# A Systematic Literature Review on Simulation Models for Healthcare

Sofia Abadía, William Derigent, Virginie Goepp

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Sofia Abadía, William Derigent, Virginie Goepp. A Systematic Literature Review on Simulation Models for Healthcare. Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, Sep 2024, Augsburg, Germany. pp.119-131, 10.1007/978-3-031-85316-6_9.hal-05156302

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# A Systematic Literature Review on Simulation Models for Healthcare

Sofia Abadía¹,², William Derigent¹ and Virginie Goepp²

1. CRAN, Université de Lorraine, Campus Sciences BP 70239, Vandœuvre-lès-Nancy, France

2. ICube INSA Strasbourg, 24 boulevard de la victoire, Strasbourg, France

{sofia.abadia-bermeo,william.derigent}@univ-lorraine.fr

virginie.goepp@insa-strasbourg.fr

**Abstract.** In the healthcare sector, the use of simulation models is pervasive and multifaceted, serving a range of purposes, including but not limited to supporting decision-making, resource allocation, and reducing overcrowding of facilities. These models may be connected to the physical world in various ways, which allows them to be integrated into other systems, such as the Digital Twin (DT). Nevertheless, in the case of DTs, the simulation model needs to be updated quickly to ensure accuracy, a task that is not straightforward when a specific simulation model with a high level of detail is required. To deal with this stake reusable models with twinning features could be a solution. As a result, a review is conducted to study existing simulation models in the healthcare sector and to assess their twinning process as well as their reusability and abstraction level. The main conclusions of this review, including 26 papers are the following. Firstly, most of the models are specific and therefore difficult to reuse. Secondly, generalisable models are not being used to develop specific models. Thirdly, the updating phase of simulation models, being part of a DT, is never mentioned.

**Keywords:** Simulation modeling, Generic model, Generalisable model, Discrete event simulation, Agent-based simulation, Digital Twin

## 1 Introduction and research issues

Simulation and modelling have been widely used in the healthcare context as decision support systems for stakeholders [1], helping with various problems such as resource allocation, both physical and financial, overcrowding of facilities and satisfaction of patients, among others. These models can vary from specific to generic models depending on their purpose and their level of detail. Boyle et al. [2] defines 3 levels of abstraction for healthcare simulation models as follows 1) specific 2) generalisable 3) generic, where specific models are highly detailed but time and effort consuming, while generic ones do not provide detail but are easier and quicker to develop. In addition to the abstraction level, simulation models can also have different types of connections between the physical and the cyber world (unidirectional, bidirectional) that can allow them to be used in Digital Shadows or Digital Twins (DTs) [3].
Nonetheless, the implementation of DTs in the healthcare sector is still scarce. Even though DTs could bring benefits in terms of hospital operational efficiency, the healthcare sector is critical, and all decisions taken by the system must be reviewed and examined before execution. To obtain a certain degree of automation while maintaining the human as a central part of the system, the ANR JUNEAU Project aims at developing a human-based DT, where the recommendation or decision taken by the DT is reviewed by a doctor and, if accepted, is automatically implemented.

To make quick and relevant recommendations, the human-based DT needs data-driven simulation models that are always up to date. Nevertheless, the construction of a detailed simulation model is time and effort consuming as specified by Boyle et al. [2]. Even more so, it is not easy to reuse them for other problems because specific models focus on precise problems. As such, it is in the interest of the project 1) to identify how current simulation models are built, if they can be reused and how and 2) to study the connection that current simulation models have with the physical world.

Other reviews have been made for simulation models in the healthcare context focusing either on simulation techniques [4], [5] or on the abstraction level of the models [1], [2]. Nonetheless, these reviews do not present information regarding the physical and cyber world connection of the simulation models or the relationship between the abstraction level and the reusability of the model. As such, this article presents a review on simulation models in the healthcare context, placing special attention on the abstraction level of the models and their reusability, and taking into consideration the twinning process, i.e., “the act of synchronizing the states of the physical and virtual entity/twin” [6].

The structure of the article is as follows: Section 2 details the methodology followed for the systematic literature review. Section 3 presents the literature synthesis and analysis. Section 4 is dedicated to discussion, where the main findings of the analysis are explained. Finally, Section 5 gives the conclusions of the review and the future work.

