In most cases, the payer is never reimbursed.Īnother use case is medical coding. A lot of the time, things slipp into the crack and the claim gets denied by the insurance. ![]() That is a hard problem statement because you have to interpret with little information on the provider side. The second use case we are solving is that once you interpret or contextual those healthcare data, we are now automating the functional requirements within healthcare.įor example, in prior authorization all the way up front, you have to identify whether this particular procedure or drug requires approval by the respective insurance company. That is the first use case that we are solving. The use case that we have been focusing upon is to structure the unstructured data in healthcare. This means that you need a human to interpret paragraphs and paragraphs of data to even work upon the data to help execute the workflow at every step to make sure that the claim gets paid by the insurance companies.Īt the foundation, we built what we call a contextual lake. At the foundation, you have all of the documentation that hospitals and clinics deal with on a daily basis. This is your healthcare data which includes medical records, benefits coverage, 837 and 835 insurance claims, and the contracts. Ram Swaminathan: We started working on the foundation of healthcare almost eight years ago. ![]() Let’s double-click down and look at the use cases and the types of customers that you are applying your capabilities to? We focus on building the next generation of artificial intelligence for healthcare. ![]() Ram Swaminathan: I am the co-founder and CEO of BUDDI.AI. Sramana Mitra: Let’s start introducing our audience to yourself as well BUDDI.AI. BUDDI.AI is taking an AI-driven approach to healthcare coding and billing. You have read our coverage of AthenaHealth over the years in the healthcare IT space.
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