The adoption of artificial intelligence (AI) and similar technology by a major London trust will help nurses, rather than put roles at risk, managers have insisted.
One of the largest NHS trusts in England has revealed that it is to begin using AI and machine learning in a bid to reduce staff workload and improve efficiency.
“This is about optimising the way the organisation functions by providing staff with better tools”
University College London Hospitals NHS Foundation Trust is hiring experts from the Alan Turing Institute to help design algorithms, in what is thought to be the first largescale use of AI in the NHS.
Better analysis of hospital data resulting from the move will lead to faster patient flows and more time for staff to focus on the core elements of their jobs, the trust claims.
One national newspaper responded to the announcement with a headline about doctors and nurses being “replaced by AI”.
But Nick McNally, managing director of research at the trust, told Nursing Times that the move was not about replacing staff.
“We’re not sacking anyone. This is about optimising the way the organisation functions by providing staff with better tools to use in day to day work,” he said.
For example, Mr McNally said his team was “excited” about an algorithm that will identify patients who are least likely to attend a radiography appointment.
“The algorithm feeds off routine trust data, learning from that data and constantly improving,” he told Nursing Times.
“We need to try something different, something innovative, something longer-term”
As a result, the hospital trust will know not to offer certain patients a Friday afternoon appointment and instead offer them the time slot they are most likely to attend, he said.
Professor Bryan Williams, director of the National Institute for Health Research, which was involved in brokering the deal between the trust and the institute, said the partnership would allow the NHS to do “so much more” with the information it collects.
“Imagine a world where we could use this data to develop algorithms to rule out diseases, suggest treatment plans or predict behavior,” he said. “The partnership has the potential to tackle some of the big issues that the NHS has never been able to solve.”
The first area of focus will be on accident and emergency where, like many hospital trusts, University College London Hospitals has struggled to hit waiting time targets.
“Our performance this year has fallen short of the four hour wait,” said trust chief executive Marcel Levi. “This is no reflection on the dedication and commitment of our staff but rather an indicator of some of the other things in the entire chain concerning the flow of acute patients in and out of the hospital that are wrong.”
A more high-tech approach was called for, Mr Levi argued. “With ever increasing numbers of patients and ongoing financial pressures, we need to try something different, something innovative, something longer-term.
“The partnership with the Alan Turing Institute provides an opportunity to work with the world’s leading data scientists to do just this,” he added.
Better information analysis on blockages to the patient journey from A&E would lead to improved patient flow through the system, he suggested.
“The partnership has the potential to tackle some of the big issues that the NHS has never been able to solve”
The Alan Turing Institute has previously applied flow models in the airline sector, looking at how passengers and baggage move through airports. Now, it will turn its attention to hospitals.
Its chief executive, Sir Alan Wilson, said: “At the Turing we believe that data science and AI will revolutionise healthcare.
“Not only through new technologies such as image recognition but also through applying cutting edge algorithms to the everyday problems facing the NHS, such as A&E waiting times,” he said.
The cost of the initiative has not been revealed. Most of the computing power is already in existence at the NIHR, Mr McNally said.
The cost to UCLH will be in personnel – specifically recruiting Alan Turing Fellows who will come in and advise managers on how to set up the necessary AI systems.
In time, it is hoped that funding will come from elsewhere – charities, research councils and government, said Mr McNally.
“Our aim is that whatever we develop at UCLH we want to disseminate that learning to help the NHS across the board,” he told Nursing Times.