Delirium AI model raises detection at second hospital; CMS proposes cutting hospital training rules; Medicare adds hospitalist quality pathway

A hospital bed in side view with the head section raised, a pillow, wheels and a side rail carrying a small call button.

A machine learning delirium risk model deployed without retraining at Mount Sinai Morningside raised the monthly delirium detection rate to 19.59% from 9.23%, with a high false positive rate, the authors said. The Centers for Medicare & Medicaid Services (CMS) proposed removing several hospital training requirements and, in the 2027 fee schedule, added a quality pathway for hospitalists. A Veterans Affairs study found 27.5% of older patients had an unplanned readmission within 90 days.

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