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SAN DIEGO, Jan. 23, 2024 /PRNewswire/ -- A recent peer reviewed study published in NPJ Digital Medicine has concluded that Healcisio’s new AI model for sepsis prediction was effective at reducing patient mortality by nearly 20%. Researchers from University of California San Diego Health analyzed over 6,000 patient encounters from two emergency departments and showed that replacing legacy identification and notification workflows with Healcisio AI significantly improved timeliness of interventions and patient outcomes. Additionally, the AI-driven notifications contributed to improvements in the Centers for Medicare & Medicaid Services (CMS) SEP-1 quality measure.
Healcisio is a healthcare technology startup based in La Jolla, CA, focused on bringing state-of-the-art AI-based diagnostic tools to the bedside. The Healcisio AI evaluated in this study works by dynamically monitoring a patient’s laboratory results, vital signs, medications, and comorbidities to detect sepsis before obvious clinical manifestations. It works behind the scenes, continuously surveilling every patient for signs of trouble. When a patient’s condition warrants concern, a message is seamlessly routed to the care team through existing electronic health record interfaces explaining the cause for concern. This explanatory approach coupled with the AI’s low false alarm rate is a welcome addition to the acute care setting which has to date been plagued by high false alarm rates and alert fatigue.
“This field is experiencing an unprecedented rate of advancement. Having support through federal investments and our extensive network of academic partners is allowing us to set the pace and shift AI’s role from a passive observer to an active partner in the diagnostic process,” said Aaron Boussina, Healcisio CEO.
This study follows a recent Healcisio fast-track Small Business Technology Transfer (STTR) program of the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH). Healcisio received $257,723 in its first year of funding to deploy its AI and expand its pioneering work in predictive health analytics utilizing smart order-sets and active sensing. In the coming year the company expects to rapidly expand the surveillance footprint within academic medical centers and community hospitals while simultaneously advancing its regulatory strategy and incorporating additional geographically diverse sites into its network.
This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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