Using Discrete-Event Simulation to Model Web-Based Crisis Counseling Service Operation: Evaluation Study.

BACKGROUND: According to the Organisation for Economic Co-operation and Development, its member states experienced worsening mental health during the COVID-19 pandemic, leading to an increase of 60% to 1000% in digital counseling access. Hong Kong, too, witnessed a surge in demand for crisis intervention services during the pandemic, attracting both nonrepeat and repeat service users during the process. As a result of the continuing demand, platforms offering short-term emotional support are facing an efficiency challenge in managing caller responses. OBJECTIVE: This aim of this paper was to assess the queuing performance of a 24-hour text-based web-based crisis counseling platform using a Python-based discrete-event simulation (DES) model. The model evaluates the staff combinations needed to meet demand and informs service priority decisions. It is able to account for unbalanced and overlapping shifts, unequal simultaneous serving capacities among custom worker types, time-dependent user arrivals, and the influence of user type (nonrepeat users vs repeat users) and suicide risk on service durations. METHODS: Use and queue statistics by user type and staffing conditions were tabulated from past counseling platform database records. After calculating the data distributions, key parameters were incorporated into the DES model to determine the supply-demand equilibrium and identify potential service bottlenecks. An unobserved-components time-series model was fitted to make 30-day forecasts of the arrival rate, with the results piped back to the DES model to estimate the number of workers needed to staff each work shift, as well as the number of repeat service users encountered during a service operation. RESULTS: The results showed a marked increase (from 3401/9202, 36.96% to 5042/9199, 54.81%) in the overall conversion rate after the strategic deployment of human resources according to the values set in the simulations, with an 85% chance of queuing users receiving counseling service within 10 minutes and releasing an extra 39.57% (3631/9175) capacity to serve nonrepeat users at potential risk. CONCLUSIONS: By exploiting scientifically informed data models with DES, nonprofit web-based counseling platforms, even those with limited resources, can optimize service capacity strategically to manage service bottlenecks and increase service uptake.

Enhancing home delivery of emergency medicine and medical supplies through clustering and simulation techniques: A case study of COVID-19 home isolation in Bangkok.

This study investigates the enhancement of the home delivery distribution network for COVID-19 Home Isolation (HI) kits during the Delta variant outbreak of the SARS-CoV-2 virus in Bangkok Metropolitan Area, Thailand. It addresses challenges related to limited resources and delays in delivering HI kits, which can exacerbate symptoms and increase mortality rates. A k-means clustering approach is utilized to optimize the assignment of service areas within the COVID-19 HI program, while discrete event simulation (DES) evaluates potential changes in the home delivery logistics network. Real-world data from the peak outbreak is used to determine the optimal allocation of resources and propose a new logistics network based on proximity to patients' residences. Experimental results demonstrate a significant 44.29 % improvement in overall performance and a substantial 40.80 % decrease in maximum service time. The findings offer theoretical and managerial implications for effective HI management, supporting practitioners and policymakers in mitigating the impact of future outbreaks.

Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

Communication dynamics in brain networks have been modeled with various approximations to signaling in the connectome. These approximations differ in their assumptions about propagation strategies (random walks, shortest path routing) and switching architectures (message switching, packet switching); however, their relationships in brain network communication models have been unclear so far. Here, we link them by investigating how the difference between packet and message switching (whether signals are packetized or not) affects the transmission completion time of propagation strategies in communication simulations in the connectome. We find that packetization selectively reduces the time of physiologically plausible strategies for the connectome that balance communication speed and information requirements. This study sheds light on the utility of packet switching for modeling efficient communication in brain networks.

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

Patient flow modeling and simulation to study HAI incidence in an Emergency Department.

Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital-which hospital units patients visit and where they spend the most time since their admission into the hospital-can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022-June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study.

BACKGROUND: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. METHODS: First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. RESULTS: The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 - 96.26%]), whilst the specificity was 85.45% [77.45 - 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 - 95.13%) and 87.85% (80.12 - 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 - 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. CONCLUSIONS: Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

Innovate and Validate: Design-Led Simulation Optimization to Test Centralized Registration Feasibility in a Multispecialty Clinic.

