Healthleap Raised $38 Million for AI Screening

The company secured funding to expand its AI platform that analyzes patient health records in hospitals.

Updated on Oct. 7, 2026 in Healthcare

Isometric editorial illustration of stacked clinical glass blocks in a clean corridor, representing data-driven patient health monitoring. — Healthleap raised $38 million to expand its AI screening platform, which automates patient risk assessment by continuously monitoring electronic health records in hospitals. AI Illustration. Upload story photo >

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Do you believe AI-driven diagnostic tools improve the quality of care in your local hospitals?

Healthleap has raised more than $38 million to scale its AI platform designed to identify hospitalized patients needing clinical attention. The technology continuously reviews electronic health records to proactively flag patients earlier than traditional methods.

Why it matters

The platform aims to improve patient outcomes by integrating structured and unstructured data to provide clinicians with real-time risk assessments. By automating the screening process, the system helps medical teams intervene sooner in high-risk cases.

Healthleap evaluated its model across 166,000 admissions involving 106,000 patients, yielding a 0.95 AUROC across full hospital stays. Cedars-Sinai recorded a 39% increase in malnutrition diagnoses after implementing the platform.

The players

Healthleap

A health technology company that develops AI software to analyze electronic health records for automated clinical screening.

Hospital of the University of Pennsylvania

A major academic medical center where the platform demonstrated a $23.8 million annualized financial impact.

Cedars-Sinai

A prominent non-profit hospital system that observed a 39% increase in malnutrition diagnoses using the screening platform.

The details

The software continuously monitors lab results, vital signs, and clinical notes to add risk information into existing workflows. This automated approach identified cases an average of four days before traditional dietitian documentation, resulting in a 1.1-day reduction in average patient length of stay.

The evaluation of the AI model spanned 3.75 years of patient admissions.

Market Landscape

This development follows the industry-wide trend of integrating clinical decision support systems into standard hospital workflows to improve operational efficiency. The ability to demonstrate significant financial and clinical outcomes positions Healthleap to compete against established electronic health record analytics providers.

Patients in hospitals using this AI technology may experience shorter hospital stays due to earlier clinical intervention. Increased diagnosis rates for conditions like malnutrition can lead to more accurate care plans and improved recovery outcomes.

The takeaway

Proactive AI monitoring of health data is proving effective at catching patient risks that manual screening often misses. Hospitals are increasingly adopting these tools to streamline clinical workflows and reduce the time patients spend in care.