Archives of Academic Emergency Medicine. 2022; 10(1): e44 OR I G I N A L RE S E A RC H Optimization of Service Process in Emergency Depart- ment Using Discrete Event Simulation and Machine Learning Algorithm Sayyed_Morteza Hosseini_Shokouh1,2, Kasra Mohammadi3, Maryam Yaghoubi1∗ 1. Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. 2. Faculty of Health, Baqiyatallah University of Medical Sciences, Tehran, Iran. 3. Industrial Management Department, Allame Tabataba’i University, Tehran, Iran. Received: March 2022; Accepted: May 2022; Published online: 8 June 2022 Abstract: Introduction: Emergency departments are operating with limited resources and high levels of unexpected re- quests. This study aimed to minimize patients’ waiting time and the percentage of units’ engagement to improve the emergency department (ED) efficiency. Methods: A comprehensive combination method involving Discrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Ge- netic Algorithm (GA) was used in this study. After simulating the case and making sure about the validity of the model, experiments were designed to study the effects of change in individuals and equipment on the average time that patients wait, as well as units’ engagement in ED. Objective functions determined using Artificial Neu- ral Network algorithm and MATLAB software were used to train it. Finally, after estimating objective functions and adding related constraints to the problem, a fractional Genetic Algorithm was used to solve the model. Re- sults: According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units’ doctors, were in an optimized condition, but the triage unit, as well as the fast track units’ doctors, should be optimized. After experiments in which the average waiting time in the triage section reached near zero, the average waiting time in the screening section was reduced to 158.97 minutes and also the coefficient of units’ engagement in both sections were 69% and 84%, respectively. Conclusion: Using the ser- vice optimization method creates a significant improvement in patient’s waiting time and stream at emergency departments, which is made possible through appropriate allocation of the human and material resources. Keywords: Efficiency; Emergency Service, Hospital; Operations Research; Patients Cite this article as: Hosseini_Shokouh SM, Mohammadi K, Yaghoubi M. Optimization of Service Process in Emergency De- partment Using Discrete Event Simulation and Machine Learning Algorithm . Arch Acad Emerg Med. 2022; 10(1): e44. https://doi.org/10.22037/aaem.v10i1.1545. 1. Introduction Hospital is one of the most important sectors of healthcare and the emergency department (ED) is considered as one of the most critical and crowded departments of hospitals (1). Hospitals’ managers are constantly trying to control rising costs, while responding to the growing demand for health care. As a result, they need to regularly review the efficiency of EDs to find opportunities for improvement (2). High re- ∗Corresponding Author: Maryam Yaghoubi; Nosrati Alley, South Sheykhba- haee Ave., Mollasadra Ave., Vanak Square, Tehran, Iran. PO Box. 14359164471, Email: yaghoobbi997@gmail.com, Tel: +982187555474, ORCID: http://orcid.org/0000-0002-2138-4205. ferral rate and limited resources (physician, nurse, etc.) have clarified the importance of optimization methods in the pro- cess of evaluation and resource utilization in the health sys- tem, and specifically in EDs (3). The number of patients who refer to EDs has a steadily grow- ing trend. There was a 30% growth in ED visits in France from 2002 to 2012. EDs were one of the most crowded de- partments of a hospital during the COVID-19 pandemic and played a very important role in therapeutic response to the COVID-19 pandemic (4, 5). The elderly, limited access to medical care from other resources, and the high rate of use of EDs for non-emergency care are the main factors leading to the growing number of patients in the EDs (6). The development of health infrastructures and appropriate This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem SM. Hosseini_Shokouh et al. 2 allocation of resources will play a very important role in the implementation of general health policies (7). In the case of healthcare organizations, the improvement efforts, and thus the decisions, are concentrated on a system that aims to pro- vide high-quality care, appropriate service times, and is also efficient in the use of resources. However, designing and op- erating these systems, especially EDs, are extremely compli- cated, mainly due to the high number of