# 2 Systematic literature review: Methodology

A Systematic Literature Review (SLR) has been conducted, following the methodology proposed by vom Brocke [7]. The iterative process proposed by this methodology and the detailed and clear explanations it provides are the main reasons for selecting it.

The methodology consists of 5 steps, 1) Definition of review scope, 2) Conceptualization of topic, 3) Literature search, 4) Literature analysis and synthesis and 5) Research agenda. The first three steps are explained in the following subsections and the remaining steps are presented in Section 3 and 5 respectively.

## 2.1 Definition of review scope

During the first step of the methodology, the purpose is to characterize the literature review. For this, vom Brocke [7] proposes 6 points which are described below, along with their application for our review:
1. Focus: main target of the investigation. The investigation conducted focuses on the healthcare context, specifically on the current simulation models that are used/proposed by the literature.

2. Goal: final objective of the literature review. The aim of this SLR is to gather information about the reusability of simulation models in the Healthcare sector and its relationship with their abstraction levels. It also aims to study the model's twinning characteristics.

3. Organization: method for organizing the review. The evaluation framework shown in **Table 1**, presented at subsection 2.2 serves as the base for the review.

4. Perspective: biased or unbiased position of the researcher. All the authors possess a neutral position for this investigation.

5. Audience: intended audience for which the review is directed. The review is intended to be read and consulted by academics specialized in simulation models and Digital Twins.

6. Coverage: level of coverage of sources. The investigation presents representative coverage of the simulation models in the healthcare context.

## 2.2 Conceptualization of the topic

A framework is developed for the evaluation and analysis of the selected articles, (see **Table 1**). It is composed of three categories, 1) Context, 2) Model and 3) Twinning. The first category *Context* aims at providing general information about the paper along with its purpose. The second category *Model* aims at collecting all information regarding the model proposed by the article (modelling technique, abstraction level and update features). Finally, the third category *Twinning* seeks to detail the connection between the physical and cyber worlds to underline the real-time capabilities, or the type of data exchanged.

<table><caption>**Table 1.** Evaluation framework</caption><thead><tr><th>Category</th><th>Criterion</th><th>Research question</th></tr></thead><tbody><tr><td rowspan="4">Context</td><td>Purpose</td><td>What is the purpose of the model?</td></tr><tr><td>Sector</td><td>For which sector is the model applied to?</td></tr><tr><td>System of interest</td><td>Which is the physical/real system that is being modeled?</td></tr><tr><td>Type of problem</td><td>Which is the problem that the model aims to relieve?</td></tr><tr><td rowspan="4">Model</td><td>Modelling technique</td><td>Which modelling technique is used?</td></tr><tr><td>Abstraction level</td><td>Which is the level of abstraction of the model? (generic, generalisable or specific) [2]</td></tr><tr><td>Update</td><td>How can the model be updated and when?</td></tr><tr><td>Reusability</td><td>How can the model be reused and under which conditions?</td></tr><tr><td>Twinning</td><td>Data exchange</td><td>What data is being exchanged?</td></tr></tbody></table>
<table><tr><td>Type</td><td>What type of twinning is supported by the model? (Digital Twin, human-based Digital Twin, Digital Shadow, Digital Model) [3], [8]</td></tr><tr><td>Connection</td><td>What kind of connection do the physical and the virtual world have? (No connection, Unidirectional, Bidirectional) [8]</td></tr><tr><td>Real time</td><td>Is the connection between the physical and virtual worlds in real time? How is it made?</td></tr></table>

## 2.3 Literature search

Having developed the evaluation framework, the literature search is performed using the search engines Scopus and Web of Science. The query defined has two groups of keywords, 1) the simulation model's keywords and 2) the context/sector of the simulation models. The focus is on discrete event simulation models for the healthcare sector. The query for the search is presented below.

TITLE-ABS-KEY ( ( "DISCRETE EVENT SIMULATION" OR "DES" OR "SIMULATION MODELS" OR "BEHAVIOUR MODELS" OR "BEHAVIOR MODELS" ) AND ( "HEALTHCARE" OR "ED" OR "EMERGENCY DEPARTMENT" ) ) AND PUBYEAR > 2016 AND PUBYEAR < 2024

The query gives 442 results in Scopus and 214 in Web of Science. Once the results have been gathered, an elimination of duplicates is conducted, resulting in 489 articles. The first screening is performed by title and source / type of publication; only articles published in journals are considered. At the end of this process 281 articles in total are selected. The second screening is conducted by keywords and abstract, prioritizing articles focusing on Emergency Departments and generic/generalisable models, resulting in 46 articles total. With this set of articles, the review started, however while reading 9 articles were found irrelevant for the investigation as they either focus on a really specific problem or do not provide a simulation model, 5 articles are not found and 6 articles are reviews that are not analyzed with the evaluation framework but that helped identify what has been already done and what is missing. As a result, 26 articles were analyzed.