OBJECTIVE: This study utilizes a design-led simulation-optimization process (DLSO) to refine a hybrid registration model for a free-standing outpatient clinic. The goal is to assess the viability of employing DLSO for innovation support and highlight key factors influencing resource requirements. BACKGROUND: Manual registration in healthcare causes delays, impacting patient services and resource allocation. This study addresses these challenges by optimizing a hybrid centralized registration and adopting technology for efficiency. METHOD: An iterative methodology with simulation optimization was designed to test a proof of concept. Configurations of four and five registration options within a hybrid centralized system were explored under preregistration adoption rates of 30% and 50%. Three self-service kiosks served as a baseline during concept design and test fits. RESULTS: Centralized registration accommodated a daily throughput of 2,000 people with a 30% baseline preregistration rate. Assessing preregistration impact on seating capacity showed significant reductions in demand and floor census. For four check-in stations, a 30%-50% preregistration increase led to a 32% seating demand reduction and a 26% decrease in maximum floor census. With five stations, a 50% preregistration reduced seating demand by 23% and maximum floor census by 20%. CONCLUSION: Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

Using simulation modelling to transform hospital planning and management to address health inequalities.

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Anti-Vascular Endothelial Growth Factor Drugs Compared With Panretinal Photocoagulation for the Treatment of Proliferative Diabetic Retinopathy: A Cost-Effectiveness Analysis.

OBJECTIVES: This study aimed to evaluate the cost-effectiveness of anti-vascular endothelial growth factor drugs (anti-VEGFs) compared with panretinal photocoagulation (PRP) for treating proliferative diabetic retinopathy (PDR) in the United Kingdom. METHODS: A discrete event simulation model was developed, informed by individual participant data meta-analysis. The model captures treatment effects on best corrected visual acuity in both eyes, and the occurrence of diabetic macular edema and vitreous hemorrhage. The model also estimates the value of undertaking further research to resolve decision uncertainty. RESULTS: Anti-VEGFs are unlikely to generate clinically meaningful benefits over PRP. The model predicted anti-VEGFs be more costly and similarly effective as PRP, generating 0.029 fewer quality-adjusted life-years at an additional cost of £3688, with a net health benefit of -0.214 at a £20 000 willingness-to-pay threshold. Scenario analysis results suggest that only under very select conditions may anti-VEGFs offer potential for cost-effective treatment of PDR. The consequences of loss to follow-up were an important driver of model outcomes. CONCLUSIONS: Anti-VEGFs are unlikely to be a cost-effective treatment for early PDR compared with PRP. Anti-VEGFs are generally associated with higher costs and similar health outcomes across various scenarios. Although anti-VEGFs were associated with lower diabetic macular edema rates, the number of cases avoided is insufficient to offset the additional treatment costs. Key uncertainties relate to the long-term comparative effectiveness of anti-VEGFs, particularly considering the real-world rates and consequences of treatment nonadherence. Further research on long-term visual acuity and rates of vision-threatening complications may be beneficial in resolving uncertainties.

Would initiating colorectal cancer screening from age of 45 be cost-effective in Germany? An individual-level simulation analysis.

