Funding will be used to scale US operations and commercialize CLEW’s critical care platform
CLEW today announced that it has successfully concluded its Series B funding, bringing the total capital raised to over $20 million. CLEW develops an analytics platform for the healthcare industry aimed at the delivery of improved clinical and operational outcomes by predicting life-threatening complications and detecting deviations from the optimal clinical path.
The round was led by Pitango Venture Capital, Israel’s largest venture capital fund and included all existing investors with additional strategic participation from Relyens – a European mutual group insurance and risk manager specializing in the healthcare industry. Pierre-Yves Antier, Head of Strategy, Innovation and Transformation of Relyens and part of the company’s executive management will also join CLEW’s Board of Directors.
Commenting on the fundraising, founder and CEO Gal Salomon said, “We view this round of funding as further validation of our technology and product offering. We are also delighted to leverage our partnership with Relyens, which is certain to provide strategic value to the company”.
“CLEW has clearly demonstrated the value that AI can bring to critical care” said Ittai Harel, Managing General Partner at Pitango Venture Capital. “We are pleased to have led this round of funding and look forward working with CLEW as they expand their footprint in the US.”
“Artificial Intelligence and machine learning hold great promise for the healthcare insurance industry”, said Pierre-Yves Antier of Relyens. “We are excited to be part of CLEW and look forward to working closely with the company as they deploy their solutions in critical care.”
CLEW (is a real-time AI analytics platform designed to help providers make better informed clinical decisions by predicting life-threatening complications across various medical care settings. With CLEW, healthcare organizations can improve outcomes and safety, streamline patient care, and efficiently handle regulations and penalties, ultimately lowering the cost of care. The platform uses machine learning and data science technology to develop patient-specific physiological, predictive models to deliver predictive warnings during all phases of a patient’s stay. Originally developed and proven in the ICU, these models optimize scarce clinical resources and guide health care providers in predicting patient deterioration, across all care settings. For more information visit clewmed.com
Media Contacts: Cheryl Isen, +1 (425) 233-9032, Cheryl@Isenandco.com