Baigorri Iguzquiaguirre, Miguel

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https://academica-e.unavarra.es/handle/2454/52547

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Baigorri Iguzquiaguirre

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Miguel

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Estadística, Informática y Matemáticas

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0000-0003-3791-5733

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1172880

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812923

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2023 - 20253
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Now showing 1 - 3 of 3
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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.