UCLH, one of the largest hospitals in the nation, has revealed comprehensive plans to utilize artificial intelligence in undertaking duties that are traditionally performed by nurses and doctors.
These tasks include deciding which A&E patients are attended to first as well as diagnosing cancer on CT scans.
The latest move by the University College London Hospitals (UCLH) involves a three-year partnership with the Alan Turing Institute.
The collaboration aims at bringing the benefits of machine learning advancement to the NHS on an unimaginable scale.
According to Prof Bryan Williams, the director of research at University College London Hospitals NHS Foundation Trust, the hospital’s revolutionary move could have a significant impact on patient results.
Bryan Williams also emphasized that the undertaking would be groundbreaking. According to him, you can decide what movies you are going to watch, order pizza or even book an airline ticket on your phone thanks to artificial intelligence.
Nonetheless, Bryan said the NHS still has a long way to go to become sophisticated enough since it still sends out letters.
At the core of the collaboration, which has UCLH investing a considerable but an undisclosed amount of money, is the conviction that machine learning algorithms can deliver new and innovative ways of directing resources, identifying individuals at risk of illness and diagnosing disease.
Bryan William added that in theory, both nurses and doctors could e deployed responsively in future the same way Uber drivers go to areas with demand during particular times of the day.
However, the move may elicit concerns of cybersecurity, privacy and the shifting functions of health experts.
The project is intended to boosting the hospital’s A&E(accident and emergency) department, which like is failing to meet the waiting time targets of the government.
Prof Marcel Levi, the chief executive officer of UCLH, said although the hospital ’s performance this the four-hour wait target, it is not a reflection of the staff ’s commitment and dedication.
Instead, it indicates several other things that are wrong in the chain regarding the flow of patients in critical condition in and out of the hospital.
In March alone, only 76.4% of patients in need of urgent care were treated in a span of four hours at the hospital’s A&E units based in England. This figure marks the lowest proportion ever since the records started back in 2010.
By utilizing data drawn from numerous presentations, a machine learning algorithm can do various things including assessing the possibility of a patient with abdominal pain to be suffering from a more severe problem.
According to Levi, although machines will never substitute doctors, the use of technology, expertise, transform how the hospital manages its services.
Another ongoing project aims at identifying patients who most likely to miss appointments. Parashkev Nachev, a consultant neurologist, based at the hospital, data such as weather conditions, address, and age to forecast 85% accuracy whether a patient will come to the hospital for MRI scans and outpatient clinics.
In the next stage, the department intends allocating appointments and sending reminder texts to optimize chances of attendance.