Assisted living communities- transforming predictive data into proactive care for COVID-19

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ABSTRACT Recent data suggests that that older Americans who contact COVID-19 are at greatest risk for hospitalization and poor outcomes. Additionally, due to advanced age and their high likelihood of having multiple chronic conditions, adults in senior living facilities are at highest risk for developing COVID-19, its most serious complications, and dying. Since the identification of first US case of novel coronavirus 2019 disease (COVID-19) in the Seattle, Washington, several outbreaks have been identified in long-term care and assisted living facilities with evidence of rapid spread. Older residents and the staff of long-term care assisted living facilities as well as public health officials are facing a multitude of challenges which render early detection of COVID-19 infections difficult in these facilities and which have posed a major barrier to the efforts to control the spread of infection. Adding to these challenges, more than half of residents with positive COVID-19 test results are asymptomatic at the time of testing, further contributing to transmission. There is an urgent unmet need for strategies for monitoring of residents in long-term care and assisted living facilities to facilitate early detection of the infection using means that require minimal person-to-person contact. While the dynamics of COVID-19 infection spread is being addressed by several contact tracing apps, assessing the risk for development of severe symptoms and hospitalization in these community residents requires active physiological monitoring and ecological momentary assessment in the context of preexisting clinical conditions and presents an immediate unmet need. With this project, we propose to deliver a user- friendly COVID-19 early detection alert platform (COVID-Alert) that integrates: 1) biosensor ensemble that noninvasively and continuously monitor and record critical vital signs (temperature, heart rate, respiratory rate, oxygen saturation, and activity level); 2) ecological momentary assessment (EMA) using the 5-question set released by CDC and adopted across US by healthcare providers and health insurance industry; 3) artificial intelligence framework that triggers an alert based on synthesis of real-time physiological biosensing data feed, EMA monitoring of symptoms, with personalized risk profiles of preexisting conditions derived from electronic health record maintained by the facility. COVID-19 clinical decision support integrated into the workflow of long-term care facilities will ensure that residents receive appropriate and timely care (resident level) and ongoing surveillance to prevent an outbreak (facility level) while avoiding unnecessary staff exposure. This study brings together a strong interdisciplinary team of experts in engineering, informatics, data science, machine learning, and CDS. The advanced data-driven predictive model will be trained and validated using both high-dimensional EHR data and clinician feedback. The process of the algorithm development and clinical implementation will be closely monitored and evaluated through formative and summative evaluation.