Predicting infection and detecting illness using Oura rings

In partnership with UCSF and Oura, we introduced the TemPredict Study at Stony Brook University Hospital—an initiative to distribute biometric sensors to our frontline healthcare workers and develop predictive algorithms that help monitor COVID-19 infection. Within weeks, we distributed 250 Oura Ring wearable sensors within our emergency department and intensive care unit, allowing employees to easily track changes in their body temperature, respiratory rate, and heart rate; providing our frontline workers access to their data may help coordinate local group responses to infection by providing early indicators of illness. We are collecting electronic medical records to help us identify COVID-19 infection, co-morbidities, and other risk factors related to COVID-19 within our study cohort. Additionally, we are distributing surveys to assess the effects of the COVID-19 pandemic on mental health and resilience of our frontline workers. By employing machine learning techniques in modeling of this large continuous dataset, we hope to develop reliable tools for predicting onset and severity of infection. If our methods prove successful, this approach may be adapted to predict future disease outbreaks and improve government and institutional responses to those outbreaks.