Discrete Event Simulation for Healthcare Organizations

Instructor: Sudha Aravindan

Sudha has a Doctor of Education degree in math education and is currently working as a Information Technology Specialist.

Have you heard of discrete event simulation (DES), where modeling is used to represent complex systems as a sequence of discrete events? In this lesson, you'll learn about DES as it is used increasingly in healthcare for improvement of services.

What is Discrete Event Simulation in Healthcare?

Matthew's private clinic is fast expanding. He is unable to keep up with new patients, and often there are not enough beds to schedule much needed procedures on time. He is facing the challenges of meeting increased patient demand with the limited resources that he has available. Matthew decides to make use of discrete event simulation (DES) to create a virtual model of his current environment so that he can visualize and plan for how to optimize use of current resources. He would also like to make use of DES to learn if expansion would be beneficial to accommodate his growing needs.

Discrete event simulation is a computerized virtual modeling method where interactions between patients and environments in a healthcare system can be modeled to make decisions for improving the services. Using a discrete simulation allows Matthew to model the simulation depending on the sequence of events that happen. So, for example, when modeling the availability of beds, patient responses to treatment is an event, and complications arising from the treatment is another event. Discrete simulation is different from continuous simulation where events are considered in real time.

DES modeling in healthcare helps identify inter-dependencies and optimize utilization of resources
DES in Health Care

Applications of DES in Healthcare?

Matthew is hoping to use the DES system to get data that will help him study potential solutions in a virtual environment before they are implemented. DES will help him find operational solutions for some of the pressing problems he has been facing recently: in-patient and out-patient admissions, scheduling procedures, managing availability of beds, and optimal use of ancillary resources like pharmacies and labs.

Through the use of DES virtual modeling system, Matthew is hoping to examine existing performance standards and the potential for changes and gather data that might help him enhance his current facilities. The performance measures he is looking for relate to patient care, including patient wait time, and resource utilization, including bed occupancy, MRI system use, and nurse availability.

DES Process

Matthew worked with a DES developer named Ann to define performance measures. The goal was to use DES modeling techniques to forecast and arrive at estimations for optimal use of the existing facilities and possible expansions if needed. Ann gave Matthew an overview of the DES process:

  • Identify and define variables for the study (patient wait time, number of beds occupied at specific times, etc)
  • Gather, clean and validate the data for statistical analysis (the data may be available in hand written records or could be exported from software that is currently used)
  • Analyze the results to understand the current system
  • Construct simulation models and run different scenarios (what if the number of beds are increased, what if the patient wait time is decreased)
  • Make recommendations for improvement

Benefits of DES

DES has a number of benefits in the healthcare realm:

  1. Important variables, such as wait time of patients, time to complete a procedure and time for availability of resources, can be studied through the simulation
  2. DES helps to identify the interdependencies between available resources and the resources that are in demand
  3. DES can help predict future outcomes when the variables are altered (what if the wait time is shorter; what if there is an unexpected delay in time to complete a procedure)
  4. DES can help identify ways to optimize utilization of equipment and resources by analyzing the interdependencies between resources available and in demand

Best Practices

There are a number of best practices that can help make DES an useful tool for healthcare services.

  • Decision points should be identified:
    The model should be able to identify and define appropriate decision points, such as the point at which decisions are made for in-patient or out-patient stay.

  • Decisions should be based on current implementations:
    For the simulation to be accurate, the decision points should be based on current implementations of guidelines and standards. Data obtained through analyzing patient records, and observed decisions, for example would be more accurate than simply asking clinicians and nurses.

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