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Mount Sinai Deploys AI Model to Improve Delirium Detection
An artificial intelligence (AI) model developed by researchers at the Icahn School of Medicine at Mount Sinai improved outcomes in hospitalized patients by quadrupling the rate of detection and treatment of delirium, according to a new study.The model identifies patients at high risk for delirium and alerts a specially trained team to assess the patient and create a treatment plan, if needed. It has been integrated into hospital operations, helping providers identify and manage delirium, a condition that can affect up to one-third of hospitalized patients.The AI tool significantly improved monthly delirium detection rates—from 4.4 to 17.2 percent—allowing for earlier intervention. Patients identified also received lower doses of sedative medications, potentially reducing side effects and improving overall care.
Medigy Insights
In their study, which involved more than 32,000 patients admitted to The Mount Sinai Hospital in New York City, the researchers used the AI model to analyze a combination of structured data and clinicians’ notes from electronic health records. It used machine learning to identify chart data patterns associated with a high risk of delirium and applied natural language processing to identify patterns from the language of chart notes written by hospital staff. This approach captures staff observations of subtle mental status changes in patients who are delirious or at heightened risk. An individual staff member writing a note may be unaware at that time that their clinical observations are helping to improve the AI model’s accuracy.
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