AI Model Improves Early Detection of Hospital-Acquired Pressure Injuries
A collaborative study by USC, Johns Hopkins, and University Hospitals developed an AI model that significantly outperforms traditional tools in predicting hospital-acquired pressure injuries. Bayesian Health, led by Dr. Suchi Saria, contributed to the work, showcasing how its platform improves early detection, reduces nursing burden, and advances health equity through unbiased, data-driven risk assessment.
Nature Medicine published three peer-reviewed articles showing how, for the first time, AI has been shown to reduce mortality in a hospital setting using Bayesian Health’s Adaptive AI solution.
The first of three studies, centered on characterizing system accuracy, provider adoption, and impact of adoption of treatment timing. It was conducted over a two-year period at five hospitals from both academic and community-based hospital settings. The study looked at front-line usage by 2,000+ providers.
NOTE – At the core of the research was an AI system referenced as Targeted Real-time Early Warning System (TREWS). Initially developed at Johns Hopkins, Bayesian Health has commercialized and advanced the methodology, integrating it into a broader adaptive AI platform that enables integration, monitoring, and tuning to account for real-world variations in populations and workflows and scaling to multiple condition areas. Bayesian led and managed the deployment across all five emergency departments and hospitals in the study.
Nature Medicine published three peer-reviewed articles showing how, for the first time, AI has been shown to reduce mortality in a hospital setting using Bayesian Health’s Adaptive AI solution.
The third of three studies is a qualitative analysis from the provider perspective, surveying important aspects that providers are looking for and perceived barriers to adoption of machine-learning technologies in their practice.
NOTE – At the core of the research was an AI system referenced as Targeted Real-time Early Warning System (TREWS). Initially developed at Johns Hopkins, Bayesian Health has commercialized and advanced the methodology, integrating it into a broader adaptive AI platform that enables integration, monitoring, and tuning to account for real-world variations in populations and workflows and scaling to multiple condition areas. Bayesian led and managed the deployment across all five emergency departments and hospitals in the study.
Nature Medicine published three peer-reviewed articles showing how, for the first time, AI has been shown to reduce mortality in a hospital setting using Bayesian Health’s Adaptive AI solution.
The second of three studies demonstrates how higher provider adoption of the deployed sepsis alert system is directly associated with significant reductions in patient mortality, morbidity and hospital cost. The study correlates Bayesian Health’s platform’s precision, model sensitivity, ease of use and integration within the EMR workflow with an 89% sustained adoption by physicians and nurses.
NOTE – At the core of the research was an AI system referenced as Targeted Real-time Early Warning System (TREWS). Initially developed at Johns Hopkins, Bayesian Health has commercialized and advanced the methodology, integrating it into a broader adaptive AI platform that enables integration, monitoring, and tuning to account for real-world variations in populations and workflows and scaling to multiple condition areas. Bayesian led and managed the deployment across all five emergency departments and hospitals in the study.
As a user exploring deployment of healthcare AI, a key challenge has been the lack of a comprehensive assessment for measuring bias within your solution. Further complicating matters; most scientific papers focus on one or two aspects of bias while meta-reviews or industry tool-kits simply surveil or summarize existing quantitative measures.
In a first-of-its-kind research paper by JAMIA (a leading informatics journal) our very own Suchi Saria brings together a team of experts in health disparities, health services, machine learning and informatics, providing a rare, end-to-end perspective of bias.
If you are exploring deployment of AI to improve patient outcomes, lower readmissions and decrease alert fatigue, this checklist provides a solid foundation for identifying and overcoming sources of bias.
Download the full checklist here to better identify and understand bias in healthcare AI solutions.
Bayesian Health’s platform drove faster treatment for sepsis, with high provider adoption.
A large, five site study analyzing use and practice impact over two years for Bayesian Health’s sepsis module showed high sensitivity (80%+) with high precision (1 in 3 alerts were provider confirmed). Sepsis is a needle in a haystack problem, so it’s hard to achieve high precision at high sensitivity, and especially harder where you’re also optimizing for earlier detection times. But all three of these things are critical to cracking the code on earlier sepsis recognition and prevention.
Bayesian’s high quality, timely clinical signals resulted in 1.85 hour faster life-saving patient treatment driven by high provider adoption (89%).
With research showing the average adoption of clinical decision support tools to be in the low double-digits, our high adoption is exciting news, and shows physician confidence and trust in the Bayesian insights, leading to proactive, earlier care. This is great news for patient outcomes, where for sepsis every hour of delayed treatment directly impacts mortality rate.
