Closing the Last Mile: AI's Role in Healthcare Transformation

Demi Radeva: We are on HIT Like a Girl, and I'm Demi Radova. 

Kim Perry: And I'm Kim Perry, the Chief Growth Officer of emtelligent. 

Demi Radeva: Kim, let's start with a quick intro. Can you share a bit about your background and your role in healthcare? 

Kim Perry: Absolutely. So my background, I like to say, functionally grew up in sales and account management.

Spent most of my career at a Fortune 500 company. Learning from what an established sales organization looks like, then moved into management consulting. And in management consulting, I've been in the healthcare practice, so I've been focused on the payers, the health systems, and really helping them with their business driven transformations enabled by technology.

And then I moved to early stage health tech to help them through their growth and scaling period. So my participation is now as the chief growth officer with Intelligent. We have been formally in market for about two years, and were an R& D company for about seven years, building the maturity of the product.

And we have now had our coming out party for the last couple of years. And it's certainly been a fascinating time for clinical AI in this market. 

Demi Radeva: We're here at Vive discussing what's coming with the new administration. From your perspective, what are the biggest policy shifts or industry changes on the horizon?

Kim Perry: There's a lot of speculation of what's to come and it's still unknown. So we're all keeping our heads down and doing what's right in front of us. 

Demi Radeva: How is that affecting your day to day? 

Kim Perry: It doesn't really affect my day to day. I just think there's a ton of activity. And I like to say upon reflection over the last three years, 2023 was really a year of education, at least for AI and health care.

So people were still understanding what the technology is, what it could do, what the use cases we might be pointing at, you know, understanding what potential ROIs, so it was very much a year of education. 2024 was, I like to say, the year of enlightenment. People were playing with more readily available technology.

So, ChatGPT was introduced, other LLMs were introduced, so people were using these tools for the first time. So, a lot of science fair projects, a lot of playing, but there was also Enlightenment of what works and what doesn't. I do think what came out of last year was an understanding that these are powerful tools and technologies to implement, but there is going to be the last 20%, so they got 80% of the way there, but they're not solutions that are ready to scale.

So how do we close that last 20%, especially in healthcare where accuracy in the data is so important. 

Demi Radeva: Clinical documentation remains a major burden for providers and unstructured data is a huge challenge. I'm curious, how is AI, particularly NLP, helping to structure and extract meaningful insights from the data?

Kim Perry: Documentation continues to be a challenge. Our providers are trained to use pros. They're trained to use Language to communicate, and it's not easy to put their communication into discrete fields and drop downs within the EMR. So they still need the ability to have this prose, the unstructured text. And so the burden around the documentation has been alleviated quite a bit with the ambient listening technologies.

So the ability to listen to the conversation and take the note-taking off of the plate. But the reality is it's still an unstructured note. Going into the M. R. And it's not usable downstream. And so that I think is the next generation of enhancing clinical documentation is how do we now structure the note at the point of capture so it can be usable downstream versus using manual intervention to Extract the insights from the data, whether it be for billing purposes or point of care or payer use cases, there's plenty of use for it for what's in the unstructured note, but it starts with the documentation.

So how we capture the structure at the point of documentation is really, I think, the next evolution. 

Demi Radeva: Do you think there are any policy or regulatory hurdles that are preventing broader adoption of this technology or is it the technology itself? 

Kim Perry: I don't think the regulatory, they're not hurdles for adoption.

Yeah. From the regulatory side, I think it's really the technology and the accuracy of the technology. And can it be usable at scale? So what I think is the barrier to adoption right now is I'm going to reflect back on the last 20%. We can get 80 percent of the way there. That's still not good enough to be implemented and adopted at scale.

We have to get closer to that hundred percent. 

Demi Radeva: How does AI powered NLP contribute to solving the challenge of interoperability? 

Kim Perry: When you are sharing clinical data, it is still in PDFs, faxes, image based documents, and those are challenging to work with. That is not clean data at all. And so where NLP comes in and some of the other tooling and technology that we've created is.

Taking those dirty data challenges and being able to extract the, not only the text from those documents, but highly structuring the text to be able to be used for a lot of different use cases. So NLP is structuring. that unstructured text to be able to be used by computers, readable by computers. 

Demi Radeva: What is a common misconception about NLP and AI assisted clinical documentation that you think needs to be debunked?

Kim Perry: The common misconception around NLP as it relates to clinical documentation is that it's easy that there are a lot of tools that can be applied to generic NLP tools that can be applied to clinical language. And that's not true. 

