학술 기사

Planning and Management of Hospitals and Other Healthcare Facilities: Layout Comparison


Factors including hospital space layout, patient behavior, patient flow, and medical procedures interact and relate to each other, and ultimately affect efficiency and performance of healthcare facilities. And hospital layout planning can’t ignore such interdependencies.

This research integrates discrete event simulation (DES) and agent-based simulation (ABS) to help managers examine, plan, and compare different spatial design schemes through the modeling of patient behavior, patient flow, and the establishment of evaluation indexes.

Developing an ED Overcrowding Solution to Improve the Quality of Care


Overcrowding in the Emergency Department (ED) is one of the most important issues in healthcare systems. The lack of downstream beds can affect the quality of care for patients who need hospitalization after an ED visit.

This research proposes a generic simulation model as one of the ED overcrowding solutions to analyze patient pathways from the ED to hospital discharge. The model is adaptable for all pathologies and can include several hospitals within a healthcare network. To identify relevant pathways the research team conducts pathway analysis using Process Mining.

Using Diabetic Retinopathy Care Process Model to Evaluate Interventions


Diabetic retinopathy is a diabetes complication that affects eyes. It’s also the leading cause of blindness for working-age Americans. Early detection, timely treatment, and appropriate follow-up care reduce the risk of severe vision loss from DR by 95%. Unfortunately, less than 50% of people with diabetes follow the recommended eye care screening guidelines.

The research team developed a diabetic retinopathy care process model integrating the natural history of diabetic retinopathy with a patient’s interaction with the care system.

Patient Flow Management Policy Evaluation with Simulation Software


Healthcare is facing great challenges to make processes more efficient and meanwhile provide better service to patients. Management of the intensive care unit (ICU), which is one of the most critical departments in terms of patient status and patient flow, also tries to provide better service and reduce the mortality rate.

During COVID-19, effective and efficient management is of utmost importance. A patient flow model developed in AnyLogic simulation software allows a comprehensive evaluation of eleven different management policies for controlling ICU admissions when facing capacity shortages.

Data-Driven Predictive Modeling of Resource Utilization in Healthcare


The main objective of this paper is to provide a simulation-based decision-support tool for the healthcare industry. This tool will help the hospital management decide on resource utilization, in particular bed allocation, for the next few months. With it, hospitals could predict admissions and see how newly implemented policies impact the patient’s flow.

Risk-Adjusted Healthcare Staffing Policy During the Pandemic – Modeled with Simulation Software


During the pandemic specialty physicians are working as frontline workers due to hospital overcrowding and a lack of providers. This places them as a high-risk target of the epidemic. Within these specialties, anesthesiologists are one of the most vulnerable groups as they come in close contact with the patient's airway.

An agent-based simulation model was developed using AnyLogic software to test various staffing policies within the anesthesiology department of the largest healthcare provider in Upstate South Carolina.

Clinical Pathway Analysis using Process Mining and Predictive Modeling in Healthcare: an Application to Incisional Hernia


An incisional hernia (IH) is a ventral hernia that develops after surgical trauma to the abdominal wall, a laparotomy. IH repair is a common surgery that can generate chronic pain, decreased quality of life, and significant healthcare costs caused by hospital readmissions. The goal of this study is to analyze the clinical pathway of patients having an IH using a medico-administrative database and predictive modeling. Predictive modeling in healthcare is used, among other things, to understand the times of occurrence of complications and associated costs. It enables the simulation of what-if scenarios to propose an improved care pathway for patients who are the most exposed.

Assessment of the Impact of Teledermatology using Discrete Event Simulation


Evolution of technology and the complexity of the medical system have contributed to the increasing interest in telemedicine. The purpose of this paper is to present a discrete event simulation model of the teledermatology process using the tool TelDerm. The logic of the simulation describes the telemedicine work flow from the detection of the problem to its resolution. The scenarios reflect different changes in the flow in order to quantify the impact of telemedicine on the healthcare system. Several key performance indicators measure medical and administrative workload variations for all human resources involved. In addition, we assess the impact on the patient’s journey through the process.

A Hybrid Modelling Approach using Forecasting and Real-Time Simulation to Prevent Emergency Department Overcrowding


Emergency Room (Emergency Department) overcrowding is a pervasive problem worldwide, which impacts both performance and safety. Staff are required to react and adapt to changes in demand in real-time, while continuing to treat patients.

This paper employs a case study to propose a hybrid application of discrete-event simulation (DES) and time-series forecasting across multiple centers in an urgent care network as one of the emergency room overcrowding solutions. It uses seasonal ARIMA time-series forecasting to predict overcrowding in a near-future moving-window (1-4 hours) using data downloaded from a digital platform (NHSquicker). NHSquicker delivers real-time wait-times from multiple centers of urgent care in the South-West of England. Alongside historical distributions, this data loads the operational state of a real-time discrete-event simulation model at initialization.

Simulation of epidemic trends for a new coronavirus under effective control measures


In December 2019, there was a case of viral pneumonia in Wuhan. After confirming that the pathogen of this disease is a new coronavirus, the World Health Organization (WHO) confirmed and named it 2019-nCoV. The pneumonia caused by this pathogen infection is called a novel corona virus pneumonia.

To better understand the mode of transmission of 2019-nCoV among the population and the effects of control measures, the study was conducted using agent-based modeling (ABM) to simulate an interactive environment over a certain space-time range. The study simulates the trend of 2019-nCoV infection at different levels of close contact in order to provide relevant information and references.