Background: Colorectal cancer (CRC) screening has been shown to be effective and cost-saving. However, the trend of rising incidence of early-onset CRC challenges the current national screening program solely for people ≥50 years in Germany, where extending the screening to those 45-49 years might be justified. This study aims to evaluate the cost-effectiveness of CRC screening strategies starting at 45 years in Germany. Method: DECAS, an individual-level simulation model accounting for both adenoma and serrated pathways of CRC development and validated with German CRC epidemiology and screening effects, was used for the cost-effectiveness analysis. Four CRC screening strategies starting at age 45, including 10-yearly colonoscopy (COL), annual/biennial fecal immunochemical test (FIT), or the combination of the two, were compared with the current screening offer starting at age 50 years in Germany. Three adherence scenarios were considered: perfect adherence, current adherence, and high screening adherence. For each strategy, a cohort of 100,000 individuals with average CRC risk was simulated from age 20 until 90 or death. Outcomes included CRC cases averted, prevented death, quality-adjusted life-years gained (QALYG), and total incremental costs considering both CRC treatment and screening costs. A 3% discount rate was applied and costs were in 2023 Euro. Result: Initiating 10-yearly colonoscopy-only or combined FIT + COL strategies at age 45 resulted in incremental gains of 7-28 QALYs with incremental costs of €28,360-€71,759 per 1,000 individuals, compared to the current strategy. The ICER varied from €1,029 to €9,763 per QALYG, and the additional number needed for colonoscopy ranged from 129 to 885 per 1,000 individuals. Among the alternatives, a three times colonoscopy strategy starting at 45 years of age proves to be the most effective, while the FIT-only strategy was dominated by the currently implemented strategy. The findings remained consistent across probabilistic sensitivity analyses. Conclusion: The cost-effectiveness findings support initiating CRC screening at age 45 with either colonoscopy alone or combined with FIT, demonstrating substantial gains in quality-adjusted life-years with a modest increase in costs. Our findings emphasize the importance of implementing CRC screening 5 years earlier than the current practice to achieve more significant health and economic benefits.

A discrete-event simulation model for assessing operating room efficiency of thoracic, gastrointestinal, and orthopedic surgeries.

BACKGROUND: In hospital management, pinpointing steps that most enhance operating room (OR) throughput is challenging. While prior literature has utilized discrete event simulation (DES) to study specific strategies such as scheduling and resource allocation, our study examines an earlier planning phase, assessing all workflow stages to determine the most impactful steps for subsequent strategy development. METHODS: DES models real-world systems by simulating sequential events. We constructed a DES model for thoracic, gastrointestinal, and orthopedic surgeries summarized from a tertiary Chinese hospital. The model covers preoperative preparations, OR occupation, and OR preparation. Parameters were sourced from patient data and staff experience. Model outcome is OR throughput. Post-validation, scenario analyses were conducted for each department, including: (1) improving preoperative patient preparation time; (2) increasing PACU beds; (3) improving OR preparation time; (4) use of new equipment to reduce the operative time of a selected surgery type; three levels of improvement (slight, moderate, large) were investigated. RESULTS: The first three improvement scenarios resulted in a 1%-5% increase in OR throughput across the three departments. Large reductions in operative time of the selected surgery types led to approximately 12%, 33%, and 38% increases in gastrointestinal, thoracic, and orthopedic surgery throughput, respectively. Moderate reductions resulted in 6%-17% increases in throughput and slight reductions of 1%-7%. CONCLUSIONS: The model could reliably reflect OR workflows of the three departments. Among the options investigated, model simulations suggest that improving OR preparation time and operative time are the most effective.

Discrete Event Simulation to Incorporate Infusion Wait-Time When Assessing Cost-Effectiveness of a Chimeric-Antigen Receptor T Cell Therapy.

OBJECTIVES: The main objective was to use discrete event simulation to model the impact of wait-time, defined as the time between leukapheresis and chimeric antigen receptor (CAR-T) infusion, when assessing the cost-effectiveness of tisagenlecleucel in young patients with relapsed/refractory acute lymphoblastic leukemia. METHODS: The movement of patients through the model was determined by parametric time-to-event distributions, with the competing risk of an event determining the costs and quality-adjusted life-years (QALYs) assigned. Cost-effectiveness was expressed using the incremental cost-effectiveness ratio (ICER) for tisagenlecleucel compared with chemotherapy over the lifetime. RESULTS: The base case generated a total of 5.79 QALYs and $622 872 for tisagenlecleucel and 1.19 QALYs and $181 219 for blinatumomab, resulting in an ICER of $96 074 per QALY. An increase in mean CAR-T wait-time to 6.20 months reduced the benefit and costs of tisagenlecleucel to 2.78 QALYs and $294 478 because of fewer patients proceeding to infusion, reducing the ICER to $71 112 per QALY. Alternatively, when the cost of tisagenlecleucel was assigned pre-infusion in sensitivity analysis, the ICER increased with increasing wait-time. CONCLUSIONS: Under a payment arrangement where CAR-T cost is incurred post-infusion, the loss of benefit to patients is not reflected in the ICER. This may be misguiding to decision makers, where cost-effectiveness ratios are used to guide resource allocation. discrete event simulation is an important tool for economic modeling of CAR-T as it is amenable to capturing the impact of wait-time, facilitating better understanding of factors affecting service delivery and consequently informed decision making to deliver faster access to CAR-T for patients.

