A new algorithm could be used in the NHS to help hospitals ‘share the load’ and ensure Covid-19 patients get access to an ICU bed when needed, a study has found.
Queen Mary University of London researchers, proposed a load balancing system that would see critical ICU patients transferred between hospitals as needed.
This would involve an algorithm that could optimally allocate new patients and reduce stress on health systems as coronavirus continues to hit the population.
The research team, also including the University of Exeter and the University of Bristol, tested the algorithm on data from the NHS and the Spanish health system.
They showed that this mathematical approach could help redistribute up to 1,000 ICU patients that otherwise likely wouldn’t receive the appropriate intensive care.
The study, led by Queen Mary University of London, proposes a load balancing method that would see critical ICU Covid-19 patients transferred across hospitals
During the pandemic, demand for ICUs varies across a country, with some hospitals receiving substantial numbers of patients whilst others are unaffected.
These differences in demand create an opportunity to balance the load of patient admissions across hospitals, the team behind the study explained.
By rerouting patients from areas of high demand to more local hospitals that may have spare capacity, they can effectively ‘spread the load’.
This is similar to an approach in computer science where different tasks are assigned to different servers to minimise the overall processing time, the team said.
In this study, the researchers adopted a similar approach to manage ICU resources in hospital networks, where the ‘load’ to be allocated is the amount of ICU patients or ventilators, and the rerouting takes place across hospitals.
During the pandemic, demand for ICUs varies across a country, with some hospitals receiving substantial numbers of patients whilst others are unaffected
Using the algorithm the researchers showed that when ICU demand is uniform across the country it is possible to enable access for up to 1,000 additional cases in the UK in a single step of the algorithm, without needing to increase capacity.
In more realistic scenarios, where we see differences in demand across hospitals or regions, the scientists found their new method could balance about 600 beds per step, potentially saving a large percentage of lives.
HOW MANY PEOPLE WHO CATCH COVID-19 DIE?
World Health Organization officials believe the infection fatality rate of Covid-19 is 0.6 per cent based on various studies, or one in 200 patients.
At a virtual news briefing from the WHO’s headquarters in Geneva on August 3, Maria Van Kerkhove, WHO’s technical lead for coronavirus response said the figure ‘may not sound like a lot, but it is quite high’.
For comparison, it is six times deadlier than the flu, which kills around 0.1 per cent of those who catch it.
A study from Harvard University found a similar rate. It said the death rate on the Diamond Princess ship was 0.5 per cent.
The cruise ship is ideal for studying because there is complete data available for everyone on board at the time there was an outbreak.
The team found the fatality rate was 1.8 per cent – 13 deaths out of 712 cases – but the rate was adjusted to 0.5 per cent to reflect the general population.
It is hoped this approach could also be used to help reduce demand when the epidemic begins to decline, allowing hospitals to return back to normal as efficiently as possible,’ said Dr Leon Danon from the University of Exeter.
‘The current Covid-19 pandemic has put many national health systems under significant pressure, particularly for ICUs and ventilators,’ Danon said.
‘So far balancing patient loads in times of high demand has occurred spontaneously, for example with hospitals sharing daily information on demand and availability of resources with colleagues in other local hospitals.
‘Whilst this quick action can help in the immediate, once multiple hospital become overwhelmed the pattern of demand becomes more complex and a more systematic approach is needed.’
The load sharing methodology developed by the team could prevent health services becoming overwhelmed with excessive demand for intensive care.
This is ‘particularly critical when the second wave we are experiencing can now be coupled with the flu season,’ the researcher added.
Dr Lucas Lacasa, part of the research team from Queen Mary, said they used real world data to validate their system works and can load balance in real time.
‘We are currently in the process of exploring how to operationalise the method within the healthcare system, and are developing a user-friendly interface for the NHS, or other health systems across the globe,’ Lacasa said.
They would then be able to embed this technology within the set of measures each country is already deploying to manage the pandemic.
‘The method is easily portable to other countries as well, and whilst this load sharing algorithm has primarily been developed for the current pandemic, there’s no reason a similar approach couldn’t be used to load balance other healthcare resources.’
The findings have been published in the journal PLOS ONE.