healthcare
Anylogic apps area: COVID 19
COVID 19: The Good, the Bad and the Agent Based Model
In the middle of the COVID-19 outbreak, simulation models trying to predict its behavior and outcomes are a dime a dozen. Are there any differences between them or is it a “seen one, seen them all” type of deal?In the middle of the COVID-19 outbreak, simulation models trying to predict its behavior and outcomes are a dime a dozen. Are there any differences between them or is it a “seen one, seen them all” type of deal?
In this series of articles, we present the difficulties of developing a good epidemic model, why they are so hard to create, what we believe are the shortcomings of the classic SEIR approach to this problem, and what the advantages are of an Agent-Based Model. We will also share our own fully parameterized and fully adaptable ABM model for you to use and adjust as you wish, as well as the results we’ve obtained from it.
אנחנו שמחים שבאפשרתנו לעזור למאבק בקורונה
"בעזרת תוכנת AnyLogic, אנחנו ממשיכים לייצר יישומים מתקדמים בפרקי זמן מינימלים. מודלים שהיו לוקחים לנו שבועות, לוקחים בעזרת יכולות הכלי ימים בודדים." נאמר על ידי ת', ראש קבוצת 'מוסטנג' ברפא"ל. היישום שפותח, מאפשר למשרד הבריאות למדל את התהליך רפואי של בדיקת COVID-19 במעבדה. מטרת ההדמיה, לסייע בחיזוי יכולת בדיקה ולתכנן את המשאבים הדרושים.
ניתן לייבא ערכי מעבדה על ידי לחיצה על לחצן הניסוי משמאל והעלאת קובץ excel לקלט.
A Hybrid Modelling Approach using Forecasting and Real-Time Simulation to Prevent Emergency Department Overcrowding
A Hybrid Modelling Approach using Forecasting and Real-Time Simulation to Prevent Emergency Department Overcrowding
Emergency Room overcrowding is a pervasive problem worldwide, which impacts on both performance and safety. Staff are required to react and adapt to changes in demand in real-time, while continuing to treat patients. The multifactorial nature of the problem does not suggest a single solution. Previous studies found contributing factors included increases in elderly presentations, complex presentations, low acuity presentations, lack of staff, reduced access to alternative services, and declining bed base. These factors point toward a local solution, which includes demand prediction, demand management and capacity management.
Demand management can take two forms. Firstly, patients can be provided with additional information that can support decisions about the most appropriate place to attend, based on their beliefs regarding the acuity of their condition, and knowledge of current wait times. Secondly, as per the aim of this paper, demand management can take the form of redirecting appropriate patients to alternative services as queues become unmanageable.
Emergency Room overcrowding is a pervasive problem worldwide, which impacts on both performance and safety. Staff are required to react and adapt to changes in demand in real-time, while continuing to treat patients. The multifactorial nature of the problem does not suggest a single solution. Previous studies found contributing factors included increases in elderly presentations, complex presentations, low acuity presentations, lack of staff, reduced access to alternative services, and declining bed base. These factors point toward a local solution, which includes demand prediction, demand management and capacity management.
Demand management can take two forms. Firstly, patients can be provided with additional information that can support decisions about the most appropriate place to attend, based on their beliefs regarding the acuity of their condition, and knowledge of current wait times. Secondly, as per the aim of this paper, demand management can take the form of redirecting appropriate patients to alternative services as queues become unmanageable.
As one of the emergency room overcrowding solutions, 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.
To support an integrated hybrid model, the DES model, which runs in minutes, has been built in AnyLogic, as the download and parser programs are written in Java. Due to the availability of time-series forecasting libraries in Python, the forecasting models use a Java/Python interface. The development of the integration framework, which brings together the near real-time data, forecasting models and real-time simulation, is a work in progress.
Patients can enter the model via the walk-in route or ambulance. A one-week hourly schedule of arrival rates (from 2018 data) defines entry into the model, and patients are allocated a severity level (triage category) on arrival, according to historical probability. Data for 2016/17/18 is stable for this distribution, and each triage category conforms with the overall daily arrival pattern. Patients are allocated a probability of X-Ray according to their triage category. Patients can be discharged home via any component part and the performance monitoring ‘clock’ stops for discharge home, or admission to the EAU, CDU or inpatient wards. We are working on the mechanisms to automate the emergency room model execution process such that as soon as new data is downloaded, it is parsed, the model variables are assigned relevant data items, and the execution of the emergency room overcrowding solution model starts.
As one of the emergency room overcrowding solutions, 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.
To support an integrated hybrid model, the DES model, which runs in minutes, has been built in AnyLogic, as the download and parser programs are written in Java. Due to the availability of time-series forecasting libraries in Python, the forecasting models use a Java/Python interface. The development of the integration framework, which brings together the near real-time data, forecasting models and real-time simulation, is a work in progress.
Patients can enter the model via the walk-in route or ambulance. A one-week hourly schedule of arrival rates (from 2018 data) defines entry into the model, and patients are allocated a severity level (triage category) on arrival, according to historical probability. Data for 2016/17/18 is stable for this distribution, and each triage category conforms with the overall daily arrival pattern. Patients are allocated a probability of X-Ray according to their triage category. Patients can be discharged home via any component part and the performance monitoring ‘clock’ stops for discharge home, or admission to the EAU, CDU or inpatient wards. We are working on the mechanisms to automate the emergency room model execution process such that as soon as new data is downloaded, it is parsed, the model variables are assigned relevant data items, and the execution of the emergency room overcrowding solution model starts.
Anylogic apps area: Healthcare
These companies already use AnyLogic for healthcare
Healthcare services are being redesigned and improved in terms of reducing waste, enhancing the patient experience and contributing to population health. Models linked with data about individual patients and particular services are being used to improve clinical decisions by health professionals, engaging patients and their carers in self-management, and tailoring treatment approaches to individuals based on their genetics, body system physiology, psychology and social networks. Modelling and simulation are also used at the policy and program level to evaluate the cost effectiveness of mixtures of interventions, not only in testing and treatment, but also in health promotion and risk prevention. An important aspect is to understand the different results of programs in different contexts,
especially in terms of social determinants of health and changes in health disparities and inequities. The challenges of organizing health and social systems to improve wellbeing, healthcare value and accessibility require multiple ways of abstracting problem situations at the aggregate and individual level, including process and agent interaction events, and understanding feedbacks and network structures. AnyLogic can combine theory and data in simulation models which can test interventions in different virtual experiment scenarios to assess their suitability for a particular country, region, organization, service or target group.
Emergency Department example
A simplistic model of an Emergency Department designed primarily to demonstrate the usage of network markup elements in conjunction with Process Modeling Library. Specific markup shapes such as nodes and paths are used to define facility layout. There are three types of resources in Emergency Department: staff (nurses, PAs and technicians), static resources (triage rooms, express care rooms, X-ray room) and portable (ultra sound device). The process includes registration, triage, and either X-ray or U-sound examination. Technicians have their own sub-process to prepare for the U-sound examination and wrap-up afterwards. The resource utilization and patients’ length of stay statistics are collected. You can control the resource capacities on-the-fly and see their impact on utilization and LOS.
For the demo click here