Continuous Remote Patient Monitoring for Post-Discharge Heart Failure Management: Workflow Modeling Using Discrete Event Simulation.

The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a major health system in the United States to reduce Heart Failure (HF)-related hospitalizations and readmissions using wearable biosensors to collect physiological data over a 30-day period to determine decompensation risk among HF patients. The alerts produced, coupled with electronic patient-reported outcomes, are utilized daily by the home health team, and escalated to the heart failure team as needed, for proactive actions. Limited research has examined anticipating the implementation and workflow challenges of such complex CRPM studies such as resource planning and staffing decisions that leverage the recorded data to drive clinical preparedness and operational efficiency. This preliminary analysis applies discrete event simulation modeling to the Cascade-HF protocol using pilot data from a soft launch to assess workload of the clinical team, evaluate escalation patterns and provide decision support recommendations to enable scale-up for all post-discharge patients.

Optimizing the management of electrophysiology labs in Chinese hospitals using a discrete event simulation tool.

BACKGROUND: The growing demand for electrophysiology (EP) treatment in China presents a challenge for current EP care delivery systems. This study constructed a discrete event simulation (DES) model of an inpatient EP care delivery process, simulating a generalized inpatient journey of EP patients from admission to discharge in the cardiology department of a tertiary hospital in China. The model shows how many more patients the system can serve under different resource constraints by optimizing various phases of the care delivery process. METHODS: Model inputs were based on and validated using real-world data, simulating the scheduling of limited resources among competing demands from different patient types. The patient stay consists of three stages, namely: the pre-operative stay, the EP procedure, and the post-operative stay. The model outcome was the total number of discharges during the simulation period. The scenario analysis presented in this paper covers two capacity-limiting scenarios (CLS): (1) fully occupied ward beds and (2) fully occupied electrophysiology laboratories (EP labs). Within each CLS, we investigated potential throughput when the length of stay or operative time was reduced by 10%, 20%, and 30%. The reductions were applied to patients with atrial fibrillation, the most common indication accounting for almost 30% of patients. RESULTS: Model validation showed simulation results approximated actual data (137.2 discharges calculated vs. 137 observed). With fully occupied wards, reducing pre- and/or post-operative stay time resulted in a 1-7% increased throughput. With fully occupied EP labs, reduced operative time increased throughput by 3-12%. CONCLUSIONS: Model validation and scenario analyses demonstrated that the DES model reliably reflects the EP care delivery process. Simulations identified which phases of the process should be optimized under different resource constraints, and the expected increases in patients served.

Cost-effectiveness of improving patients' adherence to tuberculosis treatment in South Korea using discrete event simulation.

