Raheel Retiwalla

And then generative AI as a solution, potential solution came about.

And what we did is we looked at the power of generative AI and said,

Where can we apply it?

You know, there's so many different areas.

And you're seeing and hearing about AI, as you said earlier, lots of different places in healthcare.

But where are those opportunities when we think about the administrative waste, as I mentioned to you earlier?

And what it turns out is that a lot of the decisions that are made in healthcare, so whether I should approve someone's authorization for physical therapy, how much therapy should they get?

Where should they get it?

What should it cost?

All of those things are essentially rules.

Rules that are stuck inside PDFs, in guidelines, in policies, in regulations, in benefit statements.

So regardless of how much investments healthcare organizations have made to make things more digital and portals,

this this this particular act of validating the rule by some human somewhere to say should i do this what should i do here what policy applies here it just is what drove that 500 billion dollar administrative race in the us so our goal was to use and think about application of generative ai in unlocking those rules what if we just unlocked those rules and made them available to every workflow

every person involved in the healthcare continuum.

So they have access to the rules in a manner they can use consistently, accurately, and, you know, do what they need to do faster.

And that's kind of where generative AI became the sort of the, you know, the technology platform for us to essentially validate first, test it, and then now make it available through Boost Health.

Yeah, the biggest reason for us is this idea of a black box.

And this goes specifically to the point you made earlier about bias and just the trust in AI.

At the same time, there's a ton of innovation happening, right?

So there's a new point solution to solve problem X, problem Y, problem A with its own AI something.