Spain’s Savana Medica raises $15 million to bring its AI toolkit turning clinical notes into care insights to the US

Savana, a machine learning-based service that turns clinical notes into structured patient information for physicians and pharmacists, has raised $15 million to take its technology from Spain to the U.S., the company said. The investment was led by Cathay Innovation with participation from the Spanish investment firm Seaya Ventures, which led the company’s previous round, and […]

Savana, a machine learning-based service that turns clinical notes into structured patient information for physicians and pharmacists, has raised $15 million to take its technology from Spain to the U.S., the company said.

The investment was led by Cathay Innovation with participation from the Spanish investment firm Seaya Ventures, which led the company’s previous round, and new investors like MACSF, a French insurance provider for doctors. 

The company has already processed 400 million electronic medical records in English, Spanish, German, and French.

Founded in Madrid in 2014, the company is relocating to New York and is already working with the world’s largest pharmaceutical companies and over 100 healthcare facilities.

“Our mission is to predict the occurrence of disease at the patient level. This focuses our resources on discovering new ways of providing medical knowledge almost in real time — which is more urgent than ever in the context of the pandemic,” said Savana chief executive Jorge Tello. “Healthcare challenges are increasingly global, and we know that the application of AI across health data at scale is essential to accelerate health science.”

Company co-founder and chief medical officer, Dr. Ignacio Hernandez Medrano, also emphasized that while the company is collecting hundreds of millions of electronic records, it’s doing its best to keep that information private.

“One of our main value propositions is that the information remains controlled by the hospital, with privacy guaranteed by the de-identification of patient data before we process it,” he said. 

 

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