Case Study: Transforming Uncompensated Care Via Data Analytics, Part 3

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Bad debt cripples healthcare providers, and ever-increasing regulation and governmental scrutiny only serves to exacerbate the problem. But for some, it creates an opportunity to use new technology in innovative ways to transform a constraint into an benefit, not only to patients but also to the provider bottom line.

For James Logsdon, vice president of revenue cycle operations at Texas Health Resources, a chain of 15 hospitals in the Dallas/Fort Worth area, the new regulations and reporting as required under the Patient Protection and Affordable Care Act legislation, rather than prove to be onerous became an opportunity to leverage their data analytic capabilities to create a repeatable business process that “will save money in the revenue cycle, improve patient relations and create a better patient experience.”

In this three-part article, drawn from Logsdon’s presentation at the HFMA Leadership Conference, he describes how his organization employed data analytics to not only fulfill IRS reporting requirements, but also reduce the cost of charity care processing, increase self-pay collections, and shrink the cost-to-collect ratio of Texas Health’s revenue cycle.

(For Part 1, click here; for Part 2, click here)

Part 3: The Impact of Data Analytics on Charity Care

Inserting data analytics into the charity care workflow made the process more efficient, but the question everyone wants to know is, what was the impact on the bottom line? How did it affect expenses and collections?

According to Logsdon, net expenses decreased and collections from self-pays increased. But before those benefits were realized, a lot of explaining had to happen, he says.

In 2010 using the manual charity care determination process Texas Health wrote off $20.5 million per month to charity care. The Texas Health team went live with the presumptive charity analytics workflow in early 2011. At the end of the first full month, the results were eye-opening: total charity care jumped to $26.7 million, more than $14 million of that identified by the presumptive charity analytics.

The new process was not only finding new charity care, but it was also finding much of that charity care that previously had been identified using the manual process. At the same time, bad debt plunged so that by the end of the budget year, Texas Health blew out its charity care budget by $48 million and was $106 million favorable on bad debt. “You’re going to have some interesting board conversations on why that change,” Logsdon says.

The big question, at least as far as CFOs are concerned, is what about collections? Logsdon says before implementing the new system, he was collecting 1.3 percent on uninsured ER patients, and that represented accounts that were three years old. After migrating to the new data analytics workflow, collections hit 1.19 percent after the first month. “Think I’ll get to one point three percent?” he asks, producing a chuckle from the audience.

That first full month after the new system was in place the Texas Health collections team had its highest month ever, Logsdon says. The reason, obviously, is that collectors were focusing on those patient accounts that had been identified as having the highest propensity to pay, rather than wasting time on the vast majority who could not or would not pay.

Not only did revenue increase, but expenses fell by $600,000, the result of reduced postage and staff resources. “We’re delivering a high quality patient experience while collecting more cash, and at a significantly lower cost,” Logsdon says.

The presumptive charity analytics system is only the latest in what has been a five years of focusing on increasing efficiency and cutting costs in the collections process, Logsdon says. Prior to 2008, Texas Health’s cost-to-collect increased an average of 4.1 percent per year. Beginning in 2008, that increase has not only been stemmed, but the actual expense has dropped by 4 percent per year.

Not only is Texas Health able to cut expenses, including, Logsdon says, a reduction in headcount, it also is identifying charity care in what had once been written off as bad debt, and thereby increasing the community benefit reported to the IRS.

It also opens up possibilities for the future opportunities. By using data analytics and other process improvements, Texas Health has been able to increase self-pay revenue in recent years. “The continuing increase in self-pay revenue make it clear that a smarter approach to self-pay collections and charity determinations are needed – using Data Analytics is one such solution,” concludes Logsdon. “We have to be more strategic in how we approach self-pay collections.”

Part 1: The Charity Care Challenge

Part 2: Introducing Presumptive Charity Analytics
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Posted in Medical Receivables, Patient Experience, Patient Financial Services .

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