Using Artificial Intelligence to Identify and Predict Delirium Among Hospitalized Medical Patients

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Delirium is an acute neurocognitive disorder that affects up to half of older hospitalized medical patients. It can cause confusion, disorientation, hallucinations, and agitation, leading to significant distress for patients and their caregivers. It can lead to dementia, longer hospital stays, increased health costs, and death.

While delirium can be prevented and treated, it is difficult to identify and predict. Current methods require either ongoing clinical assessment or the use of complicated screening tools. This is expensive and often impractical for most hospitals. Given these challenges, delirium reporting relies on administrative data, which significantly underestimates true rates of delirium. From a quality of care perspective, this inaccuracy makes it difficult to compare rates of delirium across hospitals and evaluate the impact of interventions to improve delirium care.

In this project, we will utilize artificial intelligence (AI) to develop two automated tools, which will use routine data extracted from electronic medical records to identify delirium cases and predict patient delirium risk. As there is concern that AI applications may worsen health inequity because they do not take into consideration social and economic variables, we will test whether there is bias in the detection and prediction of delirium with our tools. We have assembled a multidisciplinary team with expertise in clinical medicine, healthcare quality, engineering, AI and machine learning, and health equity.

Our approach is novel in delirium care and could change how quality indicators are measured. This research will enable the accurate measurement and reporting of delirium rates in hospital. Further, it will develop methods to predict patient risk of delirium in “real-time” to support targeted personalized interventions at the time of hospital admission to prevent and treat delirium.