different resources involved in the activities of providing care, the uncertainty resulting from these activities happening at different mo- ments, and the distinguished probability of simultaneously requiring resources (8). As a result, long patient waiting times and overcrowding are common problems in EDs all over the world. Optimization of patients’ stream and bottleneck elim- ination in the ED could provide a solution that lessens the cost and raises the quality of care (4). Designing an appropri- ate allocation plan for human and material resources should be considered as one of the most important tasks in EDs. In this context, simulation and optimization techniques have been used to address the described management problems in a complicated healthcare system (10)(11)(12). Most of these works do not pay attention to all stages of a patient stream in the ED. In order to respond to this gap, this study aimed to simulate an optimal patient stream in ED to re- duce patient waiting time as well as raise the percentage of resource employment to an optimal level. 2. Methods 2.1. Study design and setting The present study was a longitudinal and simulation study conducted from 21 March 2019 to 19 February 2020. The op- timization method used in this research is a combination of comprehensive methods involving Discrete event simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA). The main objectives of the model include reducing the pa- tients’ waiting time and increasing the percentage of re- sources deployed to make optimal use of them. These two objectives will be modeled as two objective functions. The number of physicians, nurses, hospital beds, and triage space were considered as variables in the objective function. The research steps of work, as shown in figure 1, were data collection, simulating the systems’ current status using DES, validation of the simulated model, implementation of exper- iment design, ANN training and achieving system changes, modelling the problem, solving the model, and studying the outcome. 2.2. Data gathering Data needed for workflow simulation were collected through observation by 2 researchers during 11 months. All the pa- tients (74,796 patients) who referred to ED from 21 March 2019 to 19 February 2020 (11 months) were recorded and in- vestigated by researchers using the census method. A sum- mary of the number of studied patients in each part of ED is presented in (Table 1). 2.3. Developing the model The relation between different parts in the emergency de- partment was registered through observation of ED work- flow. After collecting information and data regarding pro- cesses using Enterprise Dynamics software, the model was simulated. Enterprise Dynamics software is an objective- oriented simulation, which is combined with an event- driven method. First, using an auto-fit menu, the probability distribution of data related to patients’ entry rate and service entry was determined. After determining the probable func- tions, the simulation model was designed. The next stage in simulation was to make sure about the accuracy and validity of the simulated model. 2.4. Validation of model In the validation process, the average of the real and simu- lated data was studied through an independent T-test at the confidence level of 95%. In the next step, the current sta- tus of the service system regarding the queue length criterion was examined to identify the crowded sections that need im- provement. Finally, machine learning experiments were de- signed to examine the effect of changes in individuals and equipment on average patient waiting time as well as the en- gagement rate of the units. In this regard, the complete facto- rial method was utilized by considering the central and axis points as well as 3 replications in MINITAB software. After designing the experiments and implementing them on the simulated model, as well as determining the queue length and the percentage of the units’ engagement, Artificial Neu- ral Network (ANN) and fractional genetic algorithm (GA) were used for simulation using MATLAB 2016 software. In this article, we have omitted the details and writing of math- ematical models and syntax of the algorithms used and tried to clarify its management results for the reader. It should be noted that for ease of reading, the table of columns related to the minitab executive operation and its prioritization has been omitted. 