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

妄想症是一种急性神经认知障碍，影响多达一半的老年住院内科患者。它会导致困惑、迷失方向、幻觉和不安，导致患者和他们的照顾者严重痛苦。它可能导致痴呆症、更长的住院时间、增加医疗费用和死亡。虽然精神错乱是可以预防和治疗的，但它 很难识别和预测。目前的方法要么需要持续的临床评估，要么需要使用复杂的筛查工具。这是昂贵的，而且对大多数医院来说往往不切实际。考虑到这些挑战，精神错乱的报告依赖于行政数据，这些数据严重低估了真实的精神错乱比率。从护理质量的角度来看，这种不准确 这使得比较不同医院的精神错乱发生率和评估改善精神错乱护理的干预措施的影响变得困难。在这个项目中，我们将利用人工智能开发两个自动化工具，这两个工具将使用从电子病历中提取的常规数据来识别精神错乱病例和预测患者精神错乱的风险。因为有人担心人工智能 应用程序可能会加剧健康不平等，因为它们没有考虑到社会和经济变量，我们将使用我们的工具测试在检测和预测精神错乱方面是否存在偏见。我们的方法在精神错乱护理和更广泛的医疗质量改进方面是新颖的，并可能改变衡量质量指标的方式。此外，它还有可能改善患者的预后，降低医疗成本。WITH组建了一支来自计算机、健康和社会科学的多学科研究团队，并正在与安大略省健康质量顾问(HQO)合作，HQO是一家省级医疗质量顾问。HQO积极参与规划项目的方法和结果，以确保它们可用于改善系统和患者个人的卫生保健。

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 is difficult 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 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 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. 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.