Thought Leaders in Artificial Intelligence: Ram Swaminathan, CEO of BUDDI.AI (Part 1)

You have read our coverage of AthenaHealth over the years in the healthcare IT space. BUDDI.AI is taking an AI-driven approach to healthcare coding and billing.

Sramana Mitra: Let’s start introducing our audience to yourself as well BUDDI.AI.

Ram Swaminathan: I am the co-founder and CEO of BUDDI.AI. We focus on building the next generation of artificial intelligence for healthcare.

Sramana Mitra: Okay. Let’s double-click down and look at the use cases and the types of customers that you are applying your capabilities to?

Ram Swaminathan: We started working on the foundation of healthcare almost eight years ago. This is your healthcare data which includes medical records, benefits coverage, 837 and 835 insurance claims, and the contracts. At the foundation, you have all of the documentation that hospitals and clinics deal with on a daily basis. These documents are unstructured. 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.

At the foundation, we built what we call a contextual lake. The use case that we have been focusing upon is to structure the unstructured data in healthcare. That is the first use case that we are solving. 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.

For 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 a hard problem statement because you have to interpret with little information on the provider side. A lot of the time, things slipp into the crack and the claim gets denied by the insurance. This is a costly denial. You have to rework those denials. In most cases, the payer is never reimbursed.

Another use case is medical coding. We are automating medical coding. In the last 30 plus years in America, you would do medical coding where the machine might even assist, but then there is always a manual laborer who looks at the medical record and picks up all these diagnosis codes, procedure codes, and modifiers. Those codes are the ones sitting inside a medical claim to get reimbursed by the insurance company.

That is the business of healthcare in America. If the claim doesn’t have the right codes, the insurance is going to look at those codes and deny them either partially or fully. The hospitals and clinics now have to fight those claims by spending more dollars for getting the same dollars. We automate medical coding autonomously with zero human intervention and an accuracy guarantee.

Finally, the most important one is billing automation. We take these codes and put them in a claim. We then predict and prevent a claim denial even before it goes to the insurance companies. We are hoping that we are going to keep those denials within the next 12 to 24 months and help the providers get reimbursed. We live in a pandemic and one of the things that we appreciate is all the healthcare workers. We also have to appreciate that these claims have to get paid by the insurance companies. If we don’t help them on that front, then it’s a losing battle.

This segment is part 1 in the series : Thought Leaders in Artificial Intelligence: Ram Swaminathan, CEO of BUDDI.AI
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