Could AI-Driven Medical Billing be The Key to Keeping Struggling Hospitals in the Black?
AI-Driven Medical Billing

The entrepreneurs behind Sift Healthcare are using predictive analytics (AI) to crack the code on unpaid medical bills, helping health facilities work smarter when managing budgets.

Last month, Alabama’s Georgiana Medical Center closed its doors, making it the 13th Alabama hospital to close in eight years. Seven of the 13 shuttered hospitals served rural communities.

In a statement from the Alabama Hospital Association’s president, Donald E. Williamson, the Georgian Medical Center’s closure was cited as just another indicator of an

untenable situation — the cost of providing care is outpacing reimbursements.

“There’s simply no way for hospitals to continue providing uncompensated care to thousands of uninsured Alabamians,” Williamson said. “This is compounded by the fact that our hospitals aren’t receiving sufficient payment to cover the cost of the care they provide.”

Sadly, Alabama’s story isn’t unique. Across the country, 67 rural hospitals have closed since 2013, according to a report by the Medicare Payment Advisory Commission.

While the issue of hospital closures is complex and multi-factored, entrepreneur Justin Nicols saw an opportunity within one contributing problem: declining reimbursements. As the founder and CEO of Sift Healthcare, the Milwaukee-native and his team of predictive analytics and e-commerce tech experts are rethinking what health system executives call Revenue Cycle Management (RCM). Specifically, for health systems losing revenue due to unpaid bills, Sift Healthcare’s data platform offers the opportunity to better understand which patients are most likely to pay their bill, and which will toss their bill into the trash repeatedly. These insights help revenue cycle leaders chart a path to profitability.

Here’s how it works. The company’s tech applies predictive analytics and machine learning to all reimbursement and patient pay data streams to enable high-power denials management and propensity-to-pay modeling. In other words, they are going where no RCM analysis has gone before — into the abyss of billing data in order to retrieve granular payment insights not previously achievable.

The company’s CTO, Virgil Bistriceanu’s describes what they do (in a nutshell) as “root cause analysis of denials payer contract variances.” Thankfully, in addition to his extensive experience in startups and public companies [he was the first CTO at Centro Media ($300mm+ valuation) and a director at United Airlines], Bistriceanu is also a professor at the Illinois Institute of Technology. He can take us to school on this stuff.

“Where other systems are looking at 10, 20 or 100 data points, we are looking at thousands,” says Bistriceanu. “Our ability to build very rich models is greater than what others do simply because we are willing to work with a lot more data. We see things that others don’t.”

For More Information: https://healthtransformer.co/could-ai-driven-medical-billing-be-the-key-to-keeping-struggling-hospitals-in-the-black-222c6a25f10b

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