## 3 Literature analysis and synthesis

Considering the time range of the query from 2016 to 2024, Fig. 1 presents the number of articles per year. As can be seen, from the selected group of articles none of them were published in 2016, 2017 nor 2019. The year in which most of the selected articles were published is 2023 with 9 articles, followed by 2018 with 5 articles published.

<caption>Fig. 1. Articles per year</caption>
<figure><img src="image_2.png" /></figure>

Having the general context of the selected articles, we go further to their analysis and synthesis. The analysis of the articles is made based on the Evaluation Framework detailed in **Table 1**, according to its 3 main categories, 1) Context, 2) Model and 3) Twinning.

## 3.1 Context

All the selected articles focus on the healthcare context, but they can have different fields of application as can be seen in **Table 2**. The main sector to which the models are applied is the Emergency Department, a critical area in hospitals and difficult to simulate because of the ever-changing flow of patients. Models are also applied to hospital wards and specific treatment departments. In these cases, the simulation can be conducted in an easier way, because these wards and departments work on programmed consultations, so it is not common to receive unexpected patient flow. For example, [9] presents a model for financial resource allocation on a rheumatology clinic and its simulation is made based on the consultations and treatment appointments, which contains all the necessary information regarding the required rooms as well as the appointed doctors and nurses.

<table><caption>**Table 2.** Sector vs Purpose</caption><thead><tr><th></th><th>Resource allocation</th><th>Financial resource allocation</th></tr></thead><tbody><tr><td>Emergency department</td><td>[1], [2], [10], [11], [12], [13], [14], [15]</td><td></td></tr><tr><td>Specific treatments</td><td></td><td>[9]</td></tr><tr><td>Specific clinics</td><td>[16], [17], [18]</td><td>[19]</td></tr><tr><td>Whole hospital</td><td>[20], [21], [22], [23], [24]</td><td></td></tr><tr><td>Healthcare system</td><td>[25], [26]</td><td></td></tr><tr><td>Emergency Medical Services</td><td>[27]</td><td></td></tr></tbody></table>

Regarding the purpose and the type of problem the models deal with, the most significant problem is the resource allocation, mentioned in 23 out of the 26 articles. This concerns both physical resources such as hospital beds, rooms, equipment and human resources such as doctors and nurses within a unit. [18] presents a specific problem of
resource allocation, as it focuses on balancing the occupancy levels of the clinic. The other problems mentioned are financial resource allocation and occupancy levels.

These problems lead to the purposes of the models, among which are the enhancement of efficiency, prioritization problem, staff utilization and scheduling. For each purpose certain Key Performance Indicators (KPIs) have more relevance than others. For example, in the case of enhancement of efficiency, KPIs such as patient throughput, length of stay and waiting time are the most important.

## 3.2 Model

The models proposed by the articles vary in multiple aspects, such as the level of abstraction, the reusability and the modelling technique. It is important to note that in [9] the process of development of a simulation model is given, along with its requirements, but no final model is presented. As such, its abstraction level, development and reusability cannot be determined.

In the case of the modelling technique, most of the articles use Discrete Event Simulation (DES). The second most used modelling technique is hybrid simulation in which two common techniques are merged, DES and Agent-based simulation (ABS). This allows for a more complete simulation, as DES allows the simulation of events while ABS provides a simulation of the agents that participate in the process, such as doctors, nurses and patients.

Along with the modelling technique, the system requires data processing techniques with which the data collected can be prepared, processed and analyzed. The techniques mentioned by the articles are data and process mining [28] and machine learning [27]. The simulation model can also be accompanied by other techniques that focus on more specific objectives. For example, if the aim of the model is also optimization, then multi-objective optimization [23] or goal programming [10] can be used.

