Using Discrete Event Simulation to model the bottlenecks in the Acute Medical Unit pathway


30 Second Summary

In this project, a computer simulation of the acute medical pathway in a Devon trust was created, along with an interactive tool allowing parameters such as the staffing levels to be changed. This allowed staff to explore the optimum levels of resourcing, enabling risk-free testing of staffing and resource changes before committing to these changes in the real-world.

Discrete Event Simulation (DES)
Streamlit
Acute Medical Unit (AMU)
Emergency Departments
Acute Care
Hospitals
NHS
Authors
Affiliation

Becky Crofts

Royal Devon University Healthcare (RDUH) NHS Foundation Trust

Kayleigh Haydock

Royal Devon University Healthcare (RDUH) NHS Foundation Trust

The team’s project was to create a computer model of part of the acute medical pathway in their hospital trust. As part of this process patients come in with non-surgical emergencies, like an abnormal heart beat, and they’re triaged to decide the best route of care for them. In some cases that will be an admission to hospital, but increasingly this may involve alternatives that combine just a few hours in hospital with close follow up and perhaps even forms of remote monitoring, like monitoring their heart through a mobile app.

The aim of this project was to model part of the acute medical pathway in this hospital trust, simulate how the system currently works, and investigate whether there is optimal allocation of staff and resources. The benefit of this model is it allows for changing parameters such as adding extra staffing to see what impact that has on the pathway. Testing these changes in the real-world costs time and money, while computer simulation allows you to trial these changes with minimal cost and no risk.

The model isn’t validated yet, but when it is it’ll be handed over to users (e.g. clinicians and managers) to support them in making decisions about staffing or resourcing. They’ll be able to test those changes mentioned above without risk before proceeding to a real-world test of change.

Access the Github Repository