Critically ill patients at Southampton General Hospital are helping
to test a computer program which could make nurses’ lives less stressful.
The Equipment Monitor program, which won £50 000 of government funding
last week, monitors the equipment connected to the patient by means of a
neural network. Equipment Monitor is a neural network program which, like
the human brain, takes ‘decisions’ by weighing up information it receives,
in this case from the equipment.
Patients in the intensive care unit at the hospital are connected to
conventional equipment which monitors their heart rate, blood pressure,
respiration rate and other functions. While the nurses watch the equipment
as usual, Equipment Monitor is watching too. If the program proves a success,
it could eventually take over this task completely, freeing the nurses to
look after patients in other ways.
The monitor can ‘learn’ by updating the emphasis it places on the various
types of information. The neural network in the Southampton trial is learning,
under supervision, how to spot when a patient needs attention. But, unusually
for such a program, it does this during non-emergency periods and infers
from that what constitutes an emergency. Most neural networks have to ‘experience’
an emergency before they can recognise one.
Lorraine Dodd of Neural Solutions in Cheltenham, who is developing the
Equipment Monitor with her colleague Nigel Dodd, says: ‘We don’t want to
put a patient through trauma just to train the machine.’ She suggests that
this type of training, called default training, might also be useful in
nuclear reactors and chemical plants where accidents have serious consequences.
Mick Nielsen, the consultant in charge of the intensive care unit, says
that the program could make the jobs of nursing staff easier: ‘Theirs is
a very stressful job, to look after the patients and be alert to changes
in their condition. To know there is a box monitoring them too will help
to remove one element of the stress.’
Dodd goes further: ‘No machine will ever be able to do the horrid bits
of medicine,’ she says, ‘but this will allow nurses to do the real nursing
care rather than sitting watching screens for hours.’ The program could
also monitor patients under anaesthetic in the operating theatre.
Nielsen and the team at Neural Solutions will analyse data collected
during the trial, which began two weeks ago, to improve the neural network.
So far all the patients tested have been stable, but Nielsen says that when
the program did set off the alarm, it was at appropriate times.
The program was specially developed for medical monitoring and runs
on an ordinary personal computer. It won a small business award worth £50
000 from the Department of Trade and Industry last year. Last week’s award
is for a second year’s development. Dodd hopes it will then be ready to
go on sale. Neural Solutions also hopes to apply the program to environmental
Equipment Monitor has some similarities with expert systems, which store
large quantities of information gathered by human experts, and select and
supply them to anyone who needs help with making decisions. In an expert
system, rules have to be stated explicitly: for example, ‘If the heart rate
exceeds a certain value, sound the alarm.’ But in Equipment Monitor the
relationship between the items of information is different. Neural networks
build up their own rules during the learning process.