Turning Unstructured Data Into Clinical Insights w/ Clinithink CEO, Chris Tackaberry | HealthBiz Brief

Chris Tackaberry (Co-Founder & CEO, Clinithink) discusses how his company's AI technology delivers rapid intelligence from unstructured healthcare data, empowering pharmaceutical and healthcare organizations to focus their time, money, and resources more effectively.

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CareTalk is a weekly podcast that provides an incisive, no B.S. view of the US healthcare industry. Join co-hosts John Driscoll (President U.S. Healthcare and EVP, Walgreens Boots Alliance) and David Williams (President, Health Business Group) as they debate the latest in US healthcare news, business and policy.

🎙️⚕️ABOUT CHRIS
Chris is a qualified physician and MSc Computer Science graduate who spent nine years in clinical practice in anaesthesiology and intensive care before embarking on a career in healthcare IT. His combined expertise in medicine, computer science and leadership has been the foundation for his stewardship of Clinithink’s strategic direction and growth.

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#healthcare #pathology #artificialintelligence #ai #healthcaretechnology #healthcarebusiness #unstructureddata #revcycle #clinicaltrials 

Transcript

0:02

Welcome to Health Biz Briefs. I'm David Williams, president of Health Business Group here with CliniThink co-founder and CEO, Chris Taka -Berry. CliniThink uses AI to deliver insights from unstructured clinical data. Welcome, Chris. Thank you very much, David. Great to be here. How does CliniThink play in rare diseases? Rare disease is relatively common. 30 % of the United States population has a rare disease, but extremely difficult to diagnose. And that leads to what's called a diagnostic odyssey, a very long time between the onset of the disease and the time at which it's diagnosed and therefore hopefully treated. There's increasingly, it can be the revolution in next generation sequencing. So DNA sequencing, genetic testing has contributed a lot to our understanding of the rare disease space, but the missing piece is in what's called the phenotype. physical characteristics of the disease, which must be understood in relation to the genetic data to make a diagnosis. That's a very, very difficult task to undertake manually. Very few clinicians have the skills and experience necessary to do it. Our technology can automate that process and speed up the diagnostic. How did you get pulled into that? I'm not sure that's where the company started. Certainly isn't. And actually, for my sins, I didn't think we'd be able to create any value whatsoever in that space. But Alexion, which is a leading pharmaceutical company in the rare disease space, they identified us. They had a very clear and specific understanding of the problem that they wanted to solve and they identified us as the best fit for solving that problem. We've had a very long and fruitful collaboration with them and they led us into the space. I want to ask you about the role of AI in revenue cycle management. Everybody is focused on revenue cycle management because that's how you get paid. If you don't get paid, you can't keep the doors open. What's the role of AI and how does ClennaThink fit into that? Revenue cycle management from a bird's eye view is about ensuring the provider or the health system gets compensated correctly for the care that was delivered. And the care that's delivered is described in the narrative record of that care and the visit summaries that we've just done, so it's at a detailed level. So that's the source, that's the starting point in any revenue cycle management process. And the labor and effort and manual chart review required... to manage that process end to end is vast and, you know, soaks up a lot of budget and bandwidth in a typical large complex health system. Luckily, there are things which AI can do a lot faster than those human operators, typically, you high volume tasks in fractions of the time taken for manual chart review. And that means that you can... support your revenue cycle management teams in various specific activities. We support clinical documents improvement, that's a right of the backhand end of the process, and denial management helping to find the evidence that justifies a claim later on in the process. There are many other pieces of technology that sort of sit in between which we don't overlap with, but that's where our customers have found value for the use of our capability in processing unstructured data at those two specific points. in the left to right process of brain recycling. Let's talk about real world evidence. This is an area of a lot of interest, specifically in the pharmaceutical world. And a lot of money has been spent, oftentimes actually working with that unstructured data to try to put it in the right type of a format that can be used. And I understand the sort of mixed success with that. Is that how you're seeing RWE? And how do you participate in that space? Right, so RWE exactly as you say is a way for the life sciences ecosystem to take advantage of data that's already been transacted and already been captured during the routine delivery and documentation of care, as opposed to prospectively capturing it in small clinical trials. And the benefits of doing it that way is you're repurposing data that's already been collected. And the disadvantage of doing it that way is you're repurposing data that was collected for another purpose. But it turns out that in the discharge summaries and visit summaries, these very thoughtful, rich and detailed documents written by physicians at these boundaries and the logical boundaries of different phases of care or care journey, there's a ton of valuable information that can inform the decisions that people need to make, fueled by or served by real -world evidence. And we can pull that detail out and we've got. some public domain examples of very successful collaborations with our partners Premier and AstraZeneca in a, for example, in a piece of work that was presented at the American Society of Clinical Oncology last year in lung cancer, looking at very, very large volumes of data and tens of millions of documents and finding individual, you know, small cohorts of patients within that large haystack, you know, whose characteristics are germane to a particular treatment area, in this case, lung cancer. Very powerful. Well, Dr. Chris Tachibarri, Clenith Inc. CEO, thank you for joining me on Health Biz Briefs. If you like what you hear, check out my Health Biz podcast for a full length interview with Chris about his life and career.