2.5. Designing the model The effect of 4 factors or resources on the system output were measured: the number of triage operators (triage), fast track physicians, ED physicians, and the number of inpatient beds (hospitalization) . All 4 factors have a queue and need to be examined in more detail. After designing the experiments, each of the experiments had been simulated and the related information was recorded in table 3. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2022; 10(1): e44 Table 1: Number of patients in each part of the studied emergency department during the study period Number Month 1 2 3 4 5 6 7 8 9 10 11 Non-hospitalized Futile cases 3 1 6 2 2 4 3 3 1 5 2 Outpatients 4154 4578 4268 4065 3935 4085 4603 4450 4382 4431 4925 Hospitalized cases ED 2465 2660 2518 2481 2524 2490 2485 2430 2287 2298 2250 General ward 516 509 600 810 899 803 799 844 748 730 670 Special ward 132 114 136 116 125 146 121 137 124 154 151 Others DAMA 146 169 172 147 147 156 147 159 138 102 119 Dispatched1 4 3 5 4 3 5 4 3 4 3 9 Died (first 24 h) 9 11 8 7 9 9 8 7 8 4 5 Discharged2 (%6 h) 74 71 66 66 68 67 66 67 65 67 65 Discharged3 (%12 h) 14 10 12 13 11 11 13 11 17 12 9 Total 6622 7239 6792 6548 6461 6579 7091 6883 6670 6734 7177 ED: emergency department; DAMA: discharged against medical advice; h: hour. 1 : to other hospitals; 2 : percentage of discharged cases within 6 hours. 3 : percentage of discharged cases within 12 hours. Table 2: The mean waiting times of patients in different sections of the studied emergency department (ED) Waiting time (hours) Mean ± SD Min Max 95% CI Triage unit 139.72 ± 56.14 103.87 351.69 124.16- 155.29 Fast track unit 5158.57 ±1747.36 2486.89 10349.52 4673.98 - 5643.16 ED specialist 458.21 ±61.96 366.13 634.32 441.02 -475.39 Hospitalization 57.03 ±41.35 3.35 166 45.56 -68.5 SD: standard deviation. CI: Confidence interval. Min: minimum, Max: maximum. 2.6. Validation Checking the validity of the model shows that the simulated model was not statistically different from the real data (p = 0.356). After ensuring the validity of the model, experiments were designed to investigate the effects of changes in the number of personnel and equipment in the emergency de- partment on the average waiting time of patients and the per- centage of bed occupancy. 2.7. Optimization According to the model optimization result, it was deter- mined that the hospitalization unit, as well as the hospital- ization units’ doctors, were in an optimized condition, but the triage unit, as well as the fast track units’ doctors, should be optimized. According to the resulting optimized answer, the changes in patients’ average waiting time, as well as units’ efficiency coefficient were as follows (Table 4). The average waiting time in the triage unit was reduced to almost zero, also, the average waiting time in the fast track section was reduced to 158.97 minute. Units’ engagement percentage in the two mentioned sections was 69% and 84%, respectively. Although the unit engagement percentage was reduced, this slight reduction can be overlooked due to the significant im- provement in patients’ waiting time. 3. Results The studied ED’s workflow is presented in figure 2. The stud- ied ED included different parts. ED physician was in charge of patients who needed critical cares. Fast track phycisian was in charge of patients who needed fewer emergency ser- vices. After entering the ED, patients go to the triage section to be categorized into 5 levels based on their medical condi- tions. In this section, patients who are considered as level 1 and 2 are handed over to the ED physician. The ED physician sends them to recovery, hospitalization, or outpatient service parts. Level 3 patients are assigned to both ED and fast track physicians. Levels 4 and 5 patients are assigned to the fast track physician. The mean waiting times in different sections of the ED were calculated and presented in table 2. 4. Discussion The decision-making process is extremely challenging for ED managers. Therefore, offering them the possibility to ac- quire more knowledge and evidence to ease this process is extremely valuable, especially in EDs where poor decisions may lead to critical situations for patients. In this research, in contrast to traditional methods like re- gression, to estimate the objective function, trained networks This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem SM. Hosseini_Shokouh et al. 4 Figure 1: Research implementation steps. were utilized. The results show the good performance of the proposed method in the analysis and optimization of the sys- tem. The major goals of this research are reducing the pa- tients’ waiting time as well as increasing the units’ engage- ment percentage for optimal usage, which