BOSTON – February 2, 2022 – Biofourmis, a Boston-based global leader in digital therapeutics and virtual care that powers personalized predictive care, announced today a multi-year agreement with UCI Health to support continuous artificial intelligence (AI)-powered remote monitoring of acute and post-acute care patients in their homes through Biofourmis tools.
UCI Health comprises the clinical enterprise of the University of California, Irvine and is Orange County’s only academic health system. The medical center is a 459-bed acute care hospital providing tertiary and quaternary care, ambulatory and specialty medical, behavioral health and rehabilitation services. It is the primary teaching location for UCI School of Medicine. U.S. News & World Report has recognized UCI Medical Center as one of America’s Best Hospitals for 21 consecutive years and ranked it among the top 15 hospitals in California.
Biofourmis’ solutions will enable UCI Health to establish a virtual care platform for remote patient monitoring (RPM) and hospital-at-home initiatives. Biofourmis’ RPM platform will replace UCI Health’s legacy remote patient monitoring system and continue monitoring appropriate patients after their discharge. Post-acute RPM can help avoid hospital readmissions and, for some patients, can be an alternative to a rehabilitation center or skilled nursing facility.
“Building on our existing operational excellence, virtual care, and innovation strategies during the pandemic, we are focused on providing tools that allow our patients to recover in the comfort of their homes,” said UCI Health Executive Director of Virtual Care Susanna Rustad. “We want to simplify our patients’ journey and streamline patient progression through our hospital system to help transform care, using technology as an enabler.”
Vital signs and other biometrics will be automatically collected within UCI Health’s Epic electronic health record system and analyzed through the Biofourmis AI algorithms. The AI establishes a personalized patient baseline using data collected from wearable biosensors and electronic patient-reported outcomes. When the baseline is compared against population-level data, the AI creates a real-time, reliable view of disease trajectory. Machine-learning capabilities alert clinicians to opportunities that optimize treatment, predict decompensation, better engage patients, and ultimately identify and prevent serious medical events before they occur.