A Survey of Agent-Based Modeling of Hospital Environments

Friesen, Marcia R.; McLeod, R.D.

doi:10.1109/access.2014.2313957

Cited by 19 publications

(4 citation statements)

References 37 publications

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“…Simeone et al 2012) or other forms of flow graphs (Friesen and McLeod 2014) to supply the logic behind the simulated patient flow. They are applied in late stages of planning, i.e.…”

Section: Related Workmentioning

confidence: 98%

MODYPLAN: Early-Stage Hospital Simulation based on Treatment Chains

Wurzer

Lorenz

Rofiler³

et al. 2015

IFAC-PapersOnLine

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Discrete-event simulation of hospitals typically specifies flow by means of a process graph through which patients are routed. While this is generally fine for models in which processes are clearly defined, e.g. smaller units such as emergency departments, it falls short of capturing the fact that a patient can in principle go from one unit to any other unit if medical procedure requires it. This problem becomes even more evident when modeling a whole hospital, at which level specifying each individual treatment through directed edges and nodes becomes unviably complex. In the past three years, we have been developing a combined hospital simulation/space design tool in which processes are defined individually by patient, as sequence of visited units imported from the Hospital Information System (treatment chains). The major advantage of this approach lies in the fact that we can now design a future space layout in which assumed capacities (staffing, equipment, required space) can be tested against the recorded patient volume, using an Agent-Based Simulation to re-enact each treatment chain. In contrast to other hospital simulations, this approach targets early stages of architectural conception, during which the actual building structure is elaborated. Using the method, we can compare and contrast different layouts during that stage, making simulation a driver for design rather than a tool for late optimization within the final floor plan.

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“…Simeone et al 2012) or other forms of flow graphs (Friesen and McLeod 2014) to supply the logic behind the simulated patient flow. They are applied in late stages of planning, i.e.…”

Section: Related Workmentioning

confidence: 98%

MODYPLAN: Early-Stage Hospital Simulation based on Treatment Chains

Wurzer

Lorenz

Rofiler³

et al. 2015

IFAC-PapersOnLine

View full text Add to dashboard Cite

show abstract

“…A review of related works in the area of ABM in health sector can be found in [20]. The case of ED was implemented by agent-based simulations with respect to evacuation [21], optimization scheduling [22], disease propagation analysis [23].…”

Section: Related Workmentioning

confidence: 99%

An Agent-Based Model of a Business Process: The Use Case of a Hospital Emergency Department

Sulis

Leva

2018

Business Process Management Workshops

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An application of Artificial Intelligence is computational simulation which reproduces the behavior of a system, such as an organization. Simulations provide benefits into business process management, also by combining scenarios and what-if analysis. This study explores the adoption of agent-based modeling technique, in addition to traditional discrete event simulations. The focus is on a real case study of an hospital emergency department. Following the construction of a new hospital, managers are interested in simulating the actual flows in the new configuration before the moving. In our model, patients and operators are agents, acting due to simple behavioral rules in the environment. The different activities are placed on the map of the department, to provide immediate understanding of bottlenecks and queues. While first results were validated from managers, next steps include the comparison of resulting flows between the new and the old department. Logistics analysis includes the time for moving agents between different wards.

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“…Nevertheless, hybrid modeling has tremendous potential regarding operational aspects, as demonstrated by Hamza et al, who combined discrete-event and agent-based modeling to simulate the operational patient throughput [ 22 ]. On the same note, Friesen et al have identified agent-based modeling as a simulation modeling paradigm with ‘tremendous potential’ while also pointing out the necessity to combine with other paradigms to construct an accurate simulation model [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Patient Infection Rate on Emergency Department Patient Flow: Hybrid Simulation Study in a Norwegian Case

2023

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The COVID-19 pandemic put emergency departments all over the world under severe and unprecedented distress. Previous methods of evaluating patient flow impact, such as in-situ simulation, tabletop studies, etc., in a rapidly evolving pandemic are prohibitively impractical, time-consuming, costly, and inflexible. For instance, it is challenging to study the patient flow in the emergency department under different infection rates and get insights using in-situ simulation and tabletop studies. Despite circumventing many of these challenges, the simulation modeling approach and hybrid agent-based modeling stand underutilized. This study investigates the impact of increased patient infection rate on the emergency department patient flow by using a developed hybrid agent-based simulation model. This study reports findings on the patient infection rate in different emergency department patient flow configurations. This study’s results quantify and demonstrate that an increase in patient infection rate will lead to an incremental deterioration of the patient flow metrics average length of stay and crowding within the emergency department, especially if the waiting functions are introduced. Along with other findings, it is concluded that waiting functions, including the waiting zone, make the single average length of stay an ineffective measure as it creates a multinomial distribution of several tendencies.

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A Survey of Agent-Based Modeling of Hospital Environments

Cited by 19 publications

References 37 publications

MODYPLAN: Early-Stage Hospital Simulation based on Treatment Chains

MODYPLAN: Early-Stage Hospital Simulation based on Treatment Chains

An Agent-Based Model of a Business Process: The Use Case of a Hospital Emergency Department

The Impact of Patient Infection Rate on Emergency Department Patient Flow: Hybrid Simulation Study in a Norwegian Case

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