BACKGROUND: Poor adherence to tuberculosis (TB) treatment is an obstacle to controlling the disease. The Korean government's national TB control plan includes a program on adherence to TB treatment to manage patients with TB. This study aimed to assess the cost-effectiveness of a national TB program for improving patient adherence. METHODS: A discrete event simulation (DES) model was developed to estimate the costs and quality-adjusted life-years (QALYs) of adherent and non-adherent patients. In this model, we considered treatment completion, loss to follow-up, recurrence, death, and treatment changes from drug-susceptible to multidrug-resistant TB as clinical events. We obtained input parameters such as costs, probability of events, and time distributions for each event from the Korean National Health Insurance claims data. We estimated the costs and QALYs before implementation of the program (adherence rate = 79%) and at present (current adherence rate = 94%). The incremental cost-effectiveness ratio (ICER) was used to evaluate whether the program was cost-effective given the willingness-to-pay threshold. RESULTS: In the simulation, the program increasing the proportion of adherent patients gained 0.018 QALY/patient while spending $162/patient. The ICER of the TB program was $8790/QALY. Given a willingness-to-pay threshold of $20,000, the national TB program was considered cost-effective. CONCLUSION: Improvements in adherence to TB treatment through the current TB program were cost-effective. The DES model accurately reflected the real world. Commitment programs to improve patient adherence may help manage TB nationwide.

Introduction of ultra-hypofractionation in breast cancer: Implications for costs and resource use.

PURPOSE: A shift towards (ultra-)hypofractionated breast irradiation can have important implications for the practice of contemporary radiation oncology. This paper presents a systematic analysis of the impact of different fractionation schedules on multiple key performance indicators, namely resource use, costs, work times, throughput and waiting times. MATERIALS AND METHODS: Time-driven activity-based costing (TD-ABC) is applied to calculate the costs and resources consumed where the perspective of the radiotherapy department in adopted. Three fractionation regimens are considered: ultra-hypofractionation (5 x 5.2 Gy, UHF), moderate hypofractionation (15 x 2.67 Gy, HF) and conventional fractionation (25 x 2 Gy, CF). Subsequently, a discrete event simulation (DES) model of the radiotherapy care pathway is developed and scenarios are compared in which the following factors are varied: distribution of fractionation regimens, patient volume and operating hours. RESULTS: The application of (U)HF can permit radiotherapy departments to reduce the use of scarce resources, realise work time and cost savings, increase throughput and reduce waiting times. The financial advantages of (U)HF are, however, reduced in cases of excess capacity and cost savings may therefore be limited in the short-term. Moreover, although an extension of operating hours has favourable effects on throughput and waiting times, it may also reduce cost differences between fractionation schedules by increasing the capacity of resources. CONCLUSION: By providing an in-depth analysis of the consequences associated with a shift towards (U)HF in breast cancer, the present study demonstrates how a DES model based on TD-ABC costing can assist radiotherapy professionals in making data-driven decisions.

Implementing competing risks in discrete event simulation: the event-specific probabilities and distributions approach.

Background: Although several strategies for modelling competing events in discrete event simulation (DES) exist, a methodological gap for the event-specific probabilities and distributions (ESPD) approach when dealing with censored data remains. This study defines and illustrates the ESPD strategy for censored data. Methods: The ESPD approach assumes that events are generated through a two-step process. First, the type of event is selected according to some (unknown) mixture proportions. Next, the times of occurrence of the events are sampled from a corresponding survival distribution. Both of these steps can be modelled based on covariates. Performance was evaluated through a simulation study, considering sample size and levels of censoring. Additionally, an oncology-related case study was conducted to assess the ability to produce realistic results, and to demonstrate its implementation using both frequentist and Bayesian frameworks in R. Results: The simulation study showed good performance of the ESPD approach, with accuracy decreasing as sample sizes decreased and censoring levels increased. The average relative absolute error of the event probability (95%-confidence interval) ranged from 0.04 (0.00; 0.10) to 0.23 (0.01; 0.66) for 60% censoring and sample size 50, showing that increased censoring and decreased sample size resulted in lower accuracy. The approach yielded realistic results in the case study. Discussion: The ESPD approach can be used to model competing events in DES based on censored data. Further research is warranted to compare the approach to other modelling approaches for DES, and to evaluate its usefulness in estimating cumulative event incidences in a broader context.

The application of simulation methods during the COVID-19 pandemic: A scoping review.

With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.

An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit.

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

Hospital Resource Planning for Mass Casualty Incidents: Limitations for Coping with Multiple Injured Patients.

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity-in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances.