The ambulance of the future may not have a driver

Ambulances (old style). Image: Getty.

The revolution in driverless vehicles will make many jobs obsolete. In the US alone, it is estimated that driverless vehicles will wipe out 4.1m jobs. Truck drivers, delivery drivers, taxi drivers and Uber drivers will be out of work, and sooner than you might think.

But automation can be a force for good, doing jobs more cheaply, safely and efficiently. In fact, there’s one service that’s crying out for more automation: the ambulance service.

Demand for ambulance services is growing rapidly in developed countries due to a combination of a growing and ageing population, an increase of chronic diseases, and a scarcity of primary care clinics and providers. This leaves the emergency services overburdened, with a dismal outlook for the future.

With driverless vehicles already on the road, some governments are looking into the possibility of driverless ambulances. Driverless ambulances and other technology could take some of the strain off the emergency services, freeing paramedics to deal with high-risk patients where each minute waiting for treatment significantly reduces a patient’s chance of surviving. This would include cardiac arrest patients, where brain damage typically starts within four to six minutes.

Initially, health services could introduce a fleet of driverless ambulances alongside their current manned models to deal with low-risk patients – essentially starting out as “medical taxis”. Low-risk patients would be picked up by a driverless ambulance and transported to the nearest hospital or clinic for treatment. With the introduction of these ambulances, the need for paramedics to respond to every call – regardless of severity – would be greatly reduced.

However, not everyone is in favour of automated ambulances. One survey of just over 1,000 people in the US found that around half said they would be comfortable riding in one.

Supported by drones

As well as delivering Amazon packages, spying on neighbours and conducting military strikes, drones could also be used by health services to take the pressure off the ambulance service. They would be especially useful for delivering medical equipment to remote locations. In fact, a start-up called Zipline is already successfully delivering blood and medicine across Rwanda.

But these services could also be used in developed countries. For example, if a doctor in a remote rural location has to treat a patient with a rare condition, but she lacks the necessary medical supplies at her GP clinic or local hospital, a drone could deliver the supplies. Alternatively, drones could be used to deliver vital medical equipment to a drop point prior to the manned ambulance’s arrival. This would allow the patient to be treated as soon as the paramedics arrive.

Drones could also be used to transport specialised equipment, medication or even blood products between hospitals. This would reduce the need for ambulances to drive further distances to find somewhere that can treat their patient.


Predicting emergencies

For several years, police forces around the world have been using sophisticated algorithms to predict areas where crime is most likely to occur. This allows police departments to deploy officers to areas of “high demand”. While these Minority Report-style systems have proven to be controversial, a similar system that predicts illness hotspots is less likely to raise eyebrows.

Such a system could be used by ambulance services. It would collect previous trip data from the ambulances (both manned and unmanned). The software would take into consideration the time of year, weather, public events (such as concerts and protests), populations (such as elderly or deprived) and past emergencies that ambulances have responded to. This would enable the driverless ambulances to locate themselves within high-risk areas when they are not in use, allowing them to respond much faster to calls.

As these systems log more and more information, they will become increasingly more accurate at predicting medical emergencies, in the same way that data mining tools, used by social media and advertising companies, get better at figuring out what food, clothes, movies and so on you like best, and what you might like in the future.

The ConversationThese new methods may seem far off, but depending on how fast healthcare systems invest and adopt these technologies, they could be changing the way we receive medical treatment within decades. In the face of ever rising demand, technology is likely to be the saviour of ambulance services, making it faster, more effective and safer. However, it may take a while before the public are comfortable with the idea.

Keegan Shepard is a PhD Candidate at Edge Hill University.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

London Overground is experimenting with telling passengers which bits of the next train is busiest

There must be a better way than this: Tokyo during a 1972 rail strike. Image: Getty.

One of the most fun things to do, for those who enjoy claustrophobia and other people’s body odour, is to attempt to use a mass transit system at rush hour.

Travelling on the Central line at 6pm, for example, gives you all sorts of exciting opportunities to share a single square inch of floor space with a fellow passenger, all the while becoming intimately familiar with any personal hygiene problems they may happen to have. On some, particularly lovely days you might find you don’t even get to do this for ages, but first have to spend some exciting time enjoying it as a spectator sport, before actually being able to pack yourself into one unoccupied cranny of a train.

But fear not! Transport for London has come up with a plan: telling passengers which bits of the train have the most space on them.

Here’s the science part. Many trains include automatic train weighing systems, which do exactly what the name suggests: monitoring the downward force on any individual wheel axis in real time. The data thus gathered is used mostly to optimise the braking.

But it also serves as a good proxy for how crowded a particular carriage is. All TfL are doing here is translating that into real time information visible to passengers. It’s using the standard, traffic light colour system: green means go, yellow means “hmm, maybe not”, red means “oh dear god, no, no, no”. 

All this will, hopefully, encourage some to move down the platform to where the train is less crowded, spreading the load and reducing the number of passengers who find themselves becoming overly familiar with a total stranger’s armpit.

The system is not unique, even in London: trains on the Thameslink route, a heavy-rail line which runs north/south across town (past CityMetric towers!) has a similar system visible to passengers on board. And so far it’s only a trial, at a single station, Shoreditch High Street.

But you can, if you’re so minded, watch the information update every few seconds or so here.

Can’t see why you would, but I can’t see why I would either, and that hasn’t stopped me spending much of the day watching it, so, knock yourselves out.

Jonn Elledge is the editor of CityMetric. He is on Twitter as @jonnelledge and also has a Facebook page now for some reason. 

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