Demonstrating feasibility of using a robot to deliver a multicomponent intervention to reduce delirium in hospitalized elderly patients living with dementia

ABSTRACT Delirium, an acute disturbance in attention and cognition and the most common complication afflicting hospitalized elderly, affects more than 2.6 million U.S. older adults annually. Delirium associates with greater morbidity, falls, functional decline, nursing effort, daily hospital costs, hospital length of stay, and nursing home placement; It contributes to more than $164 billion in annual healthcare costs in the U.S. and accounts for double the mortality in the subsequent year. People with dementia incur greater risk for incident delirium than the general population, while delirium itself may forecast or predisposes subsequent development of dementia. Furthermore, delirium in individuals with dementia can accelerate the trajectory of the underlying cognitive decline. The adjusted mean difference in costs associated with delirium in ADRD patients with versus without delirium were $34,828. Patients with a variety of underlying conditions incur greater delirium risk. As common as delirium is, 60% of delirium cases remain undiagnosed. At least 30% to 40% of delirium cases are preventable. We lack convincing, reproducible evidence that any pharmacological intervention effectively either prevents or treats delirium, while multicomponent non-pharmacological approaches probably do so by 43%-53% compared to usual hospital care. These also reduce the risk of falls by 62% among hospitalized non-ICU patients 65 years and older. The most commonly used multicomponent non-pharmacological methods such as HELP (Hospital Elder Life Programs) and ABCDEF (in the ICU) are typically administered by nurses, families or volunteers and are labor intensive. A less labor intensive, scalable approach is needed. Robots are increasingly being adopted in healthcare to carry out various tasks that enhance patient care. The goal of this project is to develop an application that builds personalized multicomponent non-pharmacological interventions for hospitalized non-ICU elderly patients for preventing delirium, and to test the feasibility of using an autonomous navigating robot to deliver the intervention at the bedside. The primary outcome measures will be the percentage of successful delivery of the non-pharmacological interventions by the robot. Secondary outcomes will be satisfaction of nurses, patients and families with the robot application.