Simulating emergency patient flow during the COVID-19 pandemic

Bovim, Thomas Reiten; Gullhav, Anders N.; Andersson, Henrik; Dale, Jostien; Karlsen, K.

doi:10.1080/17477778.2021.2015259

Cited by 17 publications

(18 citation statements)

References 19 publications

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“…Simpy has a process based simulation worldview and has continued to be maintained (last update Apr 2020). It has been used in several Operations Research relevant publications (Bovim, Gullhav, Andersson, Dale, and Karlsen 2021, Allen, Bhanji, Willemsen, Dudfield, Logan, and Monks 2020, Monks, Harper, Anagnostou, and Taylor 2022).…”

Section: Python Discrete-event Simulation Toolsmentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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Section: Python Discrete-event Simulation Toolsmentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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“…The scenarios’ outputs deliver a useful tool to assist health authorities in planning different actions against COVID-19 spread. Bovim et al. (2021) use a DES modeling approach to build decision support tool for the hospital management while preparing to COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid RFID-IoT simulation modeling approach for analyzing scrubs’ distribution solutions in operating rooms

Maïzi

Bendavid

2023

BPMJ

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PurposeAssess the realistic impacts of implementing an Radio Frequency Identification (RFID)/Internet of Things (IoT) uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms. The authors use a hybrid simulation framework to address the following objectives and challenges: a) reduce and control operating rooms’ level of inventory; b) stabilize scrubs’ demand and c) improve infection control and prevention of cross-contamination (through scrubs over manipulation and hoarding).Design/methodology/approachThe authors adopt a Design Science approach. This methodological approach is used to design, develop, create and evaluate information technology “artifacts” (e.g. constructs, models, methods and instantiations) intended to solve organizational problems and make research contributions (Peffers et al., 2007). More specifically, the authors follow the Design Science Research Methodology process model which includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication.FindingsTo assess the realistic impacts of implementing an RFID-IoT uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms, the authors adopted a design science approach and initiated the research by documenting the business case and reviewed the existing literature to build a comparative analysis of existing uniforms’ distribution systems. The authors used a hybrid simulation model to assess the impact of three business cases: present mode of operation, implementing smart shelves or the smart distributors. The authors show that smart dispensers allow a greater control on scrubs’ utilization while eliminating the cross-contamination of the medical personnel.Practical implicationsThrough this research study, the authors provide hospitals’ managers a scientific support for uniforms’ (scrubs) distribution process improvement. The authors use a hybrid simulation model to compare innovative solutions for uniforms’ distribution systems in the form of “smart cabinets” supported by Radio Frequency Identification (RFID)/Internet of Things (IoT) technologies and choose the most appropriate design for the hospital to meet two main challenges: a) inefficiency of uniform replenishment-distribution system and b) noncompliancy with infection control regulations caused by the distribution system.Originality/valueFrom a methodological perspective, this paper addresses concerns from researchers calling quantitative research methods and using case-based research strategy to address IoT issues and assess the system in practice. From a broader point of view, this work confirms the predominant interest of RFID-IoT research work in the arena of supply chain management and logistics as the technology is used for tracking purpose and for monitoring applications. It is also one response to the research community suggesting that “hospitals should evaluate the medical effectiveness of the new technologies as well as the cost before adoption”.

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“…system simulation, we use a 2-day warm-up period and run for 7 more days (24 hours a day). The example is adapted from [2] and [58]. Table 1 in the Section A.2 of [53] shows the distributions of the service times at each stage of the process.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The performance of each design depends on the context, so the top-m designs are context-dependent. For example, in the medical resource allocation of emergency departments during the COVID-19 pandemic, good allocation designs shall vary with emergency patient flows in response to evolving situation in the pandemic [1], [2]. In other fields such as marketing and finance, contextual information can help companies achieve more revenues and user satisfaction by providing personalized pricing, advertisements, promotions, and webpage layouts based on user locations, demographics, and interests [3], [4].…”

Section: Introductionmentioning

confidence: 99%