Academic articles

Evaluation of The Effect of Chickenpox Vaccination on Shingles Epidemiology Using Agent-Based Modeling

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 Emergency Plan Modeling and Simulation in Case of Major Flood

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.

Simulation-Based Design and Traffic Flow Improvements in the Operating Room

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.

Data-Driven Simulation for Healthcare Facility Utilization Modeling and Evaluation

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.

Optimizing Home Hospital Health Service Delivery in Norway Using a Combined Geographical Information System, Agent Based, Discrete Event Simulation Model

Home hospital services; provide some hospital level services at the patient’s residence. The services include for example: palliative care, administering chemotherapy drugs, changing dressings and care for newborns. The rationale of the service is that by providing high quality care to patients at their homes their experience of the care is better and hence they respond to the treatment and/or recover quicker and are less likely to need to report to hospital to receive care for more serious/expensive conditions. The aim of this study is to evaluate the effectiveness of the home hospital service, to optimize the current configuration given existing constraints and to evaluate potential future scenarios. Using a combined discrete event simulation, agent based model and geographical information system we assess the system effects of different demand patterns, appointment scheduling algorithms (e.g. travelling salesman problem), varying levels of resource on patient outcomes and impact on hospital visits.

Exploring Cannulation Process in Chemotherapy through a Computer Simulation

The aim of this study is twofold. Firstly, to demonstrate how combining computer simulation, data from multiple data sources, and statistical methods, can extend the understanding of the issues associated with process modelling and analysis in healthcare environment, and therefore contribute to improvements in resource utilisation and safety in hospitals. Secondly, to provide simple re-useable methodology for cross-validation of multiple data-sources such as interviews, hospital IT data management systems and simulation results. The insights from this study are threefold. Firstly, the accuracy of the estimates of duration of cannulation obtained through the interviews with the nurses and the chemotherapy unit manager is very high. Secondly, although the duration estimates were precise, the process descriptions obtained through interviews with nurses were oversimplified or incomplete and therefore did not realistically reflect complexity of a medical process with a significant number of relatively rarely occurring exceptions. Thirdly, by combining multiple data-sources it is possible to reduce costs associated with observation as a most expensive data-capturing approach. A detailed exposure of the methodology including step-by-step description is provided to facilitate conducting similar research in hospitals in the future.

Evaluation of discovered clinical pathways using process mining and joint agent-based discrete-event simulation

The analysis of clinical pathways from event logs provides new insights about care processes. In this paper, we propose a new methodology to automatically perform simulation analysis of patients’ clinical pathways based on a national hospital database. Process mining is used to build highly representative causal nets, which are then converted to state charts in order to be executed. A joint multi-agent discrete-event simulation approach is used to implement models. A practical case study on patients having cardiovascular diseases and eligible to receive an implantable defibrillator is provided. A design of experiments has been proposed to study the impact of medical decisions, such as implanting or not a defibrillator, on the relapse rate, the death rate and the cost. This approach has proven to be an innovative way to extract knowledge from an existing hospital database through simulation, allowing the design and test of new scenarios.

A structured approach for constructing high fidelity ED simulation

This paper presents a structured approach to building a high-fidelity simulation for an emergency department. Our approach has three key features. First, we use the concept of modules as a building block for modeling. A module is a minimum unit that has clinical or administrative meanings in ED operation, and it consists of low level operational activities. Second, we use a structured template to formally represent modules, and we adopt notations and grammars from the business process modeling notation. This provides an enhanced clarity and transparency, which proves very useful in extracting necessary data from a hospital database or from interviewing ED staff. Finally, we define an interface, specifically data structure and handler, for converting information represented in the modules into simulation languages. This interface makes it possible to seamlessly link the modeling process to the implementation process in the simulation construction.

Modeling of healthcare systems: past, current and future trends

Increasing demand for healthcare services, due to changes in demographic shifts and constraints in healthcare funding, make it harder to manage effective, sustainable healthcare systems. Many healthcare modeling exercises have been undertaken with the aim of supporting the decision-making process. This paper reviews all of the 456 articles published by the Winter Simulation Conference over the past 48 years (1967–2015) on the subject of modeling and healthcare system simulation, and analyzes the relative frequency of approaches used. A multi-dimensional taxonomy is applied to encompass the modeling techniques, problem applications and decision levels reported in the articles. One of the most significant changes in the modeling of healthcare systems is the fact that Discrete-event Simulation (DES) is no longer used as an autonomous method, but rather as an integral part of the solution. The mixed-methods, hybrid and multi-paradigm approaches feature strongly in the current direction of modeling in healthcare systems.

Hospital processes within an integrated system view: a hybrid simulation approach

Processes in hospitals or in other healthcare institutions are usually analyzed and optimized isolated for enclosed organizations like single hospital wards or certain clinical pathways. However, many workflows should be considered in a broader scope in order to better represent the reality, i.e., in combination with other processes and in contexts of macro structures. Therefore, an integrated view is necessary which enables to combine different coherences. This can be achieved by hybrid simulation. In this case, processes can be modeled and simulated by discrete simulation techniques (i.e., DES or ABS) at the meso-level. However, holistic structures can be comfortably implemented using continuous methods (i.e., SD). This paper presents a theoretical approach that enables to consider reciprocal influences between processes and higher level entities, but also to combine hospital workflows with other subjects (e.g., ambulance vehicles).