Emergency number AI can save lives by spotting heart attacks more quickly
A woman in Copenhagen hears a loud crash in the next room and rushes in to discover her father sprawled on the floor, unresponsive.
She quickly calls Denmark's health-emergency hotline, where a person answers the phone - but a computer is eavesdropping. As the operator runs through a series of questions - the patient's age, physical condition, what he was doing when he fell - the computer quickly determines the man's heart has stopped and issues an alert.
"Those human dispatchers have an amazingly hard job," says Andreas Cleve Lohmann, co-founder of Corti, a Danish artificial intelligence software house that created the program. "This software can help them save lives."
Corti's AI employs machine learning to analyse the words a caller uses to describe an incident, the tone of voice, and background noises on the line.
The software correctly detected cardiac arrests in 93pc of cases, versus 73pc for human dispatchers, according to a study by the University of Copenhagen, the Danish National Institute of Public Health, and the Copenhagen EMS.
What's more, the software made its determination in an average of 48 seconds, more than a half-minute faster than the humans did. False positives-mistakenly concluding that a person is having a heart attack - were the same for both at 2pc. With a rapid diagnosis, dispatchers can quickly give the caller instructions on how to perform CPR or where to locate a defibrillator to shock the heart back into action.
That can make the difference between life and death. Danish studies have found that a patient's 30-day survival rate triples when a dispatcher recognises cardiac arrest during an emergency call.
"Seconds matter," says Freddy Lippert, head of Copenhagen's EMS, which provided more than 150,000 recorded calls to test the algorithm. "When I first saw their results, I thought this is too good to be true," says Lippert, who was so sceptical he asked Corti to test it again. This spring, Copenhagen began a large-scale randomised trial with live calls, and if it's successful, the city plans to use Corti on all its emergency hotlines.
Lohmann launched Corti with money from the 2013 sale of his first startup. He wanted to create a voice-based interface to help doctors make diagnoses. But in machine learning, the data available often dictates the product you can build - and one of the best audio archives in medicine belongs to the Copenhagen EMS. Unlike many other localities, the city logs and analyses all its emergency calls and tracks patient outcomes. It had already studied how well its human dispatchers recognised cardiac arrest, giving Corti a good benchmark.
This autumn, Corti will begin studies in five other EU countries and beyond. "Our plan is to plant flags with the best emergency medical departments in the world," says Lohmann who has 22 employees in Copenhagen, Seattle, and Paris. (Bloomberg)