“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.
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.
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.