Reducing Variability of Care with Bayesian’s AI Platform

March 3, 2023

Inconsistent and variable care delivery can lead to negative consequences for hospitals, patients, and payers. Bayesian’s AI platform is designed to help reduce variability in care, improve clinical outcomes, and enhance overall patient satisfaction.

Defining Variability

Variability of care refers to the differences in the way patients are treated and the outcomes they receive based on a wide array of factors such as demography, what hospital they have access to and the care practices employed in their treatment. There are many factors contributing to variability, including lack of evidence-based protocols and fragmented care delivery systems.

The Impact of Variable Care Practices

Variability of care can lead to unequal access to care, inconsistent quality of care, and higher costs for patients, hospitals and payers. These can affect patient satisfaction and create a lack of trust in the healthcare system. Payers also face financial consequences from variability in care, as they may have to cover the cost of additional treatments, longer hospital stays, or readmissions.

How Bayesian’s AI Platform Addresses Variability

Bayesian’s AI platform is designed to address variability in care by providing clinicians with real-time insights and personalized treatment recommendations. The platform uses an adaptive, modular framework that considers a patient’s unique physiology, clinical protocol, provider workflow, and hospital operations. This approach helps clinicians make more informed decisions and reduces the likelihood of variability in care delivery for a wide range of critical condition areas such as sepsis, all-cause deterioration, pressure injuries, and transitions of care, just to name a few.

Practical Uses of Bayesian’s Clinical AI to Standardize Care Practices:

Bayesian’s AI platform can be tailored to different clinical settings and hospital needs, providing a personalized approach to care.
The multi-modal platform identifies patterns and trends in patient data that traditional practices/methods may miss.
Bayesian provides access to evidence-based guidelines and best practices, leading to better treatment decisions and outcomes.
By automating certain tasks, the platform reduces the time it takes to provide care, improving patient outcomes and reducing costs associated with unnecessary procedures and extended hospital stays.

Our Unique Approach

Unlike previously studied models, Bayesian’s approach is clinically grounded, thinking like a clinician. Clinicians use the platform as an extra set of eyes and ears, making it an effective tool in improving the overall quality of care. The platform uses Bayesian statistical models to analyze patient data, such as medical history, lab results, and vital signs. These models allow clinicians to identify potential risks and predict patient outcomes based on data from similar patients. The platform also provides real-time feedback to clinicians on their treatment plans, alerting them to any potential issues or opportunities for improvement.

Benefits of Reducing Variability

Variability in care can have negative consequences for hospitals, patients, and payers. However, with Bayesian’s AI platform, clinicians can have access to real-time insights and personalized treatment recommendations that can help reduce variability in care delivery. By using Bayesian’s AI platform, hospitals can improve clinical outcomes, enhance patient satisfaction, and reduce costs associated with longer hospital stays or readmissions.

Does Ethical AI Development Rely On The “Algorithmically” Underserved?

Press

Data and Trust: Digital Transformation – The Impact of Machine Learning in Healthcare

Active Learning (Blog), Podcasts

1/03/23 – Duality – Data and Trust: Digital Transformation – The Impact of Machine Learning in Healthcare

An informative discussion between Prof. Shafi Goldwasser, Chief Scientist and Co-Founder of Duality and Suchi Saria, Founder of Bayesian Health focused on where digital transformation and healthcare meet, and how can it impact, improve, and lead to better outcomes?

Saria asks, can the public place their trust in advanced algorithms?

The answer is yes – but only after the public becomes more aware and educated about how models can read data objectively and help healthcare providers make critical decisions. The public should also be educated about how technology is extensively and rigorously validated to help blend human ingenuity with machine-driven decision making.

Once the digital transformation of healthcare is complete – and if done well – Saria hopes to see a significant reduction in mortality rates as well as in healthcare costs. And it’s within reach within as little as 10 years – driven by the innovative technology providers, with the deep expertise of statistics, machine learning and data privacy to optimize patient care.

LINKS

Read the Blog

Read more press here

2022 Year-End Review-AI: Artificial Intelligence Initiatives Accelerate in Healthcare

Press

12/22/22 Healthcare Innovation – Year-End Review-AI: Artificial Intelligence Initiatives Accelerate in Healthcare

Amid the challenges of calendar year 2022, one of the bright spots was the acceleration in artificial intelligence-related activity—both clinical and non-clinical—in healthcare.

“Careful examination of the sepsis tool implementations have found that, when Suchi Saria’s team at Bayesian Health looked closely at the success levels of sepsis-alert algorithms, they found that the actual rates of improvement in intervention turned out to be far more modest than they appeared at first glance.

In fact, she said, ‘I’ve seen incorrect evaluation. People measured sepsis for mortality, then deployed the tool, then used billing code data, and evaluated. But it looks as though you’ve improved mortality, but there’s a dilution effect.’” In other words, it’s turning out that clinician and clinical informatics leaders must necessarily test out and recalibrate any algorithms developed elsewhere, in their own organizations, since, as Patterson told me, clinicians document inside their own organizations’ electronic health records in individual ways.

