10/12/22 Smithsonian Magazine – A New, Transparent AI Tool May Help Detect Blood Poisoning

The most impressive aspect of TREWS, according to Zachary Lipton, an assistant professor of machine learning and operations research at Carnegie Mellon University, was not the model’s novelty, but the effort it must have taken to deploy it across five hospitals and 2,000 providers over a two-year period. “In this area, there is a tremendous amount of offline research,” Lipton said, but relatively few studies “actually make it to the level of being deployed widely in a major health system.” It’s so difficult to perform “in the wild” research like this, he added, because it requires collaborations across various disciplines, from product designers to systems engineers to administrators

LINKS

Read the Smithsonian Magazine article

Learn more about Bayesian’s peer-reviewed research on sepsis here

09/08/22 Signify Research – Early Warning for Sepsis from Bayesian Health – Advances of AI in Clinical Care Continue

Rigorous evaluation of AI reducing sepsis mortality. Bayesian Health’s solution has demonstrated the positive outcomes that AI-based applications can have on detecting sepsis and reducing risk of mortality. The clinical evidence to support the positive outcomes from utilizing the platform is expected to create momentum within the industry, with other vendors striving to support their own solutions with proven clinical outcomes.

LINKS:

Read the Signify Research article

Learn more about Bayesian’s peer-reviewed research on sepsis here

8/03/22 HealthDay – Early Warning System Can Identify Sepsis Patients

“Our findings indicate the potential for high-precision alert systems to identify sepsis patients early and improve patient outcomes,” the authors write. “Furthermore, our findings among high-risk patients indicate that future alert systems may reduce overall alert burden by prioritizing alerts on certain patients.”

LINKS

Read the HealthDay article

Read more about Bayesian Health’s approach to Adaptive AI

08/01/22 Scientific American – Algorithm That Detects Sepsis Cut Deaths by Nearly 20 Percent

Detecting sepsis earlier with Bayesian’s health AI. “I think that this model for machine learning may prove as vital to sepsis care as the EKG [electrocardiogram] machine has proved in diagnosing a heart attack,” says Karin Molander, who is an emergency medicine physician and chair of the nonprofit Sepsis Alliance. “It is going to allow the clinician to go from the computer…, trying to analyze 15 years’ worth of information, to go back to the bedside and reassess the patient more rapidly—which is where we need to be.”

LINKS:

Read the Scientific American article

Learn more about Bayesian’s peer-reviewed research on sepsis here

07/26/22 – ZME Science – This AI saved hundreds of lives from sepsis in just five hospitals. Across the US, thousands more could be spared Lowering sepsis risk using Bayesian’s adaptive AI.

Thanks to this system, the patients were 20% less likely to die of sepsis, potentially saving thousands of lives across the U.S. if it is implemented nationwide. About 1.7 million American adults develop sepsis, and more than a quarter million die because of it.

LINKS:

Read the ZME article

Learn more about Bayesian’s peer-reviewed research on sepsis here