Faced with labor-intensive tasks critical to maintaining good fiscal health, Hennigan wanted to take some of the manual work out of the arduous process of analyzing patient, clinical and financial data for billing inaccuracies. More specifically, he wanted to automate processes such as recognizing underpayments, managing credit balances and identifying lost charges.
After analyzing options, he came upon a system from Apollo Data Technologies, Chicago. The software uses predictive analytics to develop forecasts about future events-turning these revenue management functions from retrospective chores to real-time automated processes. More specifically, the software pulls a year's worth of data from various systems such as the hospital information system, normalizes the data and then applies logic based on historical patterns to automatically flag financial issues that need to be addressed.
Before tapping into the system, however, Hennigan had to convince staff members at the seven-hospital, Albuquerque, N.M.-based delivery system that the software had the smarts to do such complicated work. "There is the inherent health care bias saying that this new approach will never work because it's so hard to understand the health care business," he says. "I had to convince people that we needed to work with software that would actually solve our problems without requiring more work from humans."
After buying into the concept, however, the revenue management team has quickly realized that the method is, in fact, pretty powerful.
First off, the software is more expediently identifying underpayments.
"The software identifies what claims have been paid appropriately and then compares the model to the claims being pulled to determine if any have been underpaid," says Jeff Kaplan, CEO at Apollo Data Technologies.
Formerly, staff members would manually comb through the claims and if a payment appeared too low, the information would be loaded into denial management software. The program would act as a tracking tool and guide the revenue management staff member through the processes required to appeal the underpayment.
The analytics software, however, automatically identifies underpayments and produces documentation needed to send requests for full payment to the payers.
After using the system for just six months, the hospital has identified about $10 million in underpayments, $5 million of which has already been recovered.
Doing the Hard Work
The system also is being used to more efficiently manage credit balances-a notoriously time consuming process. Credit balances are patient accounts with payments and allowances that are more than the actual charges.
Such balances occur from a variety of billing and payment miscalculations, including incorrect estimates of how much patients owe up front or how much insurance companies will pay on a claim. These miscalculations must be resolved so that the hospital can either refund the money due to patients or insurers or verify that the money can, in fact, be moved from the balance sheet to the income statement.
The overall push for financial transparency, as well as the fact that Medicare requires that hospitals report all Medicare credit balance overpayment accounts on a quarterly basis, makes getting the work done even more important. When balances are not adjusted, hospitals face the risk of suspension of Medicare payments.
"It would take 50 to 60 people per year to properly do all of our credit balance management work," Hennigan says. "In today's health care environment, hiring 50 to 60 people is just not an option."
After using the system for just six months, Presbyterian identified $8 million in credit balance liability without adding additional staff.
This article can also be found at HealthDataManagement.com.
This piece is brought to you by the editorial staff of SourceMedia.
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