<table><caption>**Table 3.** Abstraction level of the models</caption><thead><tr><td>Abstraction level</td><td>Articles</td></tr></thead><tbody><tr><td>Generic</td><td>[1], [20], [21], [22], [25]</td></tr><tr><td>Generalisable</td><td>[2], [14], [23], [26], [29]</td></tr><tr><td>Specific</td><td>[10], [11], [12], [13], [15], [16], [17], [18], [19], [24], [27], [28], [30], [31], [32]</td></tr></tbody></table>

In terms of the abstraction levels of the models, there are only few generic or generalisable models. In 15 out of 26 cases models are specific, generic and generalisable models being only proposed in 5 papers each (see **Table 3**). The specific models are difficult to replicate and/or reuse in other cases, whereas generic and generalisable models are, in most of the cases, accompanied by frameworks, guiding the construction of the model. **Table 4** presents how models are developed in each of the articles. 14 out of 26 articles do not provide any guidelines to develop the model proposed, while 3 follow existing methodologies or frameworks.
<table><caption>**Table 4.** Model development</caption><thead><tr><th>Abstraction level</th><th>Propose and follow a framework</th><th>Follow existing framework</th><th>No mention</th></tr></thead><tbody><tr><td>Generic</td><td>[1], [21], [22], [25]</td><td></td><td>[20]</td></tr><tr><td>Generalisable</td><td>[2], [14], [29]</td><td>[23]</td><td>[26]</td></tr><tr><td>Specific</td><td>[30]</td><td>[11], [15]</td><td>[10], [12], [13], [16], [17], [18], [19], [24], [27], [28], [31], [32]</td></tr></tbody></table>

Continuing with the topic of reusability, most of the articles do not provide a detailed explanation of the development process or if the model can be reused and how. This is specifically the case for the articles that propose specific models. There are, however, some cases for which the development of the model is not fully described, but the simulation code can be downloaded [10], [18] (see **Table 5**). Most of the articles proposing generic or generalisable models (7 out 10 articles) provide a framework for the construction of these models. Nevertheless, the framework does not come with additional information like the simulation code and the mechanisms for model reusability are not detailed.

<table><caption>**Table 5.** Reusability of the models</caption><thead><tr><th>Abstraction level</th><th>Framework</th><th>Code</th><th>Neither</th></tr></thead><tbody><tr><td>Generic</td><td>[1], [21], [22], [25]</td><td>[22]</td><td>[20]</td></tr><tr><td>Generalisable</td><td>[2], [14], [29]</td><td>[26]</td><td>[23]</td></tr><tr><td>Specific</td><td>[30]</td><td>[10], [18]</td><td>[11], [12], [13], [15], [16], [17], [19], [24], [27], [28], [31], [32]</td></tr></tbody></table>

## 3.3 Twinning

The twinning category aims at identifying the connection between the physical and the cyber world and how it works. The connection between the physical and the cyber world is directly related to the type of system. The first option would be a simulation model that does not have any connection, in which case, it would be a digital model where any change on the physical world will not affect the cyber world and vice versa [8]. The second case is the unidirectional connection, which refers to a digital shadow. The digital shadow only has a connection from the physical to the cyber world, which means that there is a unidirectional data transfer from the physical to the cyber world [8]. In the last case, the model has a bidirectional connection, resulting in a digital twin where the cyber world also has a link with the physical world, data being transferred in both directions [8]. In the case of a bidirectional connection, it is important to note that the human-based DT also has this type of connection.

Regarding this, most of the articles present a digital shadow (see **Fig. 2**), where, based on the simulation results, the system can provide certain recommendations, but
no action is automatically executed. The human operators in charge need to review the
simulations and the recommendations and make a final decision.

One interesting finding is that only one article considers a DT and even then, it is not fully a DT but a human-based DT, where the validation of a human is required before continuing with the process. The DT has a bidirectional connection between the physical and the cyber world, which means that decisions can be made and executed automatically. As such, it is expected that no autonomous DT is used in the healthcare sector as the decisions need to be thoroughly studied before being taken and the executions need to be followed with great care. Nonetheless, the topic of human-based DT could be more widely studied for this sector as it requires human validation but also provides a level of automation that can help improve the efficiency of the hospital, ward or department.

