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Saving lives with intelligent ad hoc transfers of ambulances - Civic Innovation Platform

Saving lives with intelligent ad hoc transfers of ambulances

The idea put forward by the Munich fire service and LEAD Machine Learning GmbH comes into play when time is of the essence: large volumes of data need to be processed and predictions made in situations where potential supply bottlenecks may occur. Using an AI application, these potential gaps can be identified in good time and emergency vehicles relocated to the affected regions on an ad hoc basis. The aim is not only to increase the probability of survival, but also to counteract the lack of resources (e.g. due to a shortage of personnel) and to improve work organisation and occupational health and safety.

What makes you a strong team?

We are united by the overarching goal of the project: saving lives! The Munich fire service, with its integrated control centre, and the Berlin-based start-up LEAD Machine Learning are a smart combination, as we combine a wealth of experience and a large data set from the control centre with the agility and AI knowledge of a young company. Together, we want to set up an AI project with myriad social value!

Explain your idea in three sentences.

Due to the limited availability of ambulances and personnel, it is essential to intelligently predict the volume of call-outs in order to avoid supply gaps. Using an AI forecast, the control centre should now be able to adequately predict the areas in which bottlenecks will occur. As such, the control centre can arrange for the provision of emergency vehicles to be increased and fine-tuned, even before bottlenecks occur.

What makes your idea special?

The idea is innovative, because there is a major pain point that has not yet been solved. It may soon lead to the saving of lives without necessarily increasing the resources required, as there is a shortage of personnel that could otherwise only be offset by long-term initiatives. So far, no control centre with a data set as large as the one in Munich has been involved in such an idea. This is a unique opportunity to have a huge amount of data from recent years available for training AI.

What’s next?

We will now collect the necessary data in detail, align processing with data protection requirements and then work on a prototype AI. This will then be deployed at the integrated control centre in Munich and, in particular, will support the control centre during major events such as the Oktoberfest or EURO 2024, meaning that citizens can be helped as quickly as possible.

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