Though predictive tools are widely used in the healthcare setting, there are no official rubrics or guidelines for evaluation or requirements for consistent regulatory oversight. But, what can often happen during deployment is something called “dataset shift.” Dataset shift occurs when a tool “underperforms because of a mismatch between the data set with which it was developed and the data on which it is deployed.” When dataset shift happens, the model can be considered “unsafe.”
As a result, it’s up to the predictive tool’s developers and implementers to monitor and evaluate the solution in order to ensure model safety, sensitivity and precision. Additionally, the tool’s users—clinicians—are a critical component of evaluation, as they are the individuals who can detect or see any dataset shift issues first.
Though evaluating for dataset shift can prove challenging, as it can happen for a variety of reasons (i.e. there can be population changes, physician behavior changes, etc.). It is essential to evaluate any data-driven predictive solution for dataset shift, in order to have safe and robust predictive tools at the bedside.
Bayesian Health’s CEO, along with other experts in the field, just published a letter in the New England Journal of Medicine (NEJM) outlining the causes of dataset shift and how to successfully recognize and mitigate dataset shift through both end-users and AI governance teams.
Bayesian Health’s predictive AI platform integrates the guidelines and rubric laid out in the NEJM, to monitor for dataset shift and ensure model safety. We believe this type of evaluation is an essential part of any AI tool, and this type of ongoing monitoring is how Bayesian’s platform is able to be deployed in a variety of populations/demographics, and adapt to differing or new physician and care team behaviors and practice patterns.
You can read the full NEJM article here, and you can learn more about the AI/machine learning strategies we use to overcome common hurdles faced by many in the field, here.
Many hospitalized COVID patients experience acute respiratory failure (ARF), but it has been difficult to predict which patients will need advanced respiratory support (e.g., mechanical ventilation) and which will not. Many predictive models have been reported for COVID patients, but most predict only the most severe outcomes (ICU admission, death, etc.) and only make a single prediction at the time a patient is admitted to the hospital. In an article published this week in Critical Care Explorations, we describe ARC (Anticipating Respiratory failure in COVID), a model that continuously monitors patients throughout a hospital encounter and identifies patients at risk of experiencing ARF. We collaborated with researchers at the University of Washington to validate the model on data collected from eight hospitals across two geographically distinct regions. At 75% specificity, the model achieved 77% sensitivity and predicted ARF at a median time of 32 hours prior to onset.
In addition to ARC, Bayesian has also trained models that predict ARF in non-COVID patients. Early identification of these patients would facilitate proactive care, leading to better patient outcomes and lower costs for health systems. Even prior to COVID, respiratory failure accounted for ~30% of unplanned transfers to intensive care units.
Read the full paper here.
Predicting events using clinical data is challenging because individual data points are often missing and/or noisy. For a given patient, input data are often collected at different times and with varying frequency. For any given measurement, the frequency can also vary between patients, often based on a clinician’s suspicion of potential complications. Learning strategies that do not account for this variation will be much less accurate. This study published in IEEE Transactions on Pattern Analysis and Machine Intelligence describes two solutions to this issue. The first is applying smart interpolation techniques that account for missing data based on patient context. The second is introducing a “Wait and Watch” policy that trades off delays in making predictions against the cost of false alerts. Compared to the standard approaches (e.g., logistic regression), these techniques achieve 3-4X higher sensitivity at comparable positive predictive value.
Bayesian Health applies these and many other leading-edge learning techniques to optimize performance and clinical actionability in our predictive models.
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction
Read the full paper here.
A targeted real-time early warning score (TREWScore) for septic shock
Sepsis is a leading cause of death, contributing to 1 in every 2 or 3 hospital deaths. Early identification and treatment has a dramatic impact on morbidity and mortality. In a cover article published in Science Translational Medicine, Dr. Saria and colleagues showed that routinely available vital signs and lab results could be used to predict which patients would experience septic shock. TREWScore (Targeted Real-Time Early Warning Score) was more accurate than a routine screening protocol and another score used clinically for predicting septic shock (MEWS). TREWScore identified patients 28.2 hours (median) before onset with ⅔ of cases identified before any sepsis-related organ dysfunction.
This was the first study to show that applying machine learning techniques to clinical data could be used to proactively identify patients at risk of sepsis. Since then, over 200 related papers have been published.
Read the full research paper here.