Clinical language is a different language. It's not English and traditional approaches to NLP haven't worked.

And so taking a unique approach to NLP, which for us is coding to. I'm not going to necessarily go into that, but yeah, so I think using traditional NLP approaches applied to clinical language isn't working. It's not going to get the level of features and accuracy that is needed in healthcare by using the traditional approaches or traditional tooling out there.

So you're going to need a healthcare specific NLP engine, a clinical grade. 

Demi Radeva: Does that mean providers are actually involved in the process of building the NLP and or coders and Yeah. How do we make it better? 

Kim Perry: The approach that Intelligent Tech was a right brain, left brain approach. So our CEO is a physician, so he understands the language of medicine.

Our CTO is a researcher, right? He is a professor in computer science with a specialty in NLP. Understands the most modern approaches to solving language, but doesn't understand the language. So you need to really blend those two, the clinical lens as well as the technology lens to really get after the challenge, which is understanding the medical language.

Demi Radeva: I'm curious, many health systems hesitate to adopt AI due to concerns of accuracy, bias, and even regulatory uncertainty. I love what you guys are doing as in blending the two skill sets. And so I'm curious, are there additional things that can be done around accuracy bias? That can help advance the technology adoption 

Kim Perry: adoption, certainly with physicians or providers, they need to trust the technology and the way they gain the trust is to have transparency on where they found the information.

So what we've done again is provide the transparency. So there is proof in the answers that we're presenting in front of a provider of where it came from. So they can point back to the source of truth. Versus some of the generative tooling is certainly not explainable and oftentimes hallucinates and makes things up.

So, and I think to overcome the challenge of adoption is you have to have the transparency. You have to have the proof. 

Demi Radeva: Looking ahead, I'm curious, what's the next big frontier for AI in healthcare? 

Kim Perry: The next frontier is. I will continue to go back to closing that last mile, closing the gap to be usable in healthcare.

It has to be trained from healthcare data, focused on healthcare. You can't use general, general tooling for the healthcare ecosystem. So I believe the next frontier into clinical AIs and driving towards the adoption is really finding those. Healthcare specific tools that can close that last mile and drive the adoption.

So move beyond the awakening into the execution and adoption. 

Demi Radeva: I'm curious. Can you paint the picture for us once we are beyond the adoption? Like what does the world look like? 

Kim Perry: Hopefully more efficient, more cost effective. This industry certainly needs to transform. It is not in a position where we can currently sustain the existing healthcare ecosystem the way we're going.

So we need to use tools like this to advance and transform. So hopefully in the next five years, we are going to see a much more efficient world. We don't have enough physicians or clinicians to, to give us care. So we need to take all of the administration burden as much as possible off of their plates so we can get back to giving care.

Demi Radeva: When I think about giving care, I'm curious, is the audience just providers? Or does the audience also include caregivers? Is it, who is actually going to be utilizing this type of technology? 

Kim Perry: I would say the entire healthcare ecosystem that uses clinical data. is participating. So you have the payers that are using clinical data to optimize their processes are used to using claims data only now with interoperability and the ability to get clinical data, their worlds can change as well.

They can optimize, they can be better risk adjusters. They can be better with prior authorizations and more timely with the prior authorizations, the payment integrity use cases. So payers are certainly going to contribute, uh, definitely on the health system side and the providers, but there's also research.

There's a lot of personas that can use this technology and then life sciences, right? Life sciences and biotech, they're very eager to use clinical data to advance drug discovery, move through clinical trials much, much faster. And so if you can unlock the data, the next frontier is in front of us, but it starts with data.

And unfortunately today, 80 percent of it's locked up in unstructured data. 

Demi Radeva: And how do we know that the technology is the issue. It's not the policies necessarily. So I was like, are there any policies that are prohibiting payers from using this data today? Or life science from using this data today? But it sounds like, again, it's more on the technology side.

Kim Perry: That's the barrier today. Is the technology advanced enough to be used? Yeah. I certainly can appreciate the need for transparency. 

Demi Radeva: Is the transparency the latest kind of version of the transparency policies? Is that enough? Time will tell. 

Kim Perry: I don't know if I have an opinion. 

Demi Radeva: Yeah. Okay, and then where can our audience find and follow your work?

Kim Perry: www.emtelligent.com Yeah. And then I'm on LinkedIn. 

Demi Radeva: Amazing. Thanks so much. 

Kim Perry: Yes. Thank you. 

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