論文

Recent US healthcare reform debates have triggered substantial discussion on how best to improve access to insurance. Colorectal cancer (CRC) is an example of a largely preventable condition, if access to and use of healthcare is increased. Early and ongoing screening and intervention can identify and remove polyps before they become cancerous. We present the development of an individual-based discrete-event simulation model to estimate the impact of insurance expansion scenarios on CRC screening, incidence, mortality, and costs. A national repeated cross-sectional survey was used to estimate which individuals obtained insurance in North Carolina (NC) after the Affordable Care Act (ACA). The potential impact of expanding the state’s Medicaid program is tested and compared to no insurance reform and the ACA without Medicaid expansion. The model integrates a census-based synthetic population, national data, claims based statistical models, and a natural history module in which simulated polyps and cancer progress.

This study is designed to demonstrate the benefit of using a single simulation model in order to analyze operations at two distinct, but related, pediatric orthopedic outpatient clinics in Massachusetts. A simulation model with a built-in dashboard is constructed for the clinics using AnyLogic. The constructed simulation model has been proven to be a useful tool in anticipating the effects of changes in system features such as patient volume, provider team mix, and exam room assignment policies. With the development of a flexible simulation model the ultimate goal is to assist clinic managers in their efforts to reduce patient waiting time and lengths of stay in the two distinct orthopedic clinics.

Emergency Medical Service (EMS) is one of the most important health care services as it plays a vital role in saving people’s lives and reducing the rate of mortality and co-morbidity. The importance and sensitivity of decision making in the EMS field have been recognized by researchers who studied many problems arising in the management of EMS systems since the 1960. Some authors of similar research present a review of the many simulation models that have been developed over the years: most of the available simulation approaches are based on a Discrete Event Simulation (DES) approach.

Emergency department (ED) crowding is a worldwide challenge. It adversely affects quality of care, patient safety, and employee satisfaction. The magnitude of ED crowding can be measured by the quality metrics length-of-stay (LOS), the patient’s door-to-doctor-time (DTD), and the 4-hourstandard. This standard states that 95% of the patients stay less than four hours within the ED. In order to improve those metrics, healthcare processes have to be welldesigned and resource capacity has to match the ever increasing demand. We implemented a validated, detailed discrete-event simulation model of a multidisciplinary ED in Germany to provide decision support for ED managers. Our model incorporates several patient flows considering patients and resources of two different medical specialties. The introduced simulation model was parameterized according to real-world data. Leveraging OptQuest and AnyLogic, we combined optimization and simulation to find input staffing levels that minimize the avg. LOS of patients. Simulation experiments show that certain process modifications, nurse pooling, and optimized staffing levels lead to improvements in quality of care. With respect to that, both avoiding boarding of inpatients and implementing nurse pooling result in a decrease of more than 14% in avg. LOS and are particularly promising. We also identified that reallocating capacities from internists to nurses dedicated to internal medicine patients enhances the quality of care.

This paper provides an overview of a hybrid, discrete event simulation (DES) agent based model (ABM), simulation model of the overdue pregnancy outpatient clinic at the Obstetrics department of Akershus University Hospital, Norway. The model is being developed in collaboration with clinic staff. The purpose of the model is to better plan resources (e.g. staffing) to improve patient flow at the outpatient clinic given the uncertainty associated with demand. The uncertainty is due to an increase in the size of the hospital’s catchment area, changes to overdue pregnancy guidelines in Norway and that women can give birth before their appointments. The ABM model component represents the human parts of the system, the women and the clinic staff. The DES component represents the outpatient clinic’s physical location and processes/pathways that operate within it. The technicalities of the model are presented along with some illustrative results.

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin.

Biological interactions between varicella (chickenpox) and herpes zoster (shingles), two diseases caused by the varicella zoster virus (VZV), continue to be debated including the potential effect on shingles cases following the introduction of universal childhood chickenpox vaccination programs. Researchers investigated how chickenpox vaccination in Alberta impacts the incidence and age-distribution of shingles over 75 years post-vaccination, taking into consideration a variety of plausible theories of waning and boosting of immunity.

Health care system is one of the most critical units in case of disasters. Floods cause an increase of emergency patient flow that may overwhelm hospital resources. In this paper, we present a simulation model that evaluates health care emergency plan and assesses the resilience of the Ile-de-France region in case of a major flood. We combined in the model the health care process with a Markov chain flood model. The results can be used to elaborate an optimized strategy for evacuation and transfer operations. We provide a case study on three specialties and quantify the impact of several flood scenarios on the health care system.

A simulation model was created to model the traffic flow in the operating room. A key research challenge in operating room design is to create the most efficient layout that supports staff and patient requirements on the day of surgery. The simulation allows comparison of base model designs to future designs using several performance measures. To develop the model, we videotaped multiple surgeries in a set of operating rooms and then coded all activities by location, agent and purpose. Our current analysis compares layouts based on total distance walked by agents, as well as the number of contacts, measured as the number of times agents must change their path to accommodate some other agent or physical constraint in the room. We demonstrate the value and capability of the model by improving traffic flow in the operating room as a result of rotating the bed orientation.

Utilization evaluation for healthcare facilities such as hospitals and nursing homes is crucial for providing high quality healthcare services in various communities. In this paper, a data-driven simulation framework integrating statistical modeling and agent-based simulation (ABS) is proposed to evaluate the utilization of various healthcare facilities. A Bayesian modeling approach is proposed to model the relationship between heterogeneous individuals’ characteristics and time to readmission in the hospital and nursing home. An ABS model is developed to model the dynamically changing health conditions of individuals and readmission/discharge events. The individuals are modeled as agents in the ABS model, and their time to readmission and length of stay are driven by the developed Bayesian individualized models. An application based on Florida’s Medicare and Medicaid claims data demonstrates that the proposed framework can effectively evaluate the healthcare facility utilization under various scenarios.