Thought Leaders in Artificial Intelligence: Ram Swaminathan, CEO of BUDDI.AI (Part 2)

Sramana Mitra: There is a company that we covered extensively for a while in the payments space. This was Athenahealth. It sounds like what you are doing is like what Athenahealth does but with AI. Is that correct?

Ram Swaminathan: That is correct.

Sramana Mitra: Why don’t you expound on that? I’ll phrase the question a little bit more specifically. Athenahealth’s big innovation was having the expertise to be able to manage these codes, claims, and collections processes.

I call it a SaaS-enabled BPO. They take that process away from the providers. They facilitate the payments and they take a percentage of that payment as their collection fee. Now, you tell me how you position yourself?

Ram Swaminathan: For starters, I might have to disagree with you on SaaS-enabled BPO for Athena. We know of Athena as well and they are a classic BPO. There is no SaaS involved in that. A typical SaaS is a software as a service. The software that Athena has built exceptionally well is on the EMR front. They have had tremendous success on that front.

When it comes to billing, it becomes complicated when you talk about the SaaS model. That is the part that we have innovated. If you look at the billing side, 40% of your billing is coding. First, you have to read the medical record. That is where the problem begins if a physician has dictated the nodes in radiology, pathology, or a surgery setting. Those nodes are going to be different because a radiology node is going to look different compared to a surgery, pathology, or oncology node.

These nodes are also unstructured. This means that you have paragraphs of medical records which are dictated by the surgeon or the specialist. You will also have these physical vitals and lab diagnostic results. These data sets are all unstructured and semi-structured. When you get that data, it has to be interpreted before you can apply the regulatory rules in healthcare. This includes the medicated rules, the American College of Physician rules, or the AMA guidelines.

There are so many different guidelines in healthcare before you pick the medical, procedure, diagnosis code. Every specialty is different. Anesthesia has a crosswalk which is a little different from the way you code radiology or surgery. All of these nuances of medical coding per specialty make it extremely hard to automate. That is what we have innovated over the last eight years.

How do you autonomously code a given chart to a given code which is reimbursable by the payer? That aspect is the first aspect of billing. If you cannot do that step, then you are highly manual in that stage. After that, we go to the next step which is charge capture. You then go into framing that claim as we know it in the billing process. Traditionally, the industry called it scrubbing.

For many years in billing, there have been a lot of companies who adopted what is called the scrubbers. They scrub all these rules where a male code doesn’t apply to a female code and vice versa. There are basic rules and that rule is what we call the scrubber. We have that well. What the industry has struggled with over the years is predicting a denial and preventing that in the billing process even before you submit the claim to the payer.

The way to do that is to go back in time. It is similar to astronomy and the way to understand science, evolution, and the way we live. Astronomers look at the Hubble telescope and they go back in time. They look at the data sets and analyze them to predict what is going to happen in the future. That is exactly what we do. 

This segment is part 2 in the series : Thought Leaders in Artificial Intelligence: Ram Swaminathan, CEO of BUDDI.AI
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