<figure><img src="image_3.png" /><figcaption>Fig. 2. Type of system – connection</figcaption></figure>

Regarding the exchange of data performed by the systems, it is related to the type of connection between the physical and the cyber worlds. For all models that possess a unidirectional connection, the exchange of data only happens during the transfer of the physical world data to the cyber part of the system. In the case where the models do not have any connection, there is no automatic exchange of data and, consequently, it is not in real time.

However, real time plays an important role in data transfer, especially for Emergency Departments. The transfer of data in real time can enhance the simulations of the hospital ward or department. Nevertheless, information regarding this topic is seldom found in the papers.

# 4 Discussion

The synthesis and analysis of the selected articles provide important insights, particularly regarding the twinning process. Firstly, the main type of connection used by the models is the unidirectional connection. It is not surprising that the connections are mainly unidirectional, given that the models are implemented in the healthcare context.
Any failure in this sector is critical, as it deals with the health of people, for this reason all decisions need to be thoroughly studied and analyzed. Nevertheless, the lack of automation hinders different improvements such as the rapid allocation of resources or the decrease of waiting times.

For this point, the use of the human-based digital twin could be interesting, as it allows both the much-needed validation of a human and a connection from the cyber to the physical world for automation enhancement. Nonetheless, this system is only mentioned in one of the articles [30] and very few information can be found regarding this type of system.

Regarding the Model category, this review agrees with the result obtained on existing reviews in terms of the genericity level of the simulation models and concludes that models are in most cases context-specific. Having a specific model allows for a complete and detailed simulation of the physical system, but at the same time it decreases the reusing chances for other problems or contexts. To make reusability possible, complete information on the development of the model or its code is required. Our analysis shows that it is not the case. It is not common for articles to detail the development process of the simulation model and even less to provide a code of the simulation model. The articles present the purpose of the model along with the objectives but there is little information regarding 1) the necessary data for creating the model, 2) the data collection techniques, 3) the data processing and 4) the development process. The code of the simulation model is only provided in 4 articles [10], [18], [22], [26].

Finally, the quality of the data is the main factor that hinders both the model and model process development. The healthcare sector has important problems with the quality of the data, and it is common to find discrepancies in the records of the hospital regarding the times of a patient's path flow and the start or finish of tasks. Taking this into consideration, to obtain more accurate results in the simulations it would be necessary to improve the quality of the data collected. Nevertheless, this task is not easy because the registration of data depends completely on the hospital equipment, inner workflows and staff. As such, the systems and models require robust data processing techniques to obtain the best and most accurate set of data that helps it operate to the best of their capacities.

# 5 Conclusions and future work

This article reviews simulation models in the context of healthcare, with a special focus on the emergency departments. It particularly takes into consideration the type of model (e.g. Abstraction level) and the twinning process. An SLR is conducted over a set of 26 journal articles from Scopus and Web of Science with the purpose of analyzing the reusability of the models and its relationship with the abstraction levels along with the twinning process of the models.

The results of the SLR helps drawing three main conclusions. Firstly, most of the simulation models are at the specific level, which limits their reusability. Secondly, existing generalisable models are not being utilized to construct specific models. However, some frameworks or toolboxes have been developed for the development of
generic and generalisable models. These tools need to be studied in detail to ascertain
their suitability for the development of specific models. Third, most of the models lack
a connection between the cyber and the physical world. This means that there is limited
use of DTs in healthcare, and the update of simulation models that belong to a DT is
not mentioned. Consequently, it is necessary to investigate other sectors that deal with
DT and/or human-based DT updates.

The full development of a generalisable simulation model for the human-based dig-
ital twin can be considered for future work. The simulation model will be part of the
ANR JUNEAU project, which has as objective the development of a human-based DT
for the pediatric emergency department of the CHU Centre Hospitalier Universitaire de
Saint-Étienne in France.

Acknowledgements

The authors would like to thank the Agence Nationale de la Recherche of the French
government for funding this work, which is supported by the JUNEAU project ANR-
22-CE46-0010 running from October 2022 to October 2026.

The authors would like to thank the Région Grand Est for funding this work, which
is supported by the Data-To-Twin project running from February 2024 to January 2027.

References

1. N. Furian, D. Neubacher, M. O'Sullivan, C. Walker, and C. Pizzera, "GEDMod – Towards a generic toolkit for emergency department modeling," *Simul. Model. Pract. Theory*, vol. 87, pp. 239–273, Sep. 2018, doi: 10.1016/j.simpat.2018.07.010.

