scholarly journals Hospital preparedness during epidemics using simulation: the case of COVID-19

2021 ◽  
Author(s):  
Daniel Garcia-Vicuña ◽  
Laida Esparza ◽  
Fermin Mallor
Keyword(s):  
Simulation Model ◽  
Patient Flow ◽  
Current System ◽  
Discrete Event ◽  
Arrival Rate ◽  
Gompertz Model ◽  
Daily Basis ◽  
Arrival Process ◽  
Health Care Planning ◽  
Short Term

AbstractThis paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

scholarly journals Hospital preparedness in epidemics by using simulation. The case of COVID-19

2020 ◽  
Author(s):  
Daniel Garcia-Vicuna ◽  
Laida Esparza ◽  
Fermin Mallor
Keyword(s):  
Simulation Model ◽  
Patient Flow ◽  
Current System ◽  
Discrete Event ◽  
Health Resources ◽  
Growth Curves ◽  
Arrival Process ◽  
Short Term ◽  
Hospital Preparedness ◽  
Short Term Forecasting

This paper presents a discrete event simulation model to support the decision-making concerned with the short-term planning of the necessary hospital resources, especially Intensive Care Unit (ICU) beds, to face outbreaks, as the SARS-CoV-2. Being used as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of the admission of new patients and the patient flow through the hospital facilities. For the patient arrival process, we analyze different models based on growth curves of the twenty most affected countries (until June 15) and propose the use of the Gompertz curve. The length of stay is divided into several stages, each one modeled separately. We analyze the starting of the simulation model, which requires different procedures depending on the information available about the patients currently hospitalized. We also report the use of this simulation model during the COVID-19 outbreak in the Autonomous Community of Navarre, in Spain. Every day, the research team informed the regional logistic team in charge of planning the health resources, who programmed the ward and ICU beds based on the resulting predictions.

scholarly journals Patient Flow Simulation Using Historically Informed Synthetic Data

2021 ◽  
Author(s):  
Ezra Kenny ◽  
Hamed Hassanzadeh ◽  
Sankalp Khanna ◽  
Justin Boyle ◽  
Sandra Louise
Keyword(s):  
Emergency Department ◽  
Simulation Model ◽  
Patient Flow ◽  
Discrete Event ◽  
Synthetic Data ◽  
Flow Simulation ◽  
Average Patient ◽  
Future Demands ◽  
Healthcare Administrators ◽  
Using Data

Hospital overcrowding is a major problem for healthcare systems around the globe. In order to better estimate future demands and adequate resources for coping with such demands, statistical and computerised modelling can be applied. This can then allow healthcare administrators and decision makers to quantify the impacts of various “what-if” scenarios on hospital performance measures. This paper investigates the application of Discrete Event Simulation towards optimising Emergency Department resources while measuring overall length of stay and queuing time of emergency patients as a target performance measure. In particular, we explore strategies for generating historically informed synthetic data that helps the simulation model track patient flow through the target hospital over a future time frame. Using the developed simulation model, several resource configurations are tested using data from one of the busiest emergency departments in the state of Queensland as the baseline while quantifying the impacts of such changes on key patient flow metrics. It was found that adding a single bed (and associated resources) to the emergency department would result in a 23% decrease in average patient treatment delay.

scholarly journals Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253869
Author(s):  
Michael Saidani ◽  
Harrison Kim ◽  
Jinju Kim
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Daily Basis ◽  
Health Crisis ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Rapid Result ◽  
The Usa ◽  
The Impact

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

scholarly journals Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study

2019 ◽  
Author(s):  
Joe Viana ◽  
Tone B Simonsen ◽  
Hildegunn E Faraas ◽  
Nina Schmidt ◽  
Fredrik A Dahl ◽  
...  
Keyword(s):  
Simulation Model ◽  
Outpatient Clinic ◽  
Patient Flow ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Base Case ◽  
Term Pregnancy ◽  
Full Factorial Experimental Design ◽  
Arrival Pattern ◽  
Post Term Pregnancy

Abstract Background The demand for a large Norwegian hospital’s post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital’s catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times. Methods A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.Results Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women’s length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.Conclusions The simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.

