By Fiona Villamor on March, 25 2020

Streamlining patient workflows through predictive analytics can enable efficient, proactive treatment for patients at risk of sepsis.

The real heroes in this pandemic are the frontliners—the medical professionals and staff risking their lives and giving their all to provide care for those who need it. They play the most crucial role of addressing pressing healthcare needs, but they’re also key to early intervention for any disease, not just COVID-19.

Nurses arguably spend the most time with patients, and as such they play a huge role in the recognition and treatment of disease. Their routine patient assessments and patient care rounds are critical to improving health outcomes, and their unique position is often utilized to enable timely intervention and treatment.

A common approach to sepsis, which accounts for 1 in 5 deaths globally, revolves around nurse-led screening intervention.  At Nottingham University Hospitals, for example, a new clinical pathway was created for nurses to more accurately assess the risk of patients suspected of having neutropenic sepsis. As a result, there were less unnecessary patient admissions and a better use of resources: 34 of 73 patients were discharged home, which led to savings of £10,200.

On a similar note, the Houston Methodist Hospital adopted an evidence-based predictive analytics program for nurses to use for sepsis screening. With improved screening and response protocols, more inpatients were screened (33% compared to 10% two years before) and inpatient sepsis-associated deaths dropped to 21.1% from 29.7% post-implementation.

Arming a nurse with insights that predict whether a patient is at risk of developing sepsis allows them to focus on the proactive treatment plan. However, with a nurse-led approach in place, is it ideal to rely on a manual screening process, especially during a surge of patients or a pandemic, given that studies show mortality from sepsis increases by as much as 8% for every hour that treatment is delayed?


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Predictive analytics at the point of care

Comorbidity is a significant variable in COVID-19 deaths, and while sepsis isn’t a common risk factor, it’s been shown to be a resulting condition. In a virtual conference on sepsis and its relation to COVID-19 at the University Hospital Charité in Berlin, experts showed data suggesting that sepsis is diagnosed in 100% of COVID-associated deaths, with respiratory failure as the predominant organ failure.

Must-read: Can predictive analytics help COVID-19 deaths from rising with early sepsis intervention?

This shows, at the very least, that early sepsis care and treatment is important now more than ever. After all, studies show that 80% of sepsis death could be prevented with rapid diagnosis and treatment. With healthcare resources being more and more exhausted from the pandemic, it’s critical to continue an approach that allows medical professionals to facilitate early intervention—and nurses are still key.

By making full use of technology and predictive analytics, nurses at the frontline in emergency departments and intensive care units can accurately identify and get real-time alerts for high-risk patients, enabling aggressive medical intervention to save lives.

Here at Ducen IT, we are passionate about building predictive analytics solutions that makes proactive treatment possible. With predictive modeling applied to patient vitals and data, early sepsis care is made possible with real-time insights of patient at risk of sepsis.


We are happy to demonstrate how we have used Analance™ Predictive Analytics and make the adoption process pain free. Contact us for more information.


Fiona Villamor

Fiona Villamor is the lead writer for Ducen IT, a trusted technology solutions provider. In the past 8 years, she has written about big data, advanced analytics, and other transformative technologies and is constantly on the lookout for great stories to tell about the space.