2. L. M. Boyle, A. H. Marshall, and M. Mackay, "A framework for developing generalisable discrete event simulation models of hospital emergency departments," *Eur. J. Oper. Res.*, vol. 302, no. 1, pp. 337–347, Oct. 2022, doi: 10.1016/j.ejor.2021.12.033.

3. C. Semeraro, M. Lezoche, H. Panetto, and M. Dassisti, "Digital twin paradigm: A systematic literature review," *Comput. Ind.*, vol. 130, p. 103469, Sep. 2021, doi: 10.1016/j.compind.2021.103469.

4. A. Salmon, S. Rachuba, S. Briscoe, and M. Pitt, "A structured literature review of simulation modelling applied to Emergency Departments: Current patterns and emerging trends," *Oper. Res. Health Care*, vol. 19, pp. 1–13, Dec. 2018, doi: 10.1016/j.orhc.2018.01.001.

5. E. Doudareva and M. Carter, "Discrete event simulation for emergency department model-
ling: A systematic review of validation methods," *Oper. Res. Health Care*, vol. 33, p.
100340, Jun. 2022, doi: 10.1016/j.orhc.2022.100340.

6. D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, "Characterising the Digital Twin: A systematic literature review," *CIRP J. Manuf. Sci. Technol.*, vol. 29, pp. 36–52, May 2020, doi: 10.1016/j.cirpj.2020.02.002.

7. J. vom Brocke, A. Simons, B. Niehaves, K. Riemer, R. Plattfaut, and A. Cleven, “Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process,” presented at the Proceedings of the European Conference on Information Systems., 2009.
8. A. Fuller, Z. Fan, C. Day, and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," *IEEE Access*, vol. 8, pp. 108952–108971, 2020, doi: 10.1109/ACCESS.2020.2998358.

9. A. Botchkarev, "An Overview of the Conceptual Simulation Modelling for Optimizing Funds Allocation in Health Care: Hip and Knee Replacement Case," *Appl. Model. Simul.*, vol. 5, no. 0, pp. 145–155, Jun. 2021.

10. A. U. Rehman, Y. S. Usmani, S. H. Mian, M. H. Abidi, and H. Alkhalefah, "Simulation and Goal Programming Approach to Improve Public Hospital Emergency Department Resource Allocation," *Systems*, vol. 11, no. 9, p. 467, Sep. 2023, doi: 10.3390/systems11090467.

11. I. Munira Ibrahim, C.-Y. Liong, S. Abu Bakar, and A. Farid Najmudd, "Performance Improvement of the Yellow Zone in Emergency Department using Discrete Event Simulation Approach," *Int. J. Eng. Technol.*, vol. 7, no. 4.33, p. 102, Dec. 2018, doi: 10.14419/ijet.v7i4.33.23509.

12. E. Perez, V. Anandhan, and C. Novoa, "A Simulation-Based Planning Methodology for Decreasing Patient Waiting Times in Pure Walk-In Clinics:," *Int. J. Inf. Syst. Serv. Sect.*, vol. 12, no. 3, pp. 34–54, Jul. 2020, doi: 10.4018/IJISSS.2020070103.

13. N. Hamza, M. A. Majid, and F. Hujainah, "SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department," *IEEE Access*, vol. 9, pp. 103419–103439, 2021, doi: 10.1109/ACCESS.2021.3098625.

14. Y. Liu, T. Moyaux, G. Bouleux, and V. Cheutet, "Hybrid Simulation Modelling of Emergency Departments for Resource Scheduling," *J. Simul.*, pp. 1–16, Mar. 2023, doi: 10.1080/17477778.2023.2187321.

15. G. Terning, E. C. Brun, and I. El-Thalji, "Modeling Patient Flow in an Emergency Department under COVID-19 Pandemic Conditions: A Hybrid Modeling Approach," *Healthcare*, vol. 10, no. 5, p. 840, May 2022, doi: 10.3390/healthcare10050840.

16. W. H. Fun *et al.*, "Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System," *Healthcare*, vol. 10, no. 2, p. 189, Jan. 2022, doi: 10.3390/healthcare10020189.

17. J. J. Forbus and D. Berleant, "Using Discrete-Event Simulation to Balance Staff Allocation and Patient Flow between Clinic and Surgery," *Modelling*, vol. 4, no. 4, pp. 567–584, Nov. 2023, doi: 10.3390/modelling4040032.

