Healthleap raises $38M to expand AI that flags at-risk hospital patients
Healthleap raised $38 million across an $8 million seed round co-led by Sequoia Capital and First Round Capital and a $30 million Series A led by Hummingbird Ventures; the company did not disclose its valuation. Its platform connects to hospital electronic health records, uses language models to extract clues such as poor appetite or difficulty swallowing from clinical notes, and combines them with labs and vital signs to write nightly risk scores; Healthleap says it is deployed in more than 50 hospitals and that the software flags patients for review rather than diagnosing them. The company also reports that a malnutrition program at the Hospital of the University of Pennsylvania produced $23.8 million in annualized financial impact and that every customer has seen at least 5x hard ROI, figures TechRepublic notes are company-reported and do not establish consistency across hospitals.
Healthleap says the platform is used in more than 50 hospitals, including Penn Medicine and Cedars-Sinai, and the funding is aimed at expanding coverage to more conditions; its ROI figures remain company-reported.
Healthleap, a startup building an AI platform that reads patient records to identify patients at risk of undiagnosed illnesses, has raised $38 million in seed and Series A funding, TechCrunch has exclusively learned.
The financing includes an $8 million seed round co-led by Sequoia Capital and First Round Capital, and a $30 million Series A led by Hummingbird Ventures. The company is not disclosing its valuation.
Founded in South Africa in 2022 by siblings Jemima and Josiah Meyer (pictured above), the startup initially offered a clinical nutrition tool Jemima had built for dietitians. But the company later pivoted and built a more general-purpose platform that aims to identify patients admitted to hospitals who may be suffering from conditions like malnutrition or delirium that aren’t often identified early enough, CEO and co-founder Josiah Meyer told TechCrunch.
“A patient’s chart holds two kinds of data. Labs, weights, and vital signs sit in structured fields, but the most telling signs sit in clinicians’ written notes: poor appetite, recent weight loss, muscle loss, trouble swallowing. Our developing approach is extracting affirmative or negated mentions of these clinical concepts in an easily extensible and scalable way,” Josiah said.
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