Are you looking for vendor-supported tools or platforms to be more data-driven or to drive predictive analytics? Or maybe you are looking to find a solution to an issue?

Well, I won’t tell you what to buy, however, I will tell you what you should be asking!

Per Bayesian Health – These are 10 consistent components every predictive tool needs to have in order to have an impact on patient outcomes

Looking at the list below I can agree on every point. However, at the same time, each point is up to interpretation. Furthermore, these are broad points written with #healthcare in mind. I think this list can help many who are looking at implementing #ai, and even #automation initiatives. That being said, it is always good to clearly understand your target audience and know who will be impacted by adopting new technology. Some key perspectives would be to start by understanding that this will be a journey and getting that alignment ahead of time with your C-Suite or stakeholders will 100% be key in driving this effort forward.

*Please note* – Each one of the 10 points below has a very insightful explanation attached to it. However, due to the size limitation for Linkedin posts. I had to remove them. The link to the PDF will be in the comments below.

10 consistent components every predictive tool needs
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