Using AI to give doctors a 48-hour head start on life-threatening illness

Feedback from the qualitative study was positive, with healthcare professionals emphasising the ways in which the app accelerated the detection of patients in need, saved them time in performing administrative tasks, and improved team communication. One respondent said the app “streamlines care, and speeds up the time in which they get a specialist renal review.” Another clinician from the nephrology team stated that “Being able to look up the blood results for anyone in the hospital wherever you are is unparalleled…it must save at least – I don’t know if you could analyse it – but it must save at least a couple of hours in a day.”

Getting the right information about the right patient at the right time is a huge problem for healthcare systems across the globe. Critically, these early findings from the Royal Free suggest that, in order to improve patient outcomes even further, clinicians need to be able to intervene before AKI can be detected by the current NHS algorithm – which is why our research on AKI is so promising. These results comprise the building blocks for our long-term vision of preventative healthcare, helping doctors to intervene in a proactive, rather than reactive, manner.

Streams doesn’t use artificial intelligence at the moment, but the team now intends to find ways to safely integrate predictive AI models into Streams in order to provide clinicians with intelligent insights into patient deterioration.

This is a major milestone for the DeepMind Health team, who will be carrying this work forward as part of Google Health, led by Dr David Feinberg. As we announced in November 2018, the Streams team, and colleagues working on translational research in healthcare, will be joining Google in order to make a positive impact on a global scale. The combined experience, infrastructure and expertise of DeepMind Health teams alongside Google’s will help us continue to develop mobile tools that can support more clinicians, address critical patient safety issues and could, we hope, save thousands of lives globally.

Source: https://deepmind.com/blog/article/predicting-patient-deterioration