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Optimising the Logistics of Your EMS Agency: The Role of Operations Research

by Paul Day on 16-Oct-2017 15:09:04


A classic logistics problem that many organisations face is how to meet some form of dynamic demand for services with limited resources under some cost and operational constraints. On top of this demand is often some form of performance incentive/penalty or service level expectations from the source of the demand and other parties.

In the context of logistics for EMS operations, the primary demand comes from people requiring medical assistance within your community and the primary resources are your skilled paramedics, expensive ambulance vehicles and any wider healthcare resources such as hospitals. The pressures on these resources to reduce costs, maintain performance improve patient outcomes and so forth are increasing each. Unfortunately, you do not have infinite funds, so difficult decisions and trade-offs have to be made to meet these demands. This leads to you asking business questions and thinking about the impact of different operational scenarios.  You may question where your vehicles should be, the best locations for bases/facilities, what shifts should be established, which hospitals to transport to, how to reach response targets and more. How, then, do you go about working out the important questions to ask and then confidently answering these questions?

A large portion of the answer can come from your EMS logistics experience and the use of generic tools such as GIS systems or advanced CAD systems. Although you think you understand your operation and are intimately familiar with it, there are some areas where more evidence is really needed before you take a leap of faith in terms of operational change. This is where technology comes in to play to offer more EMS-specific solutions to assist with the many challenging logistics questions.

The Technology Toolset

The technology toolset for EMS logistics includes Operations Research, a discipline where complex real-world business operations and questions such as those posed above can be expressed mathematically. The resulting model can be queried using analytical methods in order to make better decisions both tactically and strategically. This broad toolset nicely aligns with the logistics challenge for an EMS operation. It can be used in conjunction with other related and overlapping tools such as simulations, probability theory, statistics/analytics, data science, visualisation and machine learning. In addition to providing more robust justification, an interesting feature of such approaches is that they can find surprising and unintuitive solutions or “needle in a haystack” solutions–solutions that are typically not discoverable by generic tools or the human brain and yet often provide a substantial improvement for your operation.

An underlying and fundamental part of Operations Research is the mathematical model, a mapping from real-world EMS operations. The main source of this model is your data (including geospatial, road network, historic or forecasted incident demand, base, vehicle and shift data), operational rules or costs (including dispatch, deployment and performance rules) and the clear definition of some objective (including the desire to minimise weighted cost or evaluate some specific change to your data or rules). Once the mathematical model has been formulated, it can be “solved” to provide insight and ultimately map back to the real world through some actionable change to your EMS operations. These tools can be used in various ways to address some of the broader healthcare challenges and changes you face.

So if that’s the theory, why is the application so hard?

The real world gets in the way of fully executed theories. The process described above involves mapping real-world information to a mathematical model, solving the mathematical equation in some way, and then mapping results back to the real world again. As there is tremendous translation occurring and various steps to follow, each step in this process has its own challenges and traps. The challenges and traps that may be encountered are vast.  In the next blog post of this series, we will cover these pitfalls and explore how to overcome them.

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This post was written by Paul Day

Paul Day is Director of Development for Optima solutions at Intermedix. He has more than 20 years of experience in software development, and using maths and technology to solve real world business problems. Paul obtained his bachelor’s degree in Engineering from the University of Auckland and holds a PhD in Operations Research from the University of Auckland.