LINKS

Read the Healthcare Innovation article

Navigating the ‘Wild West’ of AI adoption in healthcare

Press

12/20/22 Modern Healthcare – Navigating the ‘Wild West’ of AI adoption in healthcare

Right now, clinical AI adoption in healthcare can feel like the ‘wild west’ due to the lag in the regulator’s ability to keep pace with the dynamics within the marketplace. Consequently, health systems are taking matters into their own hands, forming internal guardrails to measure performance and substantiate AI investments across their clinical ecosystems. Ultimately, it comes down to innovation, risk-appetite and, most importantly, trust.

Our founder and CEO, Dr. Suchi Saria is quoted – “You can have the best technology in the world, but if [care teams] don’t trust it, they won’t use it, and you can’t see any benefit”.

LINKS

Read the Modern Healthcare article

Does Ethical AI Development Rely On The “Algorithmically” Underserved?

Press

11/29/22 Forbes – Does Ethical AI Development Rely On The “Algorithmically” Underserved? CHAI’s Mission

CHAI co-founders Dr. Halamka and Dr. Anderson and CHAI member Suchi Saria of Bayesian Health discuss the importance and timeliness of CHAI’s mission, and share how the organization plans to prioritize patient safety, reliability, equity, transparency, and trust in the healthcare AI development process.

“AI as a field is evolving very rapidly. As a result, there is variable expertise amongst groups in how to go about implementing it correctly and evaluating whether what they’ve implemented is working. There is significant opportunity to accelerate AI adoption by sharing best practices and developing guardrails that the broader community (government, payor and provider groups) can benefit from.” Dr. Suchi Saria

LINKS

Read the Forbes article

Early detection of sepsis with the help of AI

Press

11/20/22 Healthcare In Europe – Early detection of sepsis with the help of AI

Suchi Saria, PhD, director of the Machine Learning and Healthcare Lab at Johns Hopkins, who led this work, explains that TREWS automatically and continuously monitors disparate clinical data, including vital signs, laboratory data, medication history, procedure and clinical history, and physician notes. It generates a continuous real-time “sepsis score” that can trigger an alert to healthcare staff. Clinical caregivers can analyse why the TREWS alert was generated, accept or dismiss it, and initiate timely treatment on patients confirmed to be septic.

‘Our results showing high physician adoption and associated mortality and morbidity reductions are a milestone for the field of AI,’ comments Saria. ‘They are the culmination of nearly a decade of significant technological investment, deep collaboration, the development of novel techniques, and rigorous evaluation. Further, what’s most exciting here is that this approach is applicable not just to sepsis but to many other critical complications.’

LINKS

Read the Healthcare In Europe article

Doctors Still Struggle to Diagnose a Condition That Kills More Americans Than Stroke

Press

10/16/22 The Atlantic – Doctors Still Struggle to Diagnose a Condition That Kills More Americans Than Stroke

In July, Johns Hopkins researchers published a trio of studies in Nature Medicine and npj Digital Medicine showcasing an early-warning system that uses artificial intelligence. The system caught 82 percent of sepsis cases and significantly reduced mortality. While AI—in this case, machine learning—has long promised to improve health care, most studies demonstrating its benefits have been conducted using historical data sets. Sources told me that, to the best of their knowledge, when used on patients in real time, no AI algorithm has shown success at scale.

LINKS

Read The Atlantic article

Learn more about Bayesian’s peer-reviewed research on sepsis here

A New AI Tool May Help Detect Blood Poisoning

Press

10/12/22 Smithsonian Magazine – A New, Transparent AI Tool May Help Detect Blood Poisoning

The most impressive aspect of TREWS, according to Zachary Lipton, an assistant professor of machine learning and operations research at Carnegie Mellon University, was not the model’s novelty, but the effort it must have taken to deploy it across five hospitals and 2,000 providers over a two-year period. “In this area, there is a tremendous amount of offline research,” Lipton said, but relatively few studies “actually make it to the level of being deployed widely in a major health system.” It’s so difficult to perform “in the wild” research like this, he added, because it requires collaborations across various disciplines, from product designers to systems engineers to administrators

LINKS

Read the Smithsonian Magazine article

Learn more about Bayesian’s peer-reviewed research on sepsis here

What it takes for doctors to trust AI-triggered sepsis alerts

Press

09/26/22 AMA – What it takes for doctors to trust AI-triggered sepsis alerts

Increasing adoption in sepsis AI with Bayesian. “This is a breakthrough in many ways,” said study co-author Albert W. Wu, MD, director of the Center for Health Services and Outcomes Research at Johns Hopkins Bloomberg School of Public Health. “Up to this point, most of these types of systems have guessed wrong much more often than they get it right. Those false alarms undermine confidence.”

LINKS

Read the AMA article

Learn more about Bayesian’s peer-reviewed research on sepsis here