Bayesian Health & Johns Hopkins University Announce Ground-Breaking Results In Nature Medicine That, For The First Time, Associate Lives Saved With A Clinically Deployed Artificial Intelligence Platform

Where early AI deployments have failed to produce real-world results, Bayesian demonstrates reduced mortality, long-term efficacy, high adoption and fewer false alerts in a trio of prospective, peer-reviewed studies.

NEW YORK, JULY 21, 2022

Bayesian Health, the leading artificial intelligence (AI)-based Intelligent Care Augmentation platform developer, today announced release of three large, prospective multisite cohort studies, a first of their kind, offering a comprehensive and rigorous evaluation of the efficacy of their adaptive AI approach and showing patient lives saved.

Bayesian’s adaptive AI technology is based on nearly a decade of academic research and it succeeds where prior applications of AI in clinical care have failed. Unlike traditional AI that follows a “one-size-fits-all” approach to patients and hospitals, Bayesian’s adaptive approach to AI takes into consideration the diversity of the patient population, the unique ways in which doctors and nurses deliver care on the front lines, and the unique characteristics of each health system, allowing it to be significantly more accurate and to gain provider trust and adoption. The three studies, which appear in Nature Medicine (link, link) and npj Digital Medicine (link) were conducted in collaboration with researchers from Johns Hopkins University.

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%.

“There aren’t many things left in medicine that have a 30% mortality rate like sepsis,” said Neri Cohen, MD, PhD, President of The Center for Healthcare Innovation and Bayesian collaborator. “What makes it so vexing, is that it is relatively common and we still have made very little progress in recognizing it early enough to materially reduce the morbidity and mortality. To reduce mortality by nearly 20% is remarkable and translates to many lives saved.”

“While we all understand the value of leveraging AI to improve the delivery of care, achieving measurable impact has proven to be much harder than advertised,” said Suchi Saria, PhD, CEO of Bayesian Health and Director of Machine Learning, AI and Healthcare Lab at Johns Hopkins University. “These results showing high physician 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 rigorous evaluation.”

Sepsis is one of several conditions that Bayesian’s AI technology can help identify earlier in a hospital stay, preventing mortality and morbidity. When Bayesian’s adaptive AI suspects a patient is at risk of developing sepsis, it immediately alerts doctors and nurses through the patient’s electronic medical records (EMR) system, and then cues the provider to take specific actions, such as requesting blood cultures or prescribing antibiotics.

“Bayesian’s AI-based technology overcomes common hurdles faced by many physicians by using cutting edge strategies to increase precision, strengthen models and encourage behavior change and ongoing use,” said Cohen. “As a result, it provides technology accuracy that is 10x higher than other solutions in the marketplace.”

Bayesian’s adaptive AI is designed to integrate with a hospital’s EMR where it provides early detection flags and key insights that are actionable, shown on the patient list and/or linked with paging, phone, or other escalation pathways to alert the appropriate clinician. The flags that are generated drive prescriptive workflows for the healthcare provider and are paired with explanations and clinical history.

“Bayesian Health’s evidence-based AI/machine learning platform can leverage health systems’ substantial investment in the EMR as a base layer for patient data and help increase capacity of frontline healthcare providers,” said Lee Sacks, MD, former Chief Medical Officer at Advocate Aurora Health and Clinical Advisor for Bayesian Health. “This is especially important in our current context, where we’re struggling with staffing shortages, reducing inequalities, high patient acuity, cognitive overload and other intrinsic challenges being faced by health systems today.”

While the three studies focus on sepsis, Bayesian’s platform encompasses a wide-array of other condition-specific use cases such as clinical deterioration, pressure injuries, palliative care, transitions of care, recovery at home and proactive virtual care.

For additional information on these studies, technology and more, visit our dedicated page.
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Background

  • Sepsis, a systemic, toxic reaction to infection, is a leading cause of in-hospital death globally, comprising nearly 27% of deaths in the acute care setting (ref). Early recognition and treatment with broad-spectrum IV antibiotics are critical to decreasing mortality and morbidity.
  • Machine Learning/Artificial Intelligence is a broad class of algorithmic tools that enable continuous learning from disparate real-world datasets. Bayesian uses AI approaches that are especially suited to learn from messy, multimodal, unstructured and structured data streams we see in healthcare where we encounter challenges like significant missingness, bias, data shifts. Using these inputs, learning models can be dynamically tuned around particular use cases (such as sepsis) to differentiate and uncover patterns of risk across the patient population in real-time.
  • Bayesian’s technology was developed over nearly a decade of scientific research. The core of the research was a system referenced in the studies as Targeted Real-time Early Warning System (TREWS) and resulted in more than 15 publications in top medical and AI journals and conferences. Bayesian Health was created to commercialize TREWS through its adaptive AI platform and engaged Johns Hopkins Health System to run a real-world clinical study to prove its effectiveness.

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Bayesian Health is on a mission to make healthcare proactive by empowering physicians with real-time data to save lives. Just like the best physicians continually incorporate new data to refine their prognostication of what’s going on with a patient, Bayesian Health’s research-backed AI platform integrates every piece of available data to equip physicians with accurate and actionable clinical signals that empower them to accurately diagnose, intervene, and deliver proactive, higher quality care. With a research-first foundation of over 24 patents and peer-reviewed research papers, Bayesian’s platform is based on technology licensed from the Johns Hopkins University.