Big Data Mining Speeds Claims Payments

Sean Riesterer, director of revenue cycle and reimbursement for Silverton (Ore.) Health, knew he had to juice the time between rendering of service and submission of the bill, but he didn’t know how until …


Health care Big Data might be the talk of the town, but many efforts are still in that “talk” stage of implementation. HDM this month is profiling real-world efforts to harness Big Data for some of the pressing challenges facing providers.

Profile: Silverton (Ore.) Hospital

Sean Riesterer, director of revenue cycle and reimbursement for Silverton (Ore.) Health, knew he had to juice the time between rendering of service and submission of the bill, but he didn’t know how until he began comparing his organization with more than 1,000 others through a claims database maintained by RelayHealth. One in five of the hospital’s claims were taking between five and 10 weeks to get submitted, and the overall average was 30 days—too long for the financial health of the 48-bed organization.

“Claims data is great whether you’re talking clinical, operational or financial analysis,” Riesterer says. “It’s the best mirror we have that comes even close to being standardized. If you figure that we generate 10,000 claims a month, then we are talking ‘big data.’ ” The data is also close to real time, so Riesterer can see trends quickly, as well as the effects of any changes. “Being able to tap into it daily saves me a lot of time.”

RelayHealth’s clearinghouse processes about 20 percent of claims nationally, and using that data, Riesterer was able to identify 50 hospitals that were in the Pacific region, had between 25 and 100 beds, and offered both emergency and obstetric services. He backloaded a year’s worth of his own data into RelayHealth’s analytics programs, and quickly identified which specialties were lagging in service-to-submission, compared with peer organizations.  Documentation delays were the main culprit: one physician would finish his charts within five days, while another might take 40, with no apparent difference in the quality of either the care or documentation.

“Clinicians can have a different perception of documentation, so it’s important to have data that stands up,” Riesterer says. Having reliable data on the performance of other hospitals, as well as details on claim rejections and denials, helped him work with department heads to remedy documentation deficiencies and correct recurring errors in the claims.

It took Silverton only a few months to reduce the average service-to-submission time to 13 days, and its overall accounts receivable dropped by about 20 percent as well. Riesterer doubled his cash on hand.

‘I’m not going to say we’ve conquered the world, but having access to a large amount of claims data has greatly improved our revenue cycle and showed us things that were harming our cash flow,” he says.

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