Flow Health partners with US Department of Veterans Affairs


Flow Health partners with US Department of Veterans Affairs


Flow Health has formed a five-year partnership with the US Department of Veterans Affairs (VA) to build a medical knowledge graph with deep learning to inform medical decision-making and train artificial intelligence (AI) to personalize care plans.

The partnership’s key objective is to understand the common elements that make certain people susceptible to particular diseases, pinpointing effective treatments and identifying possible side effects in order to inform care decisions.

The collaboration will integrate large volumes of data and aims to discover relationships between genomes and phenotypes, to learn what every gene variant actually means, to identify disease risk, to make more precise diagnoses, and to suggest individualized treatments.

“Our mission is to advance healthcare by applying the latest artificial intelligence techniques to improve the detection, diagnosis, treatment and management of diseases,” said Alex Meshkin, CEO of Flow Health.

“The VA supports millions of Americans who have served our nation and deserve our honor, respect and the best care our country has to offer. Through our partnership with the VA, Flow Health is working to unleash the power of AI to benefit our nation’s veterans.”

Flow Health is building a knowledge graph of medicine and genomics from over 30 petabytes of longitudinal clinical data drawn from VA records on 22 million veterans spanning over 20 years. All patient information will be de-identified during analysis to protect privacy.

Robert Rowley, chief medical officer at Flow Health, said: “Developing artificial intelligence which can automatically identify the best diagnostic and treatment pathways will assist clinicians in delivering precision medicine to every veteran.

“To build artificial intelligence you need huge amounts of data to feed deep learning models. This is why this partnership between the VA and Flow Health is a watershed moment for deep learning in healthcare.”

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