Simulation Optimization for Healthcare Emergency Departments

Cabrera, Eduardo; Taboada, Manel; Iglesias, Ma Luisa; Epelde, Francisco; Luque, Emilio

doi:10.1016/j.procs.2012.04.161

Cited by 65 publications

(31 citation statements)

References 12 publications

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“…However, there are also dashboards which use simulation technologies to inform, guide and predict how clinical care and facility management may be impacted in a variety of scenarios. These have included studies looking at: improving radiation therapy planning (Werker et al 2009); reducing waiting times and delays (Harper and Gamlin 2003;Vanberkel and Blake 2007;Al-Araidah, Boran, and Wahsheh 2012); modelling outpatient departments (Quevedo and Chapilliquén 2015); modelling emergency departments (Zeng et al 2012;Cabrera et al 2012;Brenner et al 2010); maximizing utilization and capacities (Lee et al 2013;Ballard and Kuhl 2006); and measuring the impact of policy changes (Fletcher et al 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies employ a range of simulation tools to achieve the core functionality of their simulation dashboards, such as agent-based modelling (Cabrera et al 2012), SIMUL8 (Harper and Gamlin 2003;Brenner et al 2010;Zeng et al 2012) and Arena (Werker et al 2009;Ballard and Kuhl 2006;Vanberkel and Blake 2007;Quevedo and Chapilliquén 2015;Al-Araidah, Boran, and Wahsheh 2012). SIMUL8 and Arena offer similar discrete-event simulation capabilities for complex systems analysis, such as drug dispensing and patient flows, with Arena additionally providing an option to visualize results in a 3D environment via a dashboard utilizing charts to visualize results as they are generated.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling and simulating hospital operations in a 3D environment

Greenroyd

Price

Demian

et al. 2017

2017 Winter Simulation Conference (WSC)

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modeling and simulating hospital operations in a 3D environment

Greenroyd

Price

Demian

et al. 2017

2017 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

“…The experimental results show that the optimization simulation model optimizes staffing allocation to increase patient throughput and reduce patient waiting times. Cabrera et al [11] presented an agent-based modeling simulation to design a decision support system for healthcare EDs. An exhaustive search is also performed to determine the optimal ED staff configurations, including doctors, triage nurses, and admission personnel.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(11). u ijm or u km represents the utilization rate of each resource, whereas (1−u ijm ) or (1−u km ) indicates the waste rate of each resource and c ij ×(1−u ijm ) or c k ×(1− u km ) corresponds to waste cost.…”

Section: Average Mwc Estimatormentioning

confidence: 99%

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Feng

Chen

2015

Health Care Manag Sci

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The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

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“…Stochastic models are widely used in public health research, predicting a variety of scenarios from propagation (e.g., HIV spread in prisons [13]) to treatment (e.g., optimizing Emergency Department organization [4]). Modeling and simulation software provides healthcare planners with the ability to predict the results of scenarios.…”

Section: Computational Epidemiology and Policymentioning

confidence: 99%

Mining Mobile Datasets to Enable the Fine-grained Stochastic Simulation of Ebola Diffusion

Vogel

Theisen

Leidig

et al. 2015

Procedia Computer Science

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The emergence of Ebola in West Africa is of worldwide public health concern. Successful mitigation of epidemics requires coordinated, well-planned intervention strategies that are specific to the pathogen, transmission modality, population, and available resources. Modeling and simulation in the field of computational epidemiology provides predictions of expected outcomes that are used by public policy planners in setting response strategies.Developing up to date models of population structures, daily activities, and movement has proven challenging for developing countries due to limited governmental resources. Recent collaborations (in 2012 and 2014) with telecom providers have given public health researchers access to Big Data needed to build high-fidelity models. Researchers now have access to billions of anonymized, detailed call data records (CDR) of mobile devices for several West African countries. In addition to official census records, these CDR datasets provide insights into the actual population locations, densities, movement, travel patterns, and migration in hard to reach areas. These datasets allow for the construction of population, activity, and movement models. For the first time, these models provide computational support of health related decision making in these developing areas (via simulation-based studies).New models, datasets, and simulation software were produced to assist in mitigating the continuing outbreak of Ebola. Existing models of disease characteristics, propagation, and progression were updated for the current circulating strain of Ebola. The simulation process required the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract governmental policy level). The predictive results from this system were validated against results from the CDC's high-level predictions.

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Simulation Optimization for Healthcare Emergency Departments

Cited by 65 publications

References 12 publications

Modeling and simulating hospital operations in a 3D environment

Modeling and simulating hospital operations in a 3D environment

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

Mining Mobile Datasets to Enable the Fine-grained Stochastic Simulation of Ebola Diffusion

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