Tag Archives: doctors’

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.”

Poor lose doctors as wealthy gain them, new figures reveal

Fewer GPs are choosing to work in poorer areas but more are joining surgeries that look after wealthier populations, new official figures reveal.

The exodus, uncovered by Labour MP Frank Field, is exacerbating the existing “under-doctoring” of deprived populations – the lack of family doctors in places where poorer people live.

Experts said the widening divide between rich and poor areas in GP numbers – which is one of England’s starkest health inequalities – would force the least well-off to wait longer for an appointment, even though they are generally sicker and die earlier than the rest of the population.

Frank Field MP


‘Most worrying is the number of GPs ceasing to serve people towards the bottom of the pile,’ said Frank Field MP Photograph: Anthony Devlin/PA

“A decade ago the country was beginning to make some serious inroads into the under-doctoring of the poorest areas. What these grim figures show is that in recent years that progress has not only stalled, but actually gone into reverse,” Field told the Observer.

“The most worrying trend here is the number of GPs ceasing to serve people towards the bottom of the pile, while at the same time people in the wealthiest areas have benefited from an even better service. Vulnerable people are having to suffer in silence without being able to see a GP.

“Here’s another example of everything going in the wrong direction if our goal is to equalise health opportunities and outcomes. It is a new appalling face of inequality in modern Britain.”

There were 8,207 GPs working in areas containing the most deprived quintile of the population in England in 2008. But by last year that number had fallen to 7,696 – a drop of 511 – according to the response to a written parliamentary question Field asked recently.

But over the same decade the number of family doctors working in the most prosperous fifth of the population increased from 4,058 to 4,192 – a rise of 134, public health minister Steve Brine told Field.


We desperately need more GPs right across the country… People in deprived areas often need more access to GP services

Dr Helen Stokes-Lampard

“These figures show a really disturbing trend, particularly given that low-income areas were already under-doctored before this latest fall took place”, said Norman Lamb, the Liberal Democrat MP and ex-coalition health minister. Ministers needed to create a “patient premium”, modelled on the pupil premium introduced in 2011, to ensure more money reaches surgeries in poorer areas, he added.

Last week it emerged that GP numbers in England had fallen by 542 in the past year, despite a high-profile government pledge in 2015 to increase the workforce by 5,000 by 2020. Patients were facing longer waits for appointments and greater difficulty getting to see their own GP.

“We desperately need more GPs right across the country,” said Dr Helen Stokes-Lampard, chair of the Royal College of GPs.

“But we know that some areas – often more deprived areas, but also rural and some coastal areas – are finding it more difficult than others to recruit.

“The paradox is that people living in deprived areas tend to have a greater number of long-term conditions and more complex needs, and actually they often need more access to general practice services.”

Jeremy Hunt, the health and social care secretary, has recently expanded the number of GPs in the Targeted Enhanced Recruitment Scheme from 200 to 250, under which trainee family doctors are paid £20,000 “golden hellos” in return for starting their career in areas of the country lacking in doctors.

Dr Richard Vautrey, chair of the British Medical Association’s GPs committee, said that the £20,000 payments were helpful, but “only short-term fixes.

“This requires sustained significant investment, enabling all practices to recruit sufficient GPs and other staff,” he added.

GPs in poorer areas are also ending up with bigger caseloads, but their counterparts in wealthy places are not, because there are more of them.

The number of patients per family doctor in the most deprived quintile rose from 1,213 people in 2010 to 1,397 people last year. But those in the richest quintile are treating on average 2,514 patients, three fewer than in 2010.

The Department of Health and Social Care refused to comment on the flight of GPs from poorer areas. It said: “GPs are a crucial part of the NHS and we are committed to meeting our objective of recruiting an extra 5,000 GPs by 2020.

“More than 3,000 GPs have entered training this year, 1,500 new medical school places are being made available by 2019 and NHS England plans to recruit an extra 2,000 overseas doctors in the next three years.”

Doctors ‘muzzled’ and bullied into leaving public hospitals, says AMA

Doctors are being “muzzled” when they raise concerns to public hospital management about patient safety and are being bullied to the point of leaving the public system, the head of the Australian Medical Association, Dr Michael Gannon, has said.

Gannon blamed the problem in part on “the rise of managerialism and bureaucracy in delivering healthcare and the fact [governments] have not invested in public hospitals”.

He said while the federal government had invested a greater share of funding in public hospitals than state and territory governments, all governments had failed to direct that funding where it was most needed.

“Budgets announce record spending on hospitals but no money goes into addressing shortfalls where they exist,” Gannon told Guardian Australia.

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“The culture of managerialism we’ve seen grow over the past 20 years is of concern to everyone. We have seen administration grow from an office to an entire hospital block. There is enormous pressure on everyone in the system including managers but the reality is this puts a lot of pressure on clinicians on the coalface.

“So when doctors raise concerns about patient care, they get muzzled. I am talking about corporate bullying, those who are silencing concerns about the care patients might be receiving, because there is such pressure on budgets and performance targets and getting patients out [of beds].”

A spokesman for the federal health minister, Greg Hunt, said states were responsible for the operation and management of hospitals and for employing doctors.

“Although Michael Gannon was calling for more hospital funding in some of his comments … he made the point that it was the states and territories who needed to increase their funding,” the spokesman said.

The Queensland health minister Steven Miles dismissed Gannon’s claims that doctors were leaving public hospitals to work in the private system.

“Given public hospital activity is increasing at a greater rate than private hospital activity, it seems highly unlikely that there is an exodus of public hospital doctors to the private sector,” he said.

But Gannon stood by his comments.

“This is an issue members talk about nationally and it does exist in Queensland health as it exists in other jurisdictions,” he said. “These issues haven’t happened on one minister’s watch, it is a culture that has developed over the past 20 years and partly reflects rise of managerialism and bureaucracy in delivering healthcare.”

It comes as the president of the Royal Australasian College of Surgeons, John Batten, wrote in the College’s Building Respect, Improving Patient Safety progress report that addressing bullying, discrimination and sexual harassment in the health system was a priority.

“We know that cultural change takes time and that we are at the start of a multi-year, long-term investment in improving our workplaces and training environments,” he said.

“We must maintain our support for all our fellows, trainees, international medical graduates and partners in reporting unacceptable behaviour, standing up to unfair treatment and advocating for change.”