Data science is once again being deployed to help tackle the effects of the coronavirus with the NHS launching trials of a machine learning system that aims to predict the demand for intensive care unit (ICU) resources to treat patients suffering from the disease.
The Covid-19 Capacity Planning and Analysis System (CPAS) has been developed by data scientists from NHS Digital and a team of researchers from the University of Cambridge. It uses data from Public Health England in order to help hospitals plan and manage the deployment of life-saving resources across the NHS.
The first stage trials began this week at four hospitals across England in an effort to demonstrate the accuracy of the system and to see if it needs fine-tuning.
NHS Digital chief medical officer Professor Jonathan Benger said it was essential for the health service to be able to predict demand for critical care beds, equipment and staff.
“CPAS allows individual hospitals to plan ahead, ensuring they can give the best care to every patient. At the same time, the wider NHS can ensure that the ventilators, other equipment and drugs that each intensive care unit will need are in place at exactly the time they are required.
"In the longer term, it is hoped that CPAS can be used to predict hospital length of hospital stay, discharge planning and wider intensive care demand in the time that will come after the pandemic.”
The scheme is based on a machine learning engine called Cambridge Adjutorium, which was developed by University of Cambridge engineer Professor Mihaela van der Schaar with the assistance of her multidisciplinary team.
NHS Digital executive director for master registries and data Dr Jem Rashbass said: “We recognised that there was an opportunity to industrialise the methods and deploy this as a service through the national infrastructure, managed by NHSD, and deliver a real data-driven planning tool to hospitals.”
If the trial proves successful, the system will be launched nationwide across the NHS.