Pandemic Response Management

Estimating the capacity of a region to serve pandemic patients in need of hospital services is crucial to regional preparedness for pandemic surge conditions. This paper explores the use of techniques of stochastic discrete event simulation for estimating the maximum number of pandemic patients with intensive care and/or in-patient, isolation requirements that can be served by a consortium of hospitals in a region before requesting external resources. Estimates from the model provide an upper bound on the number of patients that can be treated if all hospital resources are re-allocated for pandemic care. The modeling approach is demonstrated on a system of five hospitals each replicating basic elements (e.g. number of beds) of the five hospitals in the Johns Hopkins Hospital System in the Baltimore-Washington, D.C. Metropolitan area under settings relevant to the COVID-19 pandemic.

The SARS-CoV-2 pandemic has driven a need for models that can predict transmission among populations and test the effectiveness of proposed interventions to control it such as testing, quarantining, vaccinations, and nonpharmaceutical interventions. This report describes a hybrid approach that is mostly agent based, but utilizes Ro and time frames from ordinary differential equation (ODE) models to estimate infection rates and reduce data requirements. The ExtendSim model developed is intended to inform management decisions regarding testing and quarantine policies and technologies.

How might one both prevent and control an epidemic spreading of disease (for example COVID-19) in supermarkets? To answer this question, a simulation approach was proposed. A number of simulation experiments were performed. The likelihood of close contact with a carrier in different ticket queue configurations was compared. Based on the results, several tips and advice was developed for customers and market managers.

This study builds on earlier research which focuses on the use of combined simulation methods to support healthcare demand predictions. The authors developed a hybrid simulation model composed of two submodels which were built using different simulation paradigms. Both models, i.e. a population model based on a continuous modeling approach and an arrivals model built with discrete-event methodology, were created in ExtendSim. This combined model simulates the consequences of demographic changes, the variability in incidence rates that result from an aging population, and the seasonal fluctuations in epidemic trends on future demand for healthcare services.

The existing hospital system is struggling with the current volume of patients. The predicted increase in volume will push the existing system past its breaking point. Our team was tasked with not only designing a new emergency department with the appropriate capacity, but more importantly seeing how various operational changes would affect the new system while investigating clinical trends and the effect on the whole hospital system.

To accomplish this task in an evidence-based way, a simulation model was developed using ExtendSim. This approach helped the clinical, managerial, and facility staff to not just understand their existing department workflow but also to provide them the opportunity to test trans-disciplinary scenarios to guide the design of the new emergency department. Furthermore it provided the client with the statistics to evaluate the impact a new Chest Pain & General Observation Center and Results Waiting Lounge would have on their entire system.

Nurses have increasingly been regarded as critical members of the planning team as architects recognize their knowledge and value. But the nurses’ role as knowledge experts can be expanded to leading efforts to integrate the clinical, operational, and architectural expertise through simulation modeling. Simulation modeling allows for the optimal merge of multifactorial data to understand the current state of the intensive care unit and predict future states. Nurses can champion the simulation modeling process and reap the benefits of a cost-effective way to test new designs, processes, staffing models, and future programming trends prior to implementation. Simulation modeling is an evidence-based planning approach, a standard, for integrating the sciences with real client data, to offer solutions for improving patient care.

A simulation model can guide the team through a more efficient planning process and will offer solutions that are not static. These dynamic solutions mirror the changing climate of health care, offering opportunities to predict the impact of future changes in clinical care, operations, and architecture in the ICU.

This paper presents a comprehensive exploration of an Internal Medicine outpatient clinic practice setting by applying a discrete event simulation (DES) model. Growing demands on outpatient clinics require greater emphasis on enhancing performance and optimizing resource utilization. Therefore, a data collection plan was designed to capture total patient visit time; including waiting, clinical care, and clinical administrative time. The collected data was fed into a DES model. The mode was validated through a statistical comparison with the performance of the real system. Various improvement alternatives were then proposed and investigated through the DES model, such as altering resource allocation, patient rooming and prioritization, and patient volume. For each scenario, key performance indicators of the system, resource utilization metrics, capacity metics, and turnaround time metrics were traced. Findings indicated that targeted improvement scenarios could be applied with 27.5%, 54.8%, and 20% enhancement in utilization, capacity, and turnaround time respectively.

Healthcare architecture design of a new ambulatory surgery unit explores the relationship between the number of operating rooms and recovery beds, the number of cases and the phases of recovery, bed capacity, staffing, and waiting room seat requirements as each affected the design. Model results allowed time for variations in the design to meet the client's needs before the project was built. Furthermore, the simulation shows the positive outcome of combining the experience of three professional disciplines: healthcare planning, architecture, and simulation modeling for nurses, doctors and patients of the new ambulatory surgery unit.

Simulation modeling is a tool of hospital-wide system process change with a focus on improving patient satisfaction through a better clinical process. Some of the improvements, based on existing systems, include decreased delays, better healthcare staff utilization and tailored scheduling. In this part 2 of 2 perioperative case studies (part 1: Ambulatory Surgery Unit), the experiences of three distinct, yet complimentary professions are combined and opitimized: nursing/healthcare planning, architecture, and simulation engineering to create a better way to provide a clinical service in a newly designed healthcare space.

A large hospital in southeast United States utilized simulation to aid project leaders in advancing to the next level of sophistication with Six Sigma. The ExtendSim model brought clarity to potential process improvements in their Emergency Department (ED) and enabled the hospital to test design ideas of a planned new facility.

Emergency management planners and health care facilities are faced with potential scenarios that may severely test their capabilities and conventional processes. This paper presents the development of a simulation-based environment that allows emergency planners and professionals to prepare more robust emergency response plans without using costly, time consuming physical drills.

The ExtendSim model built for this project could be used to:

• analyze the effects of incoming calls on a system
• optimize personnel resources at call centers
• investigate the impact of workload distribution, shift schedule adjustments, and staffing profiles on response times
• estimate the impact on the resources required in relation to a reduction or increase in the number of communication centers
• improve the efficiency and location of dispatch services
• improve the ambulance allocation algorithm