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About Clairyon

Clairyon's COMPOSER-LLM early sepsis prediction model delivers a 𝟑–𝟓𝐱 𝐥𝐨𝐰𝐞𝐫 𝐟𝐚𝐥𝐬𝐞 𝐚𝐥𝐚𝐫𝐦 𝐫𝐚𝐭𝐞 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 𝐟𝐮𝐥𝐥 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐥 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐢𝐬 𝐬𝐞𝐭, 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 𝐛𝐮𝐧𝐝𝐥𝐞𝐝 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐬𝐮𝐢𝐭𝐞 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 𝐩𝐞𝐞𝐫-𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞 [1-3]. [1] Development and prospective implementation of a large language model based system for early sepsis prediction: https://www.nature.com/articles/s41746-025-01689-w [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: https://www.nature.com/articles/s41746-024-01066-z

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