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Is Emergency Department Capitalized

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Received: September 29, 2021 / Revised: April 18, 2022 / Accepted: April 25, 2022 / Published: May 2, 2022

Emergency departments (EDs) have had to dramatically change their patient flow policies in the wake of the COVID-19 pandemic. Such changes affect patient attendance, wait times, and other dynamics related to the patient experience and care. Field experiments, surveys, and simulations can provide general insights into patient flow during pandemic situations. This article provides a comprehensive and transparent account of the development of a multimodal simulation model that simulates real-world patient traffic in the emergency department during COVID-19 pandemic situations. In addition, several practical performance measures for employees are presented. The conceptual model was extracted from key stakeholders in the clinical case through incremental construction and transformed into a statistical model. Two types of agents are the main design: patient and room. The simulation of patient flow has been validated with real-world data (Intelligent Crowding) and can reproduce the actual behavior of the patient utilization rate. To add value, the study recommends a number of phenomena that need to be studied and included in future simulation models such as more agents (doctors, nurses, hospital beds), delays because of the future, etc. collaboration with other departments in the hospital. and the duration of treatment varies greatly. action.

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Emergency departments (EDs) are complex and critical systems for supporting patients with urgent and responsive health needs [1]. Recently, there has been a special focus on the ED in light of the COVID-19 pandemic [2], forcing us to reorganize its structure [3]. Due to the circumstances of the pandemic, EDs have had to streamline their operations while managing their resources effectively.

To comply with the restrictions and guidelines, several management policies are used simultaneously, for example changes in the handling of incoming patients, prioritization of patients with suspected virus, structural changes in patient flow and use of available space. . While such measures have placed an unexpected strain on the department and its critical resources, they are intended to ensure a significant reduction in congestion and maintenance time while keeping the risk of contamination at bay.

In the past, excessive ED has been shown to have a negative impact on patient response time [4, 5, 6, 7, 8]. Excess and duration of treatment can be thought of as functional effects of some typical behaviors and various working conditions, which can be difficult for health professionals to interpret or detect with laboratory tests. Therefore, computer modeling and simulation, especially agent-based modeling, can be useful, as it allows the modeling of organizational participants through their collective behavior [1], 9, 10, 11, 12]. In addition, it can be used to support decision-making and to evaluate interventions, scenarios, operational risks, and the cost-effectiveness of policies.

Overall, there is a growing literature on ED patient flow modeling and simulation [13, 14]. There are several simulation models in which discrete events or an approximate system simulate the flow of ED patients under normal conditions [13]. Similar simulation and modeling methods have also been used to model the spread and transmission of COVID-19 [4] and multiple vaccine applications [15]. However, there is an unmet need for simulation models that compare, interpret, and predict ED patient traffic under pandemic conditions while considering multiple representative behaviors [11], a feature that is not available in discrete event or approaches. operating systems. Furthermore, there is a lack of research demonstrating the rigor and openness of conceptual models of ED, leading to difficulties for readers to understand, evaluate, and build confidence in those models. In addition, from the staff's point of view, many performance metrics used by ED managers (such as duration of treatment, length of stay, % of room occupancy, etc.) have not been considered in existing simulation models.

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How can we build simulations of Emergency Department patient processes during COVID-19 pandemic situations while considering many typical behaviors?

In this paper, we present a detailed description of how we develop such a simulation model. We first describe how we developed a sustainability model based on organizational knowledge and expertise from a case organization. We then show how this conceptual model is integrated into an associative computing model, where an agent-based model is used to model patient behavior and event modeling. ). The modeling methods used include (1) case studies and systems analysis, (2) conceptual modeling, (3) statistical modeling, and (4) verification and validation. In addition, key performance indicators (KPIs) for patient flow used by clinicians are integrated and visualized.

The rest of this article is organized as follows: Materials and Methods The following briefly summarizes the four-step simulation model used, the basic principles of the main methods used, and then the application, case study description (procedure, application) and input data (incoming patient data). Next, the Results section provides a hard copy of the conceptual model, including the purpose, KPIs, interfaces, process flow, and sequential interactions between actors. In addition, computational modeling and comparison between simulated data and real-world data will be presented. Finally, the Discussion section will go over the implications and possible conclusions from the results, and the Conclusion section will provide final, concluding remarks of the research results.

This chapter will introduce modeling methods and test cases. First, the simulation modeling method and the method of collecting and analyzing model inputs and features will be presented for both conceptual modeling and statistical modeling. Next, we will present our case organization—the emergency department of Stavanger University Hospital in Norway—and the case data we used to build, validate, and validate the models. .

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In the main literature on modeling and simulation, there are many methodological approaches to perform simulation studies [16]. In this study, we follow the process described by Randers' [17], dividing the modeling process into four main stages: (1) concept, (2) construction, (3) experimental testing and (4) implementation (see Figure 1 ).

Randers system was chosen because it focuses on the purpose of the model rather than a single problem and the conclusion as the initial prediction for the model. Most importantly, Randers method is perhaps the most independent of the modeling techniques available from the research literature. Therefore, in the context of a hybrid model that combines two or more models (discrete event and agent-based models) into a simulation study, the Randers Method is considered the best. The strategic aspects that will be primarily focused on below are the parts of the "concept" and "construction" steps. The last two steps are beyond the scope of this paper but the authors will pursue in the next paper.

In a previous paper [18], we developed and presented a general model for an ED, simulating the patient flow process before a pandemic situation. In this study, the model is expanded and presented in more detail to include patient processes in the context of a pandemic. As a general model, the extension of this model is done in collaboration with the case organization through several meetings. As suggested in the literature, this phase is started before any statistical modeling [19].

Conceptual modeling follows the four steps of Albin's [20] process to build conceptual models, shown in Figure 2. The four steps are: “Step 1 – Determine the purpose of the model”, “Step 2 – Execute determination of boundaries and their variables", "Step 3 - Describing the behavior of the systems" and "Step 4 - Describing the principles of operation of the systems". Although the steps here are numbered 1 to 4, that it does not imply the need for a strict chronological launch.The general simulation model process is a

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