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.

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.

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model\'s effectiveness by incorporating the medical personnel\'s expertise in decisions regarding the use of elevators, departments\' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.

The development of safe and effective vaccines against COVID-19 has been a turning point in the international effort to control this disease. However, vaccine development is only the first phase of the COVID-19 vaccination process. Correct planning of mass vaccination is important for any policy to immunize the population. For this purpose, it is necessary to set up and properly manage mass vaccination centers. This paper presents a discrete event simulation model of a real COVID-19 mass vaccination center located in Sfax, Tunisia. This model was used to evaluate the management of this center through different performance measures. Three person\'s arrival scenarios were considered and simulated to verify the response of this real vaccination center to arrival variability. A second model was proposed and simulated to improve the performances of the vaccination center. Like the first model, this one underwent the same evaluation process through the three arrivals scenarios. The simulation results show that both models respond well to the arrival\'s variability. Indeed, most of the arriving persons are vaccinated on time for all the studied scenarios. In addition, both models present moderate average vaccination and waiting times. However, the average utilization rates of operators are modest and need to be improved. Furthermore, both simulation models show a high average number of persons present in the vaccination center, which goes against the respect of the social distancing condition. Comparison between the two simulation models shows that the proposed model is more efficient than the actual one.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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%.
UNASSIGNED: Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

OBJECTIVE: 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.

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.
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.
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.
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.

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.
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.
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%.
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.

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.
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.
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.
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.

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.
CONCLUSIONS: 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.