![]() ![]() With Practice.AI, we’re disrupting that pattern and giving healthcare workers an improved, automated, and truly intelligent RCM experience to go beyond management to actively predicting, preventing, and solving your most pressing healthcare challenges.” If we don’t help them on that front, then it’s a losing battle.Read More: SenpAI.GG is helping Gamers to improve their Gameplay using Artificial IntelligenceĬo-founder and CEO of BUDDI AI, Ram Swaminathan, said, “Until now, the healthcare RCM industry has been largely stuck in a cycle of management-managing claims, denials, and appeals as well as patients, providers, and payers. We also have to appreciate that these claims have to get paid by the insurance companies. We live in a pandemic and one of the things that we appreciate is all the healthcare workers. 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 then predict and prevent a claim denial even before it goes to the insurance companies. We take these codes and put them in a claim. We automate medical coding autonomously with zero human intervention and an accuracy guarantee.įinally, the most important one is billing automation. The hospitals and clinics now have to fight those claims by spending more dollars for getting the same dollars. 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. That is the business of healthcare in America. Those codes are the ones sitting inside a medical claim to get reimbursed by the insurance company. 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. 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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