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STAT – Race, Bias in Machine Learning

Press, Whitepapers/Case Studies

“People have this misconception that if they just include race as a variable or don’t include race as variable, it’s enough to deem a model to be fair or unfair,” said Bayesian Health CEO Suchi Saria.

 

Read the article here

https://www.bayesianhealth.com/wp-content/uploads/2022/11/Stat-Bias-in-Machine-Learning.jpg 720 1280 integritive https://www.bayesianhealth.com/wp-content/uploads/2023/01/Bayesian-Health-logo-2x-color.png integritive2022-06-28 14:33:062023-01-04 12:56:05STAT – Race, Bias in Machine Learning

The Essential Checklist for Predictive AI Solutions

Whitepapers/Case Studies

The development of predictive AI tools in healthcare shows tremendous promise in accelerating more accurate diagnoses and improving the safety and quality of healthcare.

However, what’s been lacking is a standard way to evaluate whether or not an AI tool will do what it says it does. As a result, health systems are often left on their own to develop a way to evaluate competing solutions from scratch. It is easy to spend precious hours researching available products, as there are many technical and logistical components to understand.

We created this checklist together with leading clinicians and informaticists detail the 10 consistent components every predictive tool needs to have.

Download the full checklist here to learn about the non-negotiable features, capabilities and requirements predictive AI tools need to be safe and effective.

https://www.bayesianhealth.com/wp-content/uploads/2022/11/1280-x-720-Checklist.jpg 720 1280 integritive https://www.bayesianhealth.com/wp-content/uploads/2023/01/Bayesian-Health-logo-2x-color.png integritive2021-10-24 16:29:522023-01-04 12:54:53The Essential Checklist for Predictive AI Solutions

Adopt and Engage: Bayesian Health’s Behavior Change Model

Whitepapers/Case Studies

As health systems look to adopt new technology platforms that engage frontline caregivers to improve patient outcomes, understanding how a platform interacts with users and sustains engagement is critical to understanding if the technology will be successful. Even the very best solutions won’t have any meaningful impact unless they are adopted by primary users, and used frequently and consistently.

 

Initiating this type of behavior change isn’t an easy task as health systems, clinicians, and frontline staff are overwhelmed and overburdened; systems are working with tighter staffing ratios and reduced margins, and many solutions provide vast amounts of data in multiple systems without the tools to assist frontline staff with analyzing and prioritizing alerts and relevant information.

 

Bayesian Health builds its products around several principles of behavior change, targeting three of the hardest stages—the preparation, action, and maintenance stages—to encourage continued use and maximize impact.

Read our latest whitepaper on how Bayesian secures adoption and long-term engagement.

Download
https://www.bayesianhealth.com/wp-content/uploads/2022/12/BH-Press-posts-05.jpg 720 1280 integritive https://www.bayesianhealth.com/wp-content/uploads/2023/01/Bayesian-Health-logo-2x-color.png integritive2021-06-10 14:36:192023-01-04 17:11:02Adopt and Engage: Bayesian Health’s Behavior Change Model

Recent Posts

  • Data and Trust: Digital Transformation – The Impact of Machine Learning in Healthcare January 3, 2023
  • 2022 Year-End Review-AI: Artificial Intelligence Initiatives Accelerate in Healthcare December 22, 2022
  • Navigating the ‘Wild West’ of AI adoption in healthcare December 20, 2022
  • 33 Biotech & Healthtech Companies to Watch in ’23 December 19, 2022
  • Suchi Saria Named Winner for Entrepreneur Award in the 2022 BIG Awards for Business November 29, 2022
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