Announcing Ground-Breaking Results, Published in Nature Medicine, Associating Lives Saved with Bayesian’s Clinically Deployed Artificial Intelligence Platform

The three large, prospective multi-site cohort studies, conducted in collaboration with Johns Hopkins University, are one of the largest, most comprehensive and rigorous evaluations ever undertaken in the field of AI-driven clinical decision support for using multi-modal data to improve patient outcomes.

 

These results, showing high provider adoption and associated mortality and morbidity reductions, are a milestone for the field of AI and are the culmination of nearly a decade of significant technological investment, deep collaboration, the development of novel techniques and, for the first time, rigorous evaluation.

Remarkable Results In Real-World Settings

lead time

5.7 hrs

earlier detection on the most severe sepsis cases

sensitivity

82%

high sensitivity of sepsis case identification

adoption

89%

frontline user adoption over 2+ years

mortality

18.2%

relative reduction in sepsis mortality

Using data from 764,707 patient encounters (17,538 with sepsis) across five hospitals in both academic and community-based hospital settings with 2,000+ providers using the software, this research shows accurate early detection (1 in 3 cases were physician confirmed) at high sensitivity (82%) and significant lead time (5.7 hours earlier), high provider adoption (89%), and associated significant reductions in mortality, morbidity and length of stay.

 

Most significantly, the studies show timely use of Bayesian’s AI platform is associated with a relative reduction in mortality of 18.2%.

Recent Press Links

“Though the promise of sepsis-detection technologies has been brewing for years, these new studies were the first to quantitatively and qualitatively evaluate how the technology is adopted, used, and experienced, and its actual impact in a large population”

:

– STAT – 7/21/22 –

“The most effective tools will be those that enhance, rather than try to replace, the capabilities of bedside clinicians: those that turn a large volume of data and information into actionable knowledge and wisdom”

:

– STAT – 7/21/22 –

“There are very few studies that have looked at actual outcomes post deployment of a model like this,” said Steven Lin, executive medical director of Stanford’s Healthcare AI Applied Research team. “They looked at adoption and what actually made it work — not just that it did work, but how did it work.”

:

– Politico – 7/21/22 –

World-Class Researchers

This research was led by Suchi Saria, PhD conducted in collaboration with Dr. Albert Wu and a group of researchers from Johns Hopkins University. The team includes senior practicing physicians and nurses, clinical, human factors and machine learning researchers, and hospital administrators with expertise spanning patient safety and outcomes research, infectious diseases and health system administration.

Unique Research Approach

SIZE OF THE RESEARCH

Conducted over a five-year period at five hospitals in both the academic and community-based hospital settings and across every department (including the ED), the studies included data from 764,707 patient encounters (17,538 were septic). In the prospective deployments spanning 2.5 years, over 4,000 caregivers (2,000+ were providers) participated in the research, using Bayesian’s adaptive AI platform to help augment their care of patients and empower their decision and documentation processes.

 

While retrospective studies have demonstrated the theoretic capacity of AI/machine learning-based models to detect various conditions early, few studies have reported on clinical implementations of these models to effectively monitor, tune and learn over time to continually improve performance. Additionally, there haven’t been any studies that have associated adoption amongst thousands of providers using the tool across multiple sites and settings with actual reductions in mortality.

SCOPE OF THE RESEARCH

There has been a lot of research published focusing on CDS for sepsis detection, but most included far fewer patients and often only a single site or one or two units within the hospital. Nothing has approached the size of these studies. Likewise, the diversity of the settings where this platform was deployed (community and academic hospitals and all of their associated units) better represent real-world deployment and application.

 

This longitudinal approach, where we monitored patients beginning in the ED, through every transition and on to discharge, was key in achieving the reduction results in mortality. The scope of this research shows the relevancy of this approach no matter the setting, whether it be a standalone community hospital or a large academic medical center.

Beyond Sepsis

While this research focuses on the efficacy of Bayesian’s adaptive AI platform for early detection of sepsis, the solution is configured to target a wide-array of condition-specific use cases such as clinical deterioration, sepsis, pressure injuries and transitions of care. This modular approach allows hospitals and health systems to leverage their Bayesian deployment to tackle multiple, high-priority patient care challenges and scale their investment across the enterprise.

Want to learn more?