Vincent Geffray, Senior Director Product Marketing, Techwan, an Everbridge Company – Accelerating Medical Emergency Response with AI

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Improving Medical
Emergency
Response with AI
Vincent Geffray - Everbridge/Techwan
October 7th, 2021
SIA-REMU: A public-private cross-national
collaboration to provide the best medical help quicker.

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SIA-REMU: Public-Private Collaboration

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Goals of SIA-REMU
Leverage AI to Improve:
1. The early detection of vital emergencies
2. The emergency resources utilisation
The Expected Outcomes:
• Improve the quality of the emergency response for
patients
• Optimise the usage of the healthcare system
• Build a competence center in the fields of health and
digital technology
Funding:
• 2 million euros project. 50% funded by the Interreg fund
• End of the project: 12/31/2022

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Scope of our project
Professeur Pierre-Nicolas Carron
Dr Fabrice Dami
Professor Stephan Robert-Nicoud
PhD Student: Félicien Hêche
Use AI to Improve the speed and
the efficiency of the medical
emergency response.
By considering:
• the availability of resources
(ambulances, SMUR, helicopter)
• Their physical location
• External factors: the weather, the road
traffic, density of population, risk
intelligence, civil unrest, and anything
that can disrupt a medical emergency
• Redeployment of strategic resources
Research and Development

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Today?
Automatic Calculations with:
• the time of day (day / evening / night),
• the location of intervention
• the current availability of resources/site
• area, distance, status (open or not), time
availability (available or not)
BUT always implies the redeployment of resources to
maintain population coverage

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Tomorrow with AI?
Which AI/ML?
Reinforcement Learning: Machine Learning
(ML) technique that enables an agent to learn in
an interactive environment by trial and error
using feedback from its own actions and
experiences.
Our Goal: Build an AI model to help operators to make “the best” decisions in real time,
including:
• Choice of the vehicle(s) (type, location, availability), ETA
• type of emergency, priority level of the emergency, skills and material required,
distance to scene, time to scene,
• Correlated with external factor (other interventions, weather, road traffic
condition, strategic population coverage…).
• Provide redeployment guidelines

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How AI/ML/RL ?
• For each emergency at a time 𝑡, we assign a reward 𝑅𝑡.
• The AI is trained to maximize the following expression, called the expected return
𝐸[෍
𝑡=0
𝐻
𝑅𝑡]
where 𝐻, is a given parameter.
• For example, 𝑅𝑡 could be of the form -𝛼𝑖𝑟 where 𝑖 is the priority level of the emergency
and 𝑟 the response time.
• The parameter 𝛼𝑖 weights the response time according to the priority level 𝑖. If 𝛼𝑖 is big,
then it is important to keep the response time 𝑟 small.
• Of course, we could (should!) imagine a more subtil expression for the reward 𝑅𝑡.

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Thank You,
Questions?

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Professeur Pierre-Nicolas Carron
Dr Fabrice Dami
Professor Stephan Robert-Nicoud
PhD Student: Félicien Hêche
Vincent Geffray, Product Innovation - Everbridge/Techwan
Conseiller du Commerce Extérieur de la France