Using artificial intelligence to identify and predict delirium among hospitalized medical patients

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Delirium is an acute neurocognitive disorder which 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 isdifficult to identify and predict. Current methods require either ongoing clinical assessment or 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 underestimate true rates of delirium. From a quality of care perspective, this inaccuracymakes 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 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 AIapplications 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. Our approach is novel in delirium care and healthcare quality improvement more generally, and could change how quality indicators are measured. Moreover, it has the potential to improve patient outcomes and reduce healthcare costs. With has assembled a multidisciplinary research team from computer, health, and social sciences, and are partnering with Health Quality Ontario (HQO), a provincial advisor on quality in health care. HQO are an active participant in the planning the methods and outcomes for the project to ensure that they can be used to improve health care at both a systems and individual patient level.