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London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

London hospitals to replace doctors and nurses with AI for some tasks

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. Hospital IT systems were brought to a standstill last year 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.”

Some children reach brink of suicide before getting help with mental health, charity warns

Britain is confronting a mental health crisis because resources for children are so stretched that some only receive help if they seriously self-harm or attempt suicide, Barnardo’s has warned.

Javed Khan, chief executive of Britain’s largest children’s charity, said that young people’s mental health had never been worse in the organisation’s 152-year history. Radical action was needed, he said, because funding cuts had forced charities to abandon vital services.

“It’s never been as bad, and in another five years’ time it’s going to be even more complex,” Khan told the Observer. “This mental health crisis is getting more severe and more difficult by the day. The numbers keep going up. Educational psychologists are pulling their hair out – they haven’t got the resources. They can’t respond as fast as they need to.

“We are going to regret this period if this goes on for too long. We are going to rue the day when we took our eye off the ball.”

Neera Sharma, assistant director of policy at Barnardo’s, said that in some parts of the country the pressure on resources was so severe that only the most extreme cases received help. “The threshold is suicidal in some cases; the child would have had to have attempted suicide or committed serious self-harm to get a response,” Sharma said.

Speaking before Barnardo’s annual lecture this Wednesday, where representatives of Jeremy Hunt, the health secretary, will be among the audience, Khan urged the government to adopt a dramatic new approach.

The lack of resources has forced the charity to walk away from 1,033 contracts during the past year because the money available to local authorities meant it could not offer a sufficient service, Khan said. “They are tightening their belt to a point they cannot tighten it any more. They are asking for more to be delivered for far less resources than ever before, and there is a tipping point where you just can’t deliver a safe, high-quality service,” said Khan, who is also a member of the advisory board for the children’s commissioner for England.

One way the government could save money would be to scrap the traditional tendering process in favour of a more collaborative approach between the state and charities: “I don’t think the tendering model is sustainable – there aren’t enough resources in the system,” said Khan.

The latest on the UK’s mental health problem emerged on Thursday when statistics showed that almost one in five children could be at risk of having mental health issues later in life, according to the study of more than 850,000 seven-to-14-year-olds.

Figures from NHS trusts in England in November revealed that 60% of children and young people referred for specialist care by their GP were not receiving treatment. In December the government published a green paper on mental health problems but Khan said that the plans lacked ambition, falling significantly short of what he felt was required.

“If you analyse it, then three-quarters of children are going to get no support,” he said. “The response is insufficient, it’s not broad enough, there is limited financial detail. It talks about rolling out a number of initiatives in a number of areas but funding is only secured to these areas until 2023. The prime minister has talked about this issue as a burning injustice but we don’t think the action is matching the rhetoric.”

Last month Hunt intervened in the debate to condemn social media companies for “turning a blind eye” to mental health damage suffered by children who have uncontrolled access to their online platform.

Khan said social media was an issue – comparing new technology to “allowing a film crew into the bedroom” – and that they were also liaising directly with companies such as Google and Facebook to limit potential harm to young people.

In the UK, Samaritans can be contacted on 116 123. In the US, the National Suicide Prevention Lifeline is 1-800-273-8255. In Australia, the crisis support service Lifeline is 13 11 14. Other international suicide helplines can be found at befrienders.org

The Guardian view on shoe-box Britain: space is good for us. Let’s have some | Editorial

Housing is the defining domestic political issue of the day. It’s the nearest that most voters get, personally, to the wider economy. The latest research shows that many families are cramming into new homes that a generation ago would have been thought too small to live in. Despite televisions getting bigger, living rooms are a third smaller than the 1970s. While no celebrity chef is without a runway-sized island to prepare meals, kitchens are 13% smaller than in the 1960s. According to the LABC Warranty study, homes are now being built not just with smaller rooms, but fewer of them. If true, families have less space for children to study in and less room to dissipate the strains of relationships. What is agreed is that England has the smallest homes by floor space area of any EU country. It’s also not in doubt that extreme overcrowding can have profound effects on family health and social cohesion.

So it is bizarre that the UK is one of the few western European nations to have no mandatory minimum space standards for housing. Little wonder that other nations have bigger homes while the UK gets shoe boxes. Ministers did introduce voluntary space standards for new homes in 2015, but as these could be ignored they only gave the appearance of solidity to pure wind. When this ruse was called out in 2016, the government said it would review how these were operating. Nothing has appeared. Part of this is to do with a rightwing legacy: Margaret Thatcher got rid of rules that set a reasonable internal size for public housing. Reintroducing legal standards in the interests of society is the right thing to do. It would also be a repudiation of a Thatcherite legacy, and is at odds with the direction of travel in housing.

Under the Conservatives, housing interventions designed to promote social solidarity almost disappeared. The distribution of housing space is dominated by a largely untrammelled market in land. This has led to re-emergence of a housing inequality in space. By some calculations, the most spaciously housed tenth of the population have five times as many rooms per person than the most overcrowded tenth. The counter argument is that market mechanisms will sort this out: older people who have houses will die and release stock on to the market. Yet this reasoning does not stand up to scrutiny.

Houses are needed and the reasons are largely to do with social justice, inequality and the distribution of political power. There must be an acknowledgment of the psychological comforts that domesticity affords. Past rules would need to be updated – homes must no longer find space for dining tables and upright pianos but be designed for the activities and furniture that are typical today. The Grenfell Tower tragedy provided a new background for politicians to think about housing. For too long politicians have cultivated voters reliant on house prices remaining high, and rising. The policies that have allowed this have had a damaging impact on a growing proportion of the population. So far Theresa May’s government has exaggerated how far it is prepared to intervene on the side of this section of society. Setting mandatory standards for house sizes is one test of whether this rhetoric will yield to reality.