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South Central Ambulance Service (SCAS) is working with Ordnance Survey on a trial to increase the efficiency of ambulance fleets by minimising the amount of time when ambulances must be taken off the road for unforeseen maintenance.
With a fleet of over 300 vehicles, SCAS is testing out a predictive model of how to efficiently schedule maintenance before a vehicle succumbs to larger, possibly long-term failures. The project team includes Ordnance Survey (OS) as well as data integration, analytics, and AI company Qlik and Differentia Consulting.
The model uses two sources of telematics data from ambulances. The engine tracker monitors internal data such as engine temperature and clutch usage, monitoring location, lights, and other electrics whereas the cab tracker is linked to the 999-control room.
From this, the service can then gain new insights, which are both analytical – monitoring telemetry and the state of vehicles returning from shifts – and predictive, considering the probability of an ambulance needing servicing.
“As well as the purchasing, conversion, and equipment costs to get each ambulance on the road, it takes 12 members of staff to cover each vehicle’s weekly operational rota, all with their own associated recruitment, training, and university costs.
“Looking for other ways to innovate and improve service delivery and patient experience has led us to consider the wealth of data we capture from our vehicles. I am really excited to see what story we will be able to tell from the data and the impact this can have.
“The help we are being offered by working collaboratively with OS, Qlik, and Differentia is amazing; they are a key part of our analytical journey, providing the insight and expertise that we do not have.”
Vivienne Parsons, Senior Management Information Analyst, SCAS.
OS said it had been asked by SCAS to combine telematics data with geospatial data – specifically, the topographic and contextual data in the OS National Geographic Database – which can unlock new analytics on how road and journey characteristics could influence wear on a vehicle. By combining geospatial data with maintenance data, SCAS can build a predictive model to answer these questions.
“Ambulance services are already dedicated users of OS geospatial data, but it’s exciting to know that applying our data to this new model has great potential to improve the efficiency of the SCAS fleet, ultimately enhancing its ability to provide urgent care to those in need.
“It’s incredibly rewarding for OS to be part of this project and potentially help to apply it across other emergency service fleets longer term.”
James Armstrong, Geospatial Consultant, Ordnance Survey.
If the new trial proves successful, OS says that the potential scope of applications to other emergency services and sectors “is almost limitless.”
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