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
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
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
Coping with stress in emergency department physicians through improved patient-flow management
(Elsevier, 2020) Cildoz Esquíroz, Marta; Ibarra, Amaia; Mallor Giménez, Fermín; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Estadística, Informática y Matemáticas
This paper provides a method for the real-time monitoring of job stress in emergency department (ED) physicians. It is implemented in a Decision Support System (DSS) designed for patient-to-physician assignment after triage. Our concept of job stress includes not only the workload but also time pressure and uncertainty. A job stress function is estimated based on the consensus views of ED physicians obtained through a novel methodology involving stress factor analysis, questionnaire design, and the statistical analysis of expert opinions. The resulting stress score enables the assessment of job stress using workload data from the ED physicians’ whiteboard. These data can be used for the real-time measurement and monitoring of ED physician job stress in a stochastic and dynamic environment, which is the main novelty of this method as compared to previous workload and stress measurement proposals. A further advantage of this methodology is that it is general enough to be adapted to physician job stress monitoring in any ED. The use of the DSS for ED patient-flow management reduces job stress and spreads it more evenly among the whole team of physicians, while also improving other important ED performance measures such as arrival-to-provider time and the percentage of compliance with patient waiting time targets. A case study illustrates the application of the methodology for the construction of a stress-score, the monitoring of physician stress levels, and ED patient-flow management.
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
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 Matemáticas
This 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.
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.
Open Access
Early detection of new pandemic waves: control chart and a new surveillance index
(Public Library of Science, 2024) Cildoz Esquíroz, Marta; Gastón Romeo, Martín; Frías Paredes, Laura; García de Vicuña Bilbao, Daniel; Azcárate Camio, Cristina; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
The COVID-19 pandemic highlights the pressing need for constant surveillance, updating of the response plan in post-peak periods and readiness for the possibility of new waves of the pandemic. A short initial period of steady rise in the number of new cases is sometimes followed by one of exponential growth. Systematic public health surveillance of the pandemic should signal an alert in the event of change in epidemic activity within the community to inform public health policy makers of the need to control a potential outbreak. The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with a new surveillance metric to overcome some of their difficulties in capturing the changing dynamics of the pandemic. At statistically-founded threshold values, the new measure will trigger alert signals giving early warning of the onset of a new pandemic wave. We define a new index, the weighted cumulative incidence index, based on the daily new-case count. We model the infection spread rate at two levels, inside and outside homes, which explains the overdispersion observed in the data. The seasonal component of real data, due to the public surveillance system, is incorporated into the statistical analysis. Probabilistic analysis enables the construction of a Control Chart for monitoring index variability and setting automatic alert thresholds for new pandemic waves. Both the new index and the control chart have been implemented with the aid of a computational tool developed in R, and used daily by the Navarre Government (Spain) for virus propagation surveillance during post-peak periods. Automated monitoring generates daily reports showing the areas whose control charts issue an alert. The new index reacts sooner to data trend changes preluding new pandemic waves, than the standard surveillance index based on the 14-day notification rate of reported COVID-19 cases per 100,000 population.
Open Access
Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage
(SAGE, 2023) Cildoz Esquíroz, Marta; Ibarra Bolt, Amaya; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians’ assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.
Open Access
Simulation and optimization methods to improve the management of resources and patients in health services. Application to emergency departments
(2019) Cildoz Esquíroz, Marta; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Gobierno de Navarra / Nafarroako Gobernua
The aim of this thesis is to contribute to the sustainability of public health services by means of data analysis and through the development and application of Operational Research methods and techniques for modeling and analyzing real planning and management problems generally affecting the public health sector and Emergency Departments (EDs) in particular. The focus of the research is on the development of methods of analysis that will yield practicable solutions to improve the efficiency and quality of patient care and working conditions of the health staff. A hospital ED provides medical and/or surgical care to patients arriving in need of immediate attention. The highly stochastic environment of these departments is especially difficult to manage due to the variability of the patient arrival rate, patient severity, and (material and human) health resource requirements. They also have to provide a 24/7 service, where physicians are required to work night, day and weekend shifts, and take on different assignments.reflecting the resource consumption (including the medical staff) required for treatment. A guideline is provided for the construction of a mathematical model of the ED designed to overcome some of the shortcomings of oversimplified queuing theorymodels and capture some important issues that previous simulation models have overlooked. The first part of the thesis addresses the problem of patient-to-physician allocation following triage. It offers a proposal for new allocation rules which prove to outperform the common cyclic allocation approach by taking into account a factor usually neglected by patient-flow management policies: i.e., the workload stress experienced by physicians, which is measured in real time using a method proposed and analyzed in this thesis. The stress score is used as the KPI to assess the performance of current patient-flow management policies and as a criterion for designing new ones. This thesis also illustrates the successful implementation of one of the proposed rules, from initial concept to practical application in the hospital. The tested allocation rule outperforms the current cyclic one, as demonstrated by using the simulation model and analysis of the real data gathered during the pilot test. The second part of the thesis addresses the physician scheduling problem, which is a combinatorial optimization problem posing particular difficulty when all the constraints and objectives observed in practice are considered. The problem is modeled by means of mathematical programming, and thus cannot be solved in practice by commercial software. This leads to the development of a new solution heuristic. A key feature of this algorithm is the greedy constructive phase, which is guided by solving a linear problem in combination with a memory structure. Initial good solutions are very quickly obtained, but they can be unfeasible in heavily constrained cases. The subsequent improvement phase combines a repair strategy based on variable neighborhood search with network optimization. This is the first proposal for such a strategy. A computational analysis and a real-case solution demonstrate the quality of the solutions and the good behavior of the methodology. The research presented in this thesis fulfills the following objectives: to propose a quantitative framework (based on simulation models and their combination with optimization procedures) for the analysis of problems involved in the dimensioning and assessment of management policies in hospital emergency services; to develop a methodology for the real-time assessment of pending workload stress in physicians; to provide new patient-to-physician allocation methods with criteria including the workload and stress balancing across physicians, and patient service quality; to analyze alternatives to pure priority rules for managing the queue of patients awaiting initial emergency assessment by a physician or reevaluation following tests and/or diagnosis; to design efficient algorithms for solving the physician work-shift assignment problem taking into account all real ergonomic constraints while balancing the workload.