are modeled as the two objective functions. After collecting the information and processes by use of ED software, the model was simulated to include the system complexities in the model. After making sure about the model validity, the experiments were designed to examine the effects of changes in sectors’ individuals and equipment on the average patient waiting time as well as the units’ engagement percentage. In the study by Duguay et al. (13), some alternatives, based on adding resources, have been investigated to reduce pa- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2022; 10(1): e44 Figure 2: The relation between different parts in the emergency department. tient waiting times and to improve overall service delivery and system’s throughput. Additionally, Thorwarth et al. (14), examined the impact of staff scheduling on overall utilization and burnout issues related to overutilized staff. Considering the reviewed literature, some important performance mea- sures such as resource utilization, productivity, and lay-out efficiency have not been analyzed completely, also there is a lack of a link between these indicators and international standards. Kırış et al. (15), developed a knowledge-based reactive scheduling system for emergency departments. To minimize patient waiting time, they considered patients’ pri- orities, arrival time, flow time, and physician’s workload. In a similar study Azadeh et al. (16) proposed a genetic al- gorithm (GA) for solving the problem of scheduling priori- tized patients in emergency department laboratories. A Re- sponse Surface Methodology (RSM) is applied for tuning the GA parameters. The algorithm can significantly improve the efficiency of the emergency department by reducing the to- tal waiting time of prioritized patients. Granja et al. (17) proposed a simulation-based optimization approach to the patient admission scheduling problem to minimize patients’ length of stay, while reducing the costs and increasing (or at least maintaining) the quality of care. The number of studies applying simulation for the improve- ment of healthcare systems has increased since the early 1990s, (18). Simulation studies within EDs have been used to improve the performance and reduce patients’ waiting time by studying multiple scenarios such as growing the number of physicians, staff, and medical devices (12). Gunal et al. (19), analyzed how to increase emergency department per- formance through Discrete-Event simulation. In a similar study, Al-Refaie et al. (12), took advantage of simulation in a Jordanian hospital’s ED to decrease the average patient wait- ing time, increase the number of served patients, and im- prove nurses’ productivity. Finally, they claimed that flexi- bility in cellular service systems such as hospitals has played an important role in improving the performance of ED. Zeng et al. (20) have done a study to increase the quality of ser- vices in a hospital’s ED in Kentucky, using computer simula- tion. They considered the length of stay, waiting times, and patient elopement as the most important parameters. According to our study results, the optimization of the pa- tient stream at ED is possible through appropriate allocation of the human and material resources. Azadeh et al. (1) also optimized the process of work in the ED of a hospital in Iran by taking advantage of simulation optimization and mod- eling human errors. Repeated venipuncture, unsafe trans- portation, and sampling errors were considered in that study. Norouzzadeh et al. (21), developed a modular discrete event simulation in a hospital’s ED to simplify the process of pa- tient flow to inpatient wards. Finally, the result showed that in order to make sure about better efficiency in ED, all of the improvements should be accomplished at the same time. 5. Limitations The present study, like other studies, had limitations that we like to mention here. First, multi-criterion decision-making methods about system optimization policies were not con- sidered in the present study, but can be included in the model in future studies. Second, the study result is based on the studied patients’ features and circumstance; therefore, the effect of COVID-19 pandemic on the ED had not been con- sidered in this study, the consideration of which in future studies will be helpful. Since ED waiting time and other indicator data were not This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem SM. Hosseini_Shokouh et al. 6 Table 3: Results of the designed experiments (Different modes of resource utilization) Row Number of sources Waiting times (minute) Resource efficiency (%) Triage Fast track ED physician Hospita- lization Triage Fast track ED physician Hospita- lization Triage Fast track ED physician Hospita- lization 1 2 2 2 14 122.92 4634 446 55 0.74 0.98 0.84 0.9 2 4 2 2 14 10.56 4780 458 56 0.68 0.98 0.84 0.9 3 2 2 4 14 126.48 4842 4.8 61.6 0.74 0.98 0.79 0.9 4 2 2 2 14 124.74 4899 467 65 0.74 0.99 0.85 0.89 5 2 2 4 14 134.13 5217 4.99 80.1 0.74 0.98 0.79 0.9 6 2 2 2 14 123.82 4998 463 76 0.74 0.98 0.84 0.9 7 2 2 2 14 143.7 5513 461 69 0.74 0.98 0.84 0.9 8 2 2 1 14 397.41 12743 12026 0.03 0.68 0.92 1 0.99 9 2 2 2 14 130.29 4943 462 63 0.74 0.98 0.84 0.9 10 4 2 2 14 18.46 5143 459 70 0.68 0.98 0.84 0.9 11 2 4 2 14 115 4 662 63.25 0.74 0.88 0.83 0.92 12 2 2 2 14 122.54 4618 459 64 0.74 0.98 0.84 0.9 13 1 2 2 14 3389 37 212.5 9.39 0.99 0.98 0.78 0.96 14 2 1 2 14 6836 44381 42 0 0.02 1 0.57 0.99 15 2 2 2 14 136.78 5053 447 60 0.74 0.98 0.84 0.9 16 2 1 2 14 6831 44380 43.3 0 0.02 1 0.57 0.99 17 2 2 2 14 123.18 4622 457 64 0.74 0.98 0.84 0.9 18 2 2 2 14 134.45 4795 468 48 0.74 0.98 0.84 0.9 19 4 2 2 14 12.34 4777 450 49 0.68 0.98 0.84 0.91 20 2 2 2 14 127.23 4814 457 79 0.74 0.98 0.84 0.9 21 1 2 2 14 3407 37 208.8 11.73 1 0.98 0.78 0.96 22 2 2 2 14 136.48 5053 447 60 0.74 0.98 0.84 0.9 23 2 2 2 14 140.7 5513 461 67 0.74 0.98 0.84 0.9 24 2 2 0.5 14 461.76 13825 12023 0.2 0.67 0.92 1 0.99 25 2 2 2 14 122.58 4638 451 56 0.74 0.98 0.84 0.89 26 2 2 2 14 124.3 4814 462 67 0.74 0.98 0.85 0.9 27 2 2 2 14 136.78 4943 446 60 0.74 0.98 0.84 0.9 28 2 2 2 14 128.6 4634 451 53 0.74 0.98 0.84 0.9 29 2 1 2 14 6830 44360 42.3 0 0.02 1 0.57 0.99 30 2 2 0.5 14 481.64 14060 12063 0.14 0.67 0.92 1 0.99 31 2 2 2 14 123.82 5513 462 60 0.74 0.99 0.84 0.9 32 2 2 2 14 130.29 5425 461 76 0.74 0.98 0.84 0.9 33 2 4 2 14 114 3.9 689 58.9 0.74 0.88 0.83 0.92 34 2 2 2 14 123.2 4899 463 65 0.74 0.98 0.84 0.9 35 1 2 2 14 3385 37.29 208 8 0.99 0.98 0.78 0.97 36 2 2 2 14 128.6 4888 448 63 0.74 0.98 0.84 0.9 37 2 2 2 14 134.45 5017 450 63 0.74 0.98 0.84 0.9 38 2 4 2 14 114 3.95 691 59.4 0.74 0.88 0.83 0.92 39 2 2 4 14 121.29 4711 4.54 84.1 0.74 0.98 0.79 0.89 Table 4: The results of optimization study of 4 studied factors or resources in the emergency department (ED) Item Triage Fast track ED physician Hospitalization Patients’ waiting time Optimum 3.22 158.97 458.21 57.03 Real (before experiment) 139.72 5158.57 458.21 57.03 Units’ efficiency coefficient (%) Optimum 0.69 0.84 0.84 0.9 Real 0.74 0.98 0.84 0.9 Times are presented as minute. comprehensively integrated in the hospital information sys- tem (HIS), for ensuring reliability and completing the data, the researcher simultaneously used two ways: attending the emergency department and HIS data. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2022; 10(1): e44 6. Conclusion Using the studied optimization method creates a significant improvement in patient waiting time and the optimization of patient stream in the ED is possible through appropriate allocation of the human and material resources. It is sug- gested to study the optimization of patient stream through machine learning methodology for improving the other sec- tions of hospital departments. 7. Declarations 7.1. Acknowledgments We would like to thank the “Clinical Research Development Center of Baqiyatallah hospital” for their kind cooperation. 7.2. Authors’ contributions Concept and design: KM, MY, SMHS. Acquisition, analysis, or interpretation of data: KM & MA. Drafting of the manuscript: KM & MY. Critical revision of the manuscript:SMHS & MY. Administrative, technical, or material support:MY, SMHS. Supervision: MY & SMHS. All authors read and approved the final version. 7.3. Funding and supports None. 7.4. Competing interests The authors declare that they have no competing interests. 7.5. Ethics approval and consent to participate The Medical Research Ethics Committee of Iran reviewed the study protocol and concluded that the research is not sub- ject to the Iran Medical Research Involving Human Subjects Act, and therefore, ethics approval was waived. The neces- sity for obtaining informed consent was waived by the med- ical research ethics committee of Iran, due to the complete anonymity of the study data. Therefore, study data was in no way traceable to individuals. 7.6. Consent for publication Not applicable. 7.7. Availability of data and materials Original data remain available and access may be provided upon reasonable request. 7.8. 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Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Limitations Conclusion Declarations References