The Future of Prediction

I have read the positions stating that calls for emergency services are completely random (justifying the reason they are often called “accidents”) and therefore not able to be predicted. But both academic literature and practical experience show that demand prediction can be an effective tool in helping to balance scarce resources (ambulances and their trained crews) with public demand (requests for emergency responses even without taking into account the abuses to the system as discussed in a previous posting on the problem of “frequent flyers”) while still improving response times and controlling costs.

For anyone who thinks all of this sounds too good to be true, there are examples of where expensive technology is not having the desired affect. One such location is Lee County EMS in Florida where not only have response times not been improved, but ambulances are burning more fuel than ever and the critics include the very paramedics it is supposed to help. While predicting where the next 911 call will come from may be similiar to ”picking the winning card at a casino” as the Florida investigative news reporter suggests, that isn’t really the objective. We don’t need to know which phone will make the next call, it is enough just knowing the probability of a call coming from any given location within the service area. This may be a subtle distinction, but one that makes a huge difference at MedStar in Fort Worth or Life EMS in Grand Rapids where response times were dramatically improved by taking the next step beyond simple demand prediction and placing ambulances at positions where they can be the most effective.

1 Comment

  • daleloberger says:

    I have heard a comment that the word “prediction” is probably not the best term to describe the process of planning for upcoming demand. There is nothing “supernatural” about it, but rather it is a mathematical procedure of forecasting demand based on past history (and the fact that humans are creatures of habit within demographic collections.) From now on, I will use the more precise term of “forecasting” instead of prediction.

    It is still true, however, that different forecasting models will provide different results. Research has shown that the MARVLIS demand modeling is clearly among the best in the industry resulting in about 80% of realized demand being properly forecast in about 5% of the service area based on their current customer profiles.

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Comments
Jeffrey Hammerstein
Stop Dissing Response Times and Start Dissecting the Argument
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