{"id":46452,"date":"2022-07-21T20:01:54","date_gmt":"2022-07-21T18:01:54","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/bayesian-health-and-johns-hopkins-university-announce-ground-breaking-results-in-nature-medicine-that-for-the-first-time-associate-lives-saved-with-a-clinically-deployed-artificial-intelligence-plat\/"},"modified":"2022-07-21T20:01:54","modified_gmt":"2022-07-21T18:01:54","slug":"bayesian-health-and-johns-hopkins-university-announce-ground-breaking-results-in-nature-medicine-that-for-the-first-time-associate-lives-saved-with-a-clinically-deployed-artificial-intelligence-plat","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/bayesian-health-and-johns-hopkins-university-announce-ground-breaking-results-in-nature-medicine-that-for-the-first-time-associate-lives-saved-with-a-clinically-deployed-artificial-intelligence-plat\/","title":{"rendered":"Bayesian Health and 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"},"content":{"rendered":"
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\nWhere 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\n<\/p>\n

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NEW YORK–(BUSINESS WIRE)–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.\n<\/p>\n

\nBayesian\u2019s 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 \u201cone-size-fits-all\u201d approach to patients and hospitals, Bayesian\u2019s 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<\/i> (link<\/a>, link<\/a>) and npj Digital Medicine<\/i> (link<\/a>), were conducted in collaboration with researchers from Johns Hopkins University.\n<\/p>\n

\nUsing 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.\n<\/p>\n

\nMost significantly, the studies show timely use of Bayesian\u2019s AI platform is associated with a relative reduction in mortality of 18.2%.\n<\/p>\n

\n\u201cThere aren’t many things left in medicine that have a 30% mortality rate like sepsis,\u201d said Neri Cohen, MD, PhD, President of The Center for Healthcare Innovation and Bayesian collaborator. \u201cWhat 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.\u201d\n<\/p>\n

\n\u201cWhile 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,\u201d said Suchi Saria, PhD, CEO of Bayesian Health and Director of Machine Learning, AI and Healthcare Lab at Johns Hopkins University. \u201cThese 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.\u201d\n<\/p>\n

\nSepsis is one of several conditions that Bayesian\u2019s AI technology can help identify earlier in a hospital stay, preventing mortality and morbidity. When Bayesian\u2019s adaptive AI suspects a patient is at risk of developing sepsis, it immediately alerts doctors and nurses through the patient\u2019s electronic medical records (EMR) system, and then cues the provider to take specific actions, such as requesting blood cultures or prescribing antibiotics.\n<\/p>\n

\n\u201cBayesian\u2019s 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,\u201d said Cohen. \u201cAs a result, it provides technology accuracy that is 10x higher than other solutions in the marketplace.\u201d\n<\/p>\n

\nBayesian\u2019s adaptive AI is designed to integrate with a hospital\u2019s 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.\n<\/p>\n

\n\u201cBayesian Health\u2019s evidence-based AI\/machine learning platform can leverage health systems\u2019 substantial investment in the EMR as a base layer for patient data and help increase capacity of frontline healthcare providers,\u201d said Lee Sacks, MD, former Chief Medical Officer at Advocate Aurora Health and Clinical Advisor for Bayesian Health. \u201cThis is especially important in our current context, where we\u2019re struggling with staffing shortages, reducing inequalities, high patient acuity, cognitive overload and other intrinsic challenges being faced by health systems today.\u201d\n<\/p>\n

\nWhile the three studies focus on sepsis, Bayesian\u2019s 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.\n<\/p>\n

\nFor additional information on these studies, technology and more, visit our dedicated page<\/a>.\n<\/p>\n

\nBackground<\/b>\n<\/p>\n