Bayesian Health offers an adaptive AI/ML platform that forecasts declining trajectories within a hospital/health system’s patient population. Our research-backed platform is designed to empower providers with the ability to identify and intervene with next-best actions in a timely way. This is accomplished by sending accurate and actionable clinical signals for a wide range of critical condition areas (Sepsis, Deterioration, HAPI) within the EMR and existing workflows. As a result, physicians and care team members are able to catch life-threatening complications much earlier, leading to better outcomes for patients at risk of sepsis, all-cause deterioration and pressure injuries, as well as reducing healthcare costs.
Use Cases
- Bayesian's clinical AI solution is a platform that helps healthcare providers detect early signs of illness or disease in patients. The app is designed to be used by clinicians and healthcare professionals who have access to a hospital or healthcare system's electronic medical records (EMR) data. The app uses this data to analyze patient information, including labs, vitals, notes, and third-party data to identify patients who are at risk.
- When the app identifies a patient who may be at risk, it displays actionable insights and flags that prompt the clinician to complete a short questionnaire or order medications. The app also provides context for why the patient was flagged, enabling clinicians to make the best possible clinical decisions.
- The app is typically used in a hospital or healthcare system setting, where clinicians have access to EMR data. It fits seamlessly into the existing workflow and processes of the healthcare system, and provides alerts and notifications to the appropriate clinician when necessary.
- Overall, Bayesian's clinical AI solution is a powerful tool that helps healthcare providers identify patients at risk and deliver the best possible care. By using EMR data to continuously improve its performance, the app can help clinicians make more informed decisions and improve patient outcomes.
Available in These Countries
- The application is available in the United States
Supported Devices
- Desktop
Version Details
Compatible Cerner platform: PowerChart, CareCompass, Clinical Leader Organizer
App version: 1.0.24
We do bi-weekly releases
Key Features
Bayesian Health Process Infographic
Bayesian Health sits within the EMR, consuming patient data from the patient ecosystem. When signs of risk are identified, the solution surfaces actionable, contextualized clinical inferences within prescriptive EMR workflows, allowing providers to react in a timely manner.
Bayesian Health - Alerts Within Patient List
Screenshot of AI clinical inference alert as it appears in Patient List
Bayesian Health - Actionable, Contextualized Workflow
Screenshot of AI clinical inference directly within Cerner prescriptive workflows, including Sepsis bundles.
RESEARCH FIRST APPROACH
In recognition that AI solutions need to be held to the same rigorous standard of evidence as other diagnostic and therapeutic tools in medicine, Bayesian Health has taken a research-first approach as the integral component of our GTM strategy. Supported by 26+ peer reviewed publications, Bayesian is the first and only machine learning platform demonstrating reductions in mortality and better outcomes.
Bayesian Health believes that AI algorithms should be given the same rigorous scrutiny as drugs and medical devices undergoing clinical trials. This is why we have focused on a research-first approach to developing our product, models and services. Bayesian Health is the only intelligent care augmentation platform that is backed by rigorous peer reviewed results in real world settings.
NATURE MEDICINE STUDIES
In July '22, Bayesian published the results of the largest and most rigorous evaluation to date of AI in a real world setting. Bayesian Health and Johns Hopkins announced the ground-breaking results in Nature Medicine that, for the first time, associated lives saved with Bayesian’s clinically deployed artificial intelligence platform.
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.
What’s significant about these three studies?
These ground-breaking results bring context, transparency and rigor to the field. While retrospective studies done in the past have demonstrated that machine learning-based models can detect sepsis early, few studies have reported on clinical implementations of these models. Until now, 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.