scholarly journals A DISCRETE EVENT SIMULATION (DES) BASED APPROACH TO MAXIMIZE THE PATIENT THROUGHPUT IN OUTPATIENT CLINIC

2020 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Vidanelage L. Dayarathna ◽  
Hebah Mismesh ◽  
Mohammad Nagahisarchoghaei ◽  
Aziz Alhumoud
Keyword(s):  
Discrete Event Simulation ◽  
Nurse Practitioners ◽  
Outpatient Clinic ◽  
Current System ◽  
Discrete Event ◽  
Discrete Distributions ◽  
Arrival Process ◽  
Future Research ◽  
Event Simulation ◽  
Number Of Patients

The healthcare system is a complex system which exhibits conditions of uncertainty, ambiguity emergence that incurs incoming patient congestion. Discrete event simulation (FlexSim) is considered as a viable decision support tool in analyzing a system for improvement. Using a data-driven discrete event simulation approach, this paper portrays a comprehensive analysis to maximize the number of patients in an on-campus clinic, located at Mississippi State University. The outcome of the analysis of current system exhibits that deploying a few nurse practitioners results in bottlenecks which decreases the systems’ throughput substantially due to the overall longer patients’ waiting time.  Access to the laboratory is characterized through multi-server queuing network, arrival process is followed discrete distributions, and batch sizes and arrival times are stochastic in nature. In an effort to plummet inpatient congestion at the outpatient clinic, by using empirically calibrated simulation model, we will figure out the best balance between the number of the lab technician and incoming patient during working hour. An analysis of optimal solutions is demonstrated, which is followed by recommendation and avenues for future research.

scholarly journals Digital Twin of COVID-19 Mass Vaccination Centers

2021 ◽  
Vol 13 (13) ◽  
pp. 7396
Author(s):  
Francesco Pilati ◽  
Riccardo Tronconi ◽  
Giandomenico Nollo ◽  
Sunderesh S. Heragu ◽  
Florian Zerzer
Keyword(s):  
Simulation Model ◽  
Mobile Application ◽  
Patient Flow ◽  
Discrete Event ◽  
Process Time ◽  
Digital Twin ◽  
Event Simulation ◽  
Time Period ◽  
Minimum Number ◽  
Short Time

The problem is the vaccination of a large number of people in a short time period, using minimum space and resources. The tradeoff is that this minimum number of resources must guarantee a good service for the patients, represented by the time spent in the system and in the queue. The goal is to develop a digital twin which integrates the physical and virtual systems and allows a real-time mapping of the patient flow to create a sustainable and dynamic vaccination center. Firstly, to reach this goal, a discrete-event simulation model is implemented. The simulation model is integrated with a mobile application that automatically collects time measures. By processing these measures, indicators can be computed to find problems, run the virtual model to solve them, and replicate improvements in the real system. The model is tested in a South Tyrol vaccination clinic and the best configuration found includes 31 operators and 306 places dedicated for the queues. This configuration allows the vaccination of 2164 patients in a 10-h shift, with a mean process time of 25 min. Data from the APP are managed to build the dashboard with indicators like number of people in queue for each phase and resource utilization.

scholarly journals Modeling, Assessment and Design of an Emergency Department of a Public Hospital through Discrete-Event Simulation

2021 ◽  
Vol 11 (2) ◽  
pp. 805
Author(s):  
Alexandre Castanheira-Pinto ◽  
Bruno S. Gonçalves ◽  
Rui M. Lima ◽  
José Dinis-Carvalho
Keyword(s):  
Emergency Department ◽  
Simulation Model ◽  
Emergency Departments ◽  
Patient Flow ◽  
Waiting Times ◽  
Discrete Event ◽  
Analytical Description ◽  
Northern Region ◽  
Optimized Design ◽  
Initial State

