Researching patient deterioration with the US Department of Veterans Affairs
We’re excited to announce a medical research partnership with the US Department of Veterans Affairs (VA), one of the world’s leading healthcare organisations responsible for providing high-quality care to veterans and their families across the United States.
This project will see us analyse patterns from historical, depersonalised medical records to predict patient deterioration.
Patient deterioration is a significant global health problem that often has fatal consequences. Studies estimate that 11% of all in-hospital deaths are due to patient deterioration not being recognised early enough or acted on in the right way.
Alongside world-renowned clinicians and researchers at the VA, we are analysing patterns from approximately 700,000 historical, depersonalised medical records in order to determine if machine learning can accurately identify the risk factors for patient deterioration and correctly predict its onset.
We’re focusing on Acute Kidney Injury (AKI), one of the most common conditions associated with patient deterioration, and an area where DeepMind and the VA both have expertise. This is a complex challenge, because predicting AKI is far from easy. Not only is the onset of AKI sudden and often asymptomatic, but the risk factors associated with it are commonplace throughout hospitals. AKI can also strike people of any age, and frequently occurs following routine procedures and operations like a hip replacement.
Our goal is to find ways to improve the algorithms currently used to detect AKI and allow doctors and nurses to intervene sooner. Eventually, we hope to apply similar approaches to other signs of patient deterioration as well, leading to improved care for many more patients, with fewer people developing serious infections and conditions—ultimately saving lives.
As with all of our research work, we are committed to treating the data for this project with the utmost care and respect. The data being used in the research are depersonalised, meaning that any information that could be used to identify individuals has been removed before DeepMind receives it. You can read more about our own approach to information governance here.
The work we’re currently conducting is exploratory, but we’re optimistic about the long term potential for machine learning technology in this area. In a world where nearly all hospital resources go toward managing symptoms after people are already ill, we hope that predictive techniques can pave the way for more preventive healthcare and help keep people from getting sick in the first place. We’ll keep you updated as we continue this work.