VigiLanz to Demonstrate Major Accomplishments with Business Intelligence & Predictive Analytics Models

Initial Results to Identify Severely Septic Patients Early Shows Strong Promise

MINNEAPOLIS–(BUSINESS WIRE)–VigiLanz, a digital healthcare intelligence firm, today announced that its new business intelligence and predictive analytics software-as-a-service (SaaS) platform is demonstrating significantly positive results since its introduction in October 2015. The company is demonstrating its SaaS platform at the HIMSS annual information technology conference and exhibit at its booth #12306 in Las Vegas between February 29th and March 3rd.

VigiLanz’ business intelligence (BI) is an innovative clinical SaaS service designed to provide real-time predictive insights to optimize clinical interventions and outcomes, reduce medical errors, lower patient readmission risk, identify hotspot disease trends and improve population health. VigiLanz’ BI suite is engineered to integrate and complement VigiLanz’ real-time Clinical Intelligence and Surveillance services that today support nearly 400 hospitals in the U.S.

“The VigiLanz BI platform and our Temporalytics® predictive analytics model is actively being tested for the early identification of patients at high risk for sepsis, and is demonstrating strong initial results in sensitivity, specificity and positive predictive value,” said David Goldsteen M.D., VigiLanz CEO and Chairman. “Based on our performance to date, we continue to believe in our contribution to helping transform health care through a real time, integrated clinical and business intelligence platform that can impact patient care and outcomes beyond current methodologies, by delivering better, more timely decision-making support and intelligence.”

Dr. Goldsteen said trials comparing VigiLanz’ Temporalytics models with the standard or modified SIRS criteria for early identification of severely septic patients are, “in the initial phases, demonstrating positive predictive value increase of well over a two-fold factor along with meaningful improvements in both sensitivity and specificity, without adversely impacting false-negative patient diagnoses rates.”

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