London hospitals to replace doctors and nurses with AI for some tasks
London hospitals to replace doctors and nurses with AI
for some tasks
UCLH aims to bring ‘game-changing’ benefits of artificial
intelligence to NHS patients, from cancer diagnosis to reducing wait times
By Hannah Devlin Science correspondent Mon 21 May 2018
05.21 EDT
One of the country’s biggest hospitals has unveiled
sweeping plans to use artificial intelligence to carry out tasks traditionally
performed by doctors and nurses, from diagnosing cancer on CT scans to deciding
which A&E patients are seen first.
The three-year partnership between University College
London Hospitals (UCLH) and the Alan Turing Institute aims to bring the
benefits of the machine learning revolution to the NHS on an unprecedented
scale.
Prof Bryan Williams, director of research at University
College London Hospitals NHS Foundation Trust, said that the move could have a
major impact on patient outcomes, drawing parallels with the transformation of
the consumer experience by companies such as Amazon and Google.
“It’s going to be a game-changer,” he said. “You can go
on your phone and book an airline ticket, decide what movies you’re going to
watch or order a pizza … it’s all about AI,” he said. “On the NHS, we’re
nowhere near sophisticated enough. We’re still sending letters out, which is
extraordinary.”
At the heart of the partnership, in which UCLH is
investing a “substantial” but unnamed sum, is the belief that machine learning
algorithms can provide new ways of diagnosing disease, identifying people at
risk of illness and directing resources. In theory, doctors and nurses could be
responsively deployed on wards, like Uber drivers gravitating to locations with
the highest demand at certain times of day. But the move will also trigger
concerns about privacy, cyber security and the shifting role of health
professionals.
The first project will focus on improving the hospital’s
accident and emergency department, which like many hospitals is failing to meet
government waiting time targets.
“Our performance this year has fallen short of the
four-hour wait, which is no reflection on the dedication and commitment of our
staff,” said Prof Marcel Levi, UCLH chief executive. “[It’s] an indicator of
some of the other things in the entire chain concerning the flow of acute
patients in and out the hospital that are wrong.”
In March, just 76.4% of patients needing urgent care were
treated within four hours at hospital A&E units in England in March – the
lowest proportion since records began in 2010.
Using data taken from thousands of presentations, a
machine learning algorithm might indicate, for instance, whether a patient with
abdomen pain was likely to be suffering from a severe problem, like intestinal
perforation or a systemic infection, and fast-track those patients preventing
their condition from becoming critical.
“Machines will never replace doctors, but the use of
data, expertise and technology can radically change how we manage our services
– for the better,” said Levi.
Another project, already underway, aims to identify
patients who are are likely to fail to attend appointments. A consultant
neurologist at the hospital, Parashkev Nachev, has used data including factors
such as age, address and weather conditions to predict with 85% accuracy
whether a patient will turn up for outpatient clinics and MRI scans.
In the next phase, the department will trial
interventions, such as sending reminder texts and allocating appointments to
maximise chances of attendance.
“We’re going to test how well it goes,” said Williams.
“Companies use this stuff to predict human behaviour all the time.”
Other projects include applying machine learning to the
analysis of the CT scans of 25,000 former smokers who are being recruited as
part of a research project and looking at whether the assessment of cervical
smear tests can be automated. “There are people who have to look at those all
day to see if it looks normal or abnormal,” said Williams.
Might staff resent ceding certain duties to computers –
or even taking instructions from them? Prof Chris Holmes, director for health
at the Alan Turing Institute, said the hope is that doctors and nurses will be
freed up to spend more time with patients. “We want to take out the more
mundane stuff which is purely information driven and allow time for things the
human expert is best at,” he said.
When implementing new decision-making tools, the hospital
will need to guard against “learned helplessness”, where people become so
reliant on automated instructions that they abandon common sense. While an
algorithm might be correct 99.9% of the time, according to Holmes, “once in a
blue moon it makes a howler”. “You want to quantify the risk of that,” he
added.
UCLH is aiming to circumvent privacy concerns that have
overshadowed previous collaborations, including that of the Royal Free Hospital
in London and Google’s DeepMind, in which the hospital inadvertently shared the
health records of 1.6 million identifiable patients. Under the new partnership,
algorithms will be trained on the hospital’s own servers to avoid any such
breaches and private companies will not be involved, according to Holmes.
“We’re critically aware of patient sensitivity of data
governance,” he said. “Any algorithms we develop will be purely in-house.”
Questions also remain about the day-to-day reality of
integrating sophisticated AI software with hospital IT systems, which are
already criticised for being clunky and outdated. And there will be concerns
about whether the move to transfer decision-making powers to algorithms would
make hospitals even more vulnerable to cyber attacks. after becoming victim to a global ransomware
attack that resulted in operations being cancelled, ambulances being diverted
and patient records being unavailable.
Williams acknowledged that adapting NHS IT systems would
be a challenge, but added “if this works and we demonstrate we can dramatically
change efficiency, the NHS will have to adapt.”
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