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Healthleap Raises $38M for Clinical AI Screening Platform Targeting Overlooked Inpatient Conditions

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Healthleap Raises $38M for Clinical AI Screening Platform Targeting Overlooked Inpatient Conditions

Clinical artificial intelligence platform Healthleap announced that it has raised $38M in combined seed and Series A financing backed by Sequoia Capital , First Round Capital , and Hummingbird Ventures (consisting of an $8M seed round co-led by Sequoia and First Round and a $30M Series A led by Hummingbird).

Directly targets an entrenched inpatient diagnostic blind spot: up to 50% of hospitalized patients are clinically at risk for disease-related malnutrition, yet fewer than 9% ever receive a formal clinical diagnosis, exacerbating patient morbidity, mortality, and hospital length of stay (LOS).

Replaces static, manual screening workflows: while acute hospitals historically screen for malnutrition using a two-question survey administered once at admission—routinely missing sedated, confused, intubated, or critically ill patients—Healthleap ingests every patient’s complete medical record every morning, synthesizing clinical notes, laboratory trends, vital signs, medication orders, and active problem lists.

Founded in 2022 by sibling duo Josiah Meyer (CEO) and Jemima Meyer (clinician and co-founder), Healthleap operates as an EHR-integrated clinical surveillance layer:

Continuous Multi-Source Chart Ingestion: The platform reads each patient’s complete electronic health record every morning—synthesizing physician progress notes, nursing assessments, laboratory panels, vitals trajectories, active medication orders, and historical problem lists.

Prioritized Clinical Worklists: Rather than overwhelming providers with static alerts, Healthleap ranks patient risk and routes actionable scores into optimized EHR workflows, highlighting for bedside clinicians and registered dietitians who requires evaluation first.

Proactive Diagnostic Capture: Enables hospitals to screen their entire inpatient census daily with high diagnostic precision, identifying deteriorating or undernourished patients who would otherwise go unnoticed until post-discharge complications develop.


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