Emergency departments in hospitals are having many difficulties in achieving the performance levels required by health regulators and society. The waiting times as well as the total throughput time are examples of performance indicators that emergency departments need to improve in order to provide a better service to the community. To achieve improvement of performance, the present paper shows a methodology to assist the design process of an emergency department using simulation techniques. In this study, the emergency department of a hospital located in the northern region of Portugal was considered to test the proposed simulation technique. The emergency department initial state was assessed, in terms of patient flow, as well as the human resources needed at every stage of the service. In order to understand in depth the process that a patient goes through during an emergency episode, a comprehensive study was performed on the hospital database. This allowed the analytical description of an emergency episode, which was further used as an input to the simulation model. After developing the simulation model with the information obtained by the hospital’s database, a validation stage was performed. Finally, in order to achieve an optimized design for the emergency department several variant scenarios were considered and evaluated. This methodology proved to be very useful in determining an optimized operation for complex, and non-linear systems.

scholarly journals Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals

2021 ◽  
Author(s):  
Alberto De Santis ◽  
Tommaso Giovannelli ◽  
Stefano Lucidi ◽  
Mauro Messedaglia ◽  
Massimo Roma
Keyword(s):  
Emergency Department ◽  
Optimization Problem ◽  
Discrete Event ◽  
Arrival Rate ◽  
Black Box ◽  
Arrival Process ◽  
Accurate Estimation ◽  
Piecewise Constant ◽  
Piecewise Constant Approximation ◽  
The Impact

AbstractModeling the arrival process to an Emergency Department (ED) is the first step of all studies dealing with the patient flow within the ED. Many of them focus on the increasing phenomenon of ED overcrowding, which is afflicting hospitals all over the world. Since Discrete Event Simulation models are often adopted to assess solutions for reducing the impact of this problem, proper nonstationary processes are taken into account to reproduce time–dependent arrivals. Accordingly, an accurate estimation of the unknown arrival rate is required to guarantee the reliability of results. In this work, an integer nonlinear black–box optimization problem is solved to determine the best piecewise constant approximation of the time-varying arrival rate function, by finding the optimal partition of the 24 h into a suitable number of not equally spaced intervals. The black-box constraints of the optimization problem make the feasible solutions satisfy proper statistical hypotheses; these ensure the validity of the nonhomogeneous Poisson assumption about the arrival process, commonly adopted in the literature, and prevent mixing overdispersed data for model estimation. The cost function of the optimization problem includes a fit error term for the solution accuracy and a penalty term to select an adequate degree of regularity of the optimal solution. To show the effectiveness of this methodology, real data from one of the largest Italian hospital EDs are used.

Patient Flow Analysis Using Real-Time Locating System Data: A Case Study in an Outpatient Oncology Center

2020 ◽  
Vol 16 (12) ◽  
pp. e1471-e1480
Author(s):  
Hyojung Kang ◽  
Ethan Haswell
Keyword(s):  
Real Time ◽  
Input Data ◽  
Patient Flow ◽  
Current System ◽  
Waiting Times ◽  
Discrete Event ◽  
Cancer Center ◽  
Time Data ◽  
Care Processes ◽  
Improve Patient

PURPOSE: Electronic health records (EHRs) have been mainly used to analyze bottlenecks in care processes of outpatient oncology clinics. However, EHR data lead to some limitations in understanding patient flow because they are manually entered and not updated in real time. Data generated from a real-time location system (RTLS) can supplement EHR data. This study aims to demonstrate how RTLS data combined with EHR data can be used to evaluate potential interventions to improve patient flow in an outpatient cancer center. METHODS: EHR and RTLS data obtained from a large cancer center in central Virginia were analyzed to estimate process times and determine the various patient paths patients follow during their visit for infusion. Using the input data, we developed a discrete-event simulation (DES) model and assessed 5 what-if scenarios involving changes in staff scheduling and care processes. RESULTS: Raw RTLS data including > 3.5 million observations were preprocessed to remove noise and extract meaningful information. The DES results showed that new nursing schedules for the infusion center and improved pharmacy processes have positive impacts on reducing patient waiting times by approximately 20% and overall length of stay by approximately 3.4% to 4.6%, compared with the current system. CONCLUSION: Combining EHR and RTLS data, we were able to capture dynamic aspects of patient flow more realistically. DES models that represent a complex system based on accurate input data can help decision making on determining operational changes to improve patient flow.

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