Clairyon

Clairyon's COMPOSER-LLM early sepsis prediction model delivers a – than competing models—without sacrificing sensitivity. In prospective deployment, COMPOSER-LLM achieved a PPV of ~60% at ~70% sensitivity—with an effective PPV exceeding 80% when early detection of infections is included. Moreover, COMPOSER-LLM doesn’t just flag sepsis risk; it provides a, helping clinicians distinguish sepsis from mimics like cirrhosis, cardiogenic shock, and GI bleeding. And it’s not just an algorithm—it’s part of a that includes Clairyon’s SEP-1 automated abstraction engine (QUALLM) and software tools that span the entire continuum of care, quality reporting, clinical documentation improvement, and reimbursement. In a space where hype often outpaces clarity, Clairyon is building for clinical trust, operational integration, and real-world impact, backed by -[1-3].[1] Development and prospective implementation of a large language model based system for early sepsis prediction: [2] Large Language Models for More Efficient Reporting of Hospital Quality Measures: https://ai.nejm.org/doi/full/10.1056/AIcs2400420 [3] Integrating artificial intelligence into healthcare systems: more than just the algorithm: