A new app that is intended to get alerts to clinicians faster than traditional methods of communication has been described as a potential lifesaver. The system, which is called Streams and is described by developer DeepMind as a ‘mobile medical assistant’, was being trialled at London’s Royal Free Hospital.
It was found to speed up the detection of serious kidney conditions. Clinicians received warning signals via the app in an average of 14 minutes when blood tests had indicated the condition. Without Streams, the process would usually take several hours.
Mary Emerson, lead nurse specialist at the Royal Free, told the BBC that it’s a massive change to be able to receive alerts about patients anywhere in the hospital. She added that healthcare is mobile and real time, and this device is the first to allow her to see results in a mobile, real-time way.
App described as ‘potentially lifesaving’
Consultant and kidney specialist Dr Sally Hamour added that the system was ‘potentially lifesaving’.
‘We need to gather a lot more information about this technology and we need to look at it over a longer time frame,’ she said.
‘But it is certainly the case that some patients are very unwell, information comes to the correct team very quickly, and then we can put measures in place to try to make that patient safe and reverse the impact on their kidney function.’
The app sends information straight to frontline doctors and nurses in the form of easy-to-read graphs and results. According to DeepMind, pagers, paper records and fax machines are generally used to communicate important clinical information.
DeepMind has made global headlines with its game-playing AI, which used machine learning to beat human players at chess and Go, as well as popular video games such as the eSports favourite StarCraft II. It has also conducted research into health applications, however, and algorithms have been developed to analyse anonymised eye scans and improve breast cancer detection.
As well as the Streams app, the company has developed an algorithm that analyses electronic health record datasets for signs of acute kidney injury (AKI). It found that correct diagnoses were made up to 48 hours earlier than was currently the case. It also correctly spotted nine out of ten patients whose condition deteriorated so severely that they then required dialysis.
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