Simulation in Healthcare


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In this era, simulation has many meanings to many people. To a simulationist or industrial engineer, simulation is a tool for imitating the operations of processes. Simulations model processes by using math equations that match the actual process, Often, simulation in done vial software programs, such as ProModel, FlexSim, Simul8, and Simio.

The tools helps figure out how a system will behave, identifying bottlenecks and points where flow is not optimal. In healthcare design simulation can ensure the right quantity of spaces (the number of CT scanners that we need), the right location of spaces (the impact of location trauma rooms next to the ambulance entry versus locating them next to the walk-in entrance), and the right size of spaces (the number of seats needed in this waiting area).

In simulation, there are five important steps in model development:
1. Conceptual: Define the goals and objectives of your model.
2. Specification: Gather data and define the algorithms.
3. Computation: Program the model.
4. Verification: Is the model I built correct?
5. Validation: Did I build the correct model?
Many of us tend to focus on the computation step; however, the other steps are just as important.

Many applications of simulation on healthcare that present on the conference from around the globe. One talk, “A Simulation-based Analysis on Reducing Patient Wait Time for Consultation in an Outpatient Clinic,” focused on an eye clinic at a hospital in Singapore. Another talk, “Simulation as a Guide for Appointment Template Redesign in Endoscopy,” looked at optimizing the scheduling of patients for endoscopy procedures at a large academic medical center in the United States. And the French healthcare system was represented in the talk, “Ensuring the Overall Performance of a New Hospital Facility through Discrete-event Simulation.”

Simulation is one tool that healthcare improvement professionals use in their work. It helps us predict how a system will behave. It allows us to test scenarios without affecting the real system. Often, simulation in healthcare can facilitate consensus on what changes we want to make achieve better results. However, simulation can be tricky if it is programmed incorrectly or the results are not interpreted correctly. Bad decisions can be made based on these errors. It is important to have someone who understands the process being simulated so that the computer programming accurately reflects the process.

Further, data is necessary to identify the statistical distribution that each step of the process follows. A lot of technicalities must be accounted for, and that takes time and knowledge of statistics, the process being modeled, and the simulation software. IEs are well-versed in statistics and process analyses, and that makes them perfect for trying simulation in healthcare.