18. M. Kakad, M. Utley, and F. A. Dahl, "Using stochastic simulation modelling to study occupancy levels of decentralised admission avoidance units in Norway," *Health Syst.*, vol. 12, no. 3, pp. 317–331, Jul. 2023, doi: 10.1080/20476965.2023.2174453.

19. T. Tagimacruz, M. Cepoiu-Martin, and D. A. Marshall, "Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic," *Health Syst.*, pp. 1–11, Jun. 2023, doi: 10.1080/20476965.2023.2219293.

20. K. Gulhane, A. Khan, and R. Joshi, "ENHANCING OPERATIONAL EFFICIENCY OF HOSPITALS USING DISCRETE EVENT SIMULATION." Accessed: Feb. 28, 2024. [Online]. Available: https://www.acade-mia.edu/search?q=ENHANCING%20OPERATIONAL%20EFFICIENCY%20OF%20HO SPITALS%20USING%20DISCRETE%20EVENT%20SIMULATION&utf8=%E2%9C% 93

21. H.-J. Shin, K.-W. Cho, and C.-H. Oh, "SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0," *Wirel. Commun. Mob. Comput.*, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/5795037.
22. M. L. Penn, T. Monks, A. A. Kazmierska, and M. R. A. R. Alkoheji, "Towards generic modelling of hospital wards: Reuse and redevelopment of simple models," *J. Simul.*, vol. 14, no. 2, pp. 107-118, Apr. 2020, doi: 10.1080/17477778.2019.1664264.

23. Z. Rahimi Rise and M. M. Ershadi, "An integrated HFMEA simulation-based multi-objective optimisation model to improve the performances of hospitals: A case study," *J. Simul.*, vol. 17, no. 4, pp. 422-443, Jul. 2023, doi: 10.1080/17477778.2021.2015260.

24. G. J. Melman, A. K. Parlikad, and E. A. B. Cameron, "Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation," *Health Care Manag. Sci.*, vol. 24, no. 2, pp. 356-374, Jun. 2021, doi: 10.1007/s10729-021-09548-2.

25. I. Djitog, H. O. Aliyu, and M. K. Traoré, "A model-driven framework for multi-paradigm modeling and holistic simulation of healthcare systems," *SIMULATION*, vol. 94, no. 3, pp. 235-257, Mar. 2018, doi: 10.1177/0037549717744888.

26. J. M. Tyler, B. J. Murch, C. Vasilakis, and R. M. Wood, "Improving uptake of simulation in healthcare: User-driven development of an open-source tool for modelling patient flow," *J. Simul.*, vol. 17, no. 6, pp. 765-782, Nov. 2023, doi: 10.1080/17477778.2022.2081521.

27. D. Olave-Rojas and S. Nickel, "Modeling a pre-hospital emergency medical service using hybrid simulation and a machine learning approach," *Simul. Model. Pract. Theory*, vol. 109, p. 102302, May 2021, doi: 10.1016/j.simpat.2021.102302.

28. O. Tamburis and C. Esposito, "Process mining as support to simulation modeling: A hospital-based case study," *Simul. Model. Pract. Theory*, vol. 104, p. 102149, Nov. 2020, doi: 10.1016/j.simpat.2020.102149.

29. S. V. Kovalchuk, A. A. Funkner, O. G. Metsker, and A. N. Yakovlev, "Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification," *J. Biomed. Inform.*, vol. 82, pp. 128-142, Jun. 2018, doi: 10.1016/j.jbi.2018.05.004.

30. P. A. Boareto et al., "A hybrid model to support decision making in the stroke clinical pathway," *Simul. Model. Pract. Theory*, vol. 120, p. 102602, Nov. 2022, doi: 10.1016/j.simpat.2022.102602.

31. R. R. Corsini, A. Costa, S. Fichera, and A. Pluchino, "A configurable computer simulation model for reducing patient waiting time in oncology departments," *Health Syst.*, vol. 12, no. 2, pp. 208-222, Apr. 2023, doi: 10.1080/20476965.2022.2030655.

32. H. Hassanzadeh et al., "A discrete event simulation for improving operating theatre efficiency," *Int. J. Health Plann. Manage.*, vol. 38, no. 2, pp. 360-379, Mar. 2023, doi: 10.1002/hpm.3589.