier to factory, but also the costs of handling parts within the factory and returning the empty containers to the supplier.
Open Access
Dynamic mean absolute error as new measure for assessing forecasting errors
(Elsevier, 2018-02-14) Frías Paredes, Laura; Mallor Giménez, Fermín; Gastón Romeo, Martín; León, Teresa; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind energy forecasting are used to illustrate the use of the new DMAE index and show the advantages of this new index over other error indices.
Open Access
Police as first reponders improve out-of-hospital cardiac arrest survival
(BMC, 2023) Jean Louis, Clint; Cildoz Esquíroz, Marta; Echarri Sucunza, Alfredo; Beaumont, Carlos; Mallor Giménez, Fermín; Greif, Robert; Baigorri Iguzquiaguirre, Miguel; Reyero Díez, Diego; Ciencias de la Salud; Osasun Zientziak; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Background: Police forces are abundant circulating and might arrive before the emergency services to Out-of-Hospital-Cardiac-Arrest victims. If properly trained, they can provide basic life support and early defibrillation within minutes, probably increasing the survival of the victims. We evaluated the impact of local police as first responders on the survival rates of out-of-hospital cardiac arrest victims in Navarra, Spain, over 7 years. Methods: A retrospective analysis of an ongoing Out-of-Hospital Cardiac registry to compare the characteristics and survival of Out-of-Hospital-Cardiac-Arrest victims attended to in first place by local police, other first responders, and emergency ambulance services between 2014 and 2020. Results: Of 628 cases, 73.7% were men (aged 68.9 ± 15.8), and 26.3% were women (aged 65,0 ± 14,7 years, p < 0.01). Overall survival of patients attended to by police in the first place was 17.8%, other first responders 17.4% and emergency services 13.5% with no significant differences (p > 0.1). Time to initiating cardiopulmonary resuscitation is significant for survival. When police arrived first and started CPR before the emergency services, they arrived at a mean of 5.4 ± 3 min earlier (SD = 3.10). This early police intervention showed an increase in the probability of survival by 10.1%. Conclusions: The privileged location and the sole amount of personnel of local police forces trained in life support and their fast delivery of defibrillators as first responders can improve the survival of out-of-hospital cardiac arrest victims.
Open Access
Estimation of patient flow in hospitals using up-to-date data: application to bed demand prediction during pandemic waves
(Public Library of Science, 2023) García de Vicuña Bilbao, Daniel; López-Cheda, Ana; Jácome, María Amalia; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.
Open Access
Including learning and forgetting processes in agent-based simulation models: application to police intervention in out-of-hospital cardiac arrests
(Elsevier, 2025-01-01) Baigorri Iguzquiaguirre, Miguel; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Agent-based modeling has become increasingly popular in recent decades; however, defining agents that accurately depict human behavior remains a significant challenge. This paper contributes to the precise definition of human-like agents by incorporating learning and forgetting processes from the medical and psychological literature into agent-based simulation models. Specifically, the mathematical model for forgetting is developed to be compatible with empirical findings. The empirical evidence also supports the decomposition of the learning process into training sessions and the application of skills in real situations, as followed in this model. The resulting model of learning agents is then applied to study police intervention in out-of-hospital cardiac arrests. In numerous urban areas, there's ongoing discussion regarding the provision of defibrillators in patrol cars and CPR training for police officers. The results demonstrate that including learning and forgetting processes in simulation models provide a more accurate understanding of the benefits of using local police to attend cardiac arrests.
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 Matematika
Management 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.
Open Access
I Congreso Salud, Desastres y Desarrollo Sostenible: libro congreso
(2022) Azcárate Camio, Cristina; Cildoz Esquíroz, Marta; Frías Paredes, Laura; Ibarra, Amaia; Galbete Jiménez, Arkaitz; García de Vicuña Bilbao, Daniel; Gastón Romeo, Martín; Moler Cuiral, José Antonio; Mallor Giménez, Fermín; Jean Louis, Clint; Institute of Smart Cities - ISC
El congreso se plantea como un foro de encuentro de investigadores del área de Investigación Operativa con interés en aplicaciones a la salud, los desastres y el desarrollo sostenible, y los profesionales de la toma de decisiones concernientes a los ámbitos anteriores. Este encuentro promueve el intercambio de conocimiento y experiencias entre Universidad y Servicios de Salud para afrontar retos asociados al acceso de la población a unos servicios de salud de calidad y a la gestión del riesgo creciente de desastres naturales o provocados por el ser humano. El envejecimiento de la población y el desarrollo tecnológico plantean nuevos entornos para la provisión de los servicios de salud, en los que su correcta planificación y gestión debe contribuir a garantizar su eficiencia y sostenibilidad. El creciente impacto en términos de vidas humanas y daños económicos causados por desastres naturales y no naturales, como incendios, inundaciones, terremotos, fugas industriales, pandemias, etc. precisa de su comprensión para desarrollar estrategias de prevención y elaborar planes efectivos de respuesta.