Peer-Reviewed Publication
Anesth Analg2026October 9, 2026Journal Article
Artificial Intelligence-Driven Clinical Decision Support for Postoperative Delirium Prevention: A Pragmatic Randomized Controlled Trial.
Danielle Scharp1, Kerry Meyers2, Girish Nadkarni2, Alex Federman1, Catherine Sarkisian3, Matthew A Levin2,4, Spencer Perry5, Pooja Gownivaripally5, Natalia N Egorova6, Yuxia Ouyang6, Susana Vacas7, Ira Hofer4
1From the Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, NY, New York.
2Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York.
3Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University California Los Angeles and Veterans Administration, Greater Los Angeles, Geriatrics Research Education and Clinical Center, Los Angeles, California.
4Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, NY, New York.
5Icahn School of Medicine at Mount Sinai, New York, New York.
6Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, NY, New York; and.
7Department of Anesthesiology, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Abstract
BACKGROUND: Postoperative delirium affects up to 50% of older adults with cognitive impairment, yet preoperative identification of at-risk patients remains limited. Artificial intelligence methods, such as natural language processing, can extract cognitive impairment signals from electronic health records and enable targeted clinical decision support interventions to promote adherence to periopera…
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