The Department of Veterans Affairs has partnered with artificial intelligence vendor DeepMind to develop machine learning algorithms that accurately identify risk factors that could lead to the deterioration of hospitalized patients, enabling clinicians to intervene before their conditions worsen.
The partnership will initially focus on analyzing patterns from 700,000 historical, de-identified health records to uncover the early warning signs of risk for acute kidney injury (AKI), one of the most common conditions associated with patient deterioration—and an area of expertise for U.K.-based DeepMind.
“This is a complex challenge, because predicting AKI is far from easy,” according to Dominic King, clinical lead for DeepMind. “Not only is the onset of AKI sudden and often asymptomatic, but the risk factors associated with it are common 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.”
Ultimately, the machine learning technology will be applied to other signs of patient deterioration in an effort to prevent serious infections and conditions, according to King.
“Medicine is more than treating patients’ problems,” said VA Secretary David Shulkin, MD. “Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation’s veterans by predicting deterioration and applying interventions early.”
Last month, the U.S. Food and Drug Administration approved a clinical monitoring platform that alerts hospital staff in near real time of a patient’s deteriorating condition about six hours in advance using a predictive algorithm.
“We are proud to partner with the Department of Veterans Affairs on this important challenge,” said Mustafa Suleyman, co-founder of DeepMind. “This project has great potential intelligently to detect and prevent deterioration before patients show serious signs of illness. Speed is vital when a patient is deteriorating. The sooner the right information reaches the right clinician, the sooner the patient can be given the right care.”
According to the VA, about 11 percent of inpatient deaths globally are a result of patient deterioration not being detected early enough.
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