An elderly female patient stands at a reception desk at a doctor’s office and speaks with a female doctor.

How Big Data Healthcare Manages Payment Reconciliation


Payment automation streamlines workflow, improves accuracy, and accelerates access to working capital.

One of the most difficult bottlenecks in healthcare payments is often the painstaking and costly process of reconciling remittance advice files from insurance payers against the bank deposits the healthcare provider receives.

Between 80% and 90% of all large hospital systems in the U.S. currently perform this reconciliation manually, an inefficient use of time and resources. According to the McKinsey Quarterly, about 30% of all claims payments are on paper and require providers to manually reconcile the claims and deposit the checks. This adds to the already heavy workforce burden on the healthcare system, with an estimated one in five healthcare workers in the U.S. having quit their jobs since 2020.

To remove this costly constriction, Big Data Healthcare (BDHC), a wholly owned indirect subsidiary of Fifth Third Bank, figured out how to use intelligent data automation to reduce days of labor-intensive work to just a few minutes. The company’s flagship product, FUSE, is a platform that automates the reconciliation of remittance details to deposit information, producing a customized output file for the provider’s electronic health records system (EHR).

Automation and Machine Learning

Under the current payment system, the healthcare provider submits an electronic patient claim known as an 837 file to the insurance company or clearing house. The payer responds with an electronic remittance advice called an 835. Utilizing a combination of robotic process automation, artificial intelligence, and machine learning, Big Data’s breakthrough has been its ability to receive and process electronic data interchange files from any bank, insurance company, clearinghouse, or document management system that a hospital may use.

"We ingest input files from any clearinghouse, bank, and lockbox that a hospital system is using today; we do not require our customers to disrupt their current vendor technology," says Dean Puzon, President and Co-Founder of Big Data Healthcare. "Within about four to six weeks of onboarding a new healthcare customer, we are able to ingest nearly 100% of their payments and do not require them to change a thing. As long as we have a regular cadence of input files, the 835, and the deposit file, we can stand up FUSE relatively quickly."

One of the major advantages of such a solution is the sheer speed in which files can be processed and reconciled. For healthcare providers, the direct benefit is far faster access to much-needed working capital. It also yields substantial cost savings, because full-time employees previously working in manual reconciliation can then be redirected to other more important roles.

Puzon estimates that some of the existing BDHC customers now using FUSE previously needed a team of full-time staff members dedicated to reconciliation of remittance and deposits. "Their previous approach was to engage full-time resources to work on the reconciliation," he says. "But with FUSE processing those files, those employees can be quickly redeployed to other revenue-producing areas. Our case studies suggest that this automation results in 60%–75% cost savings."

These benefits do not only apply to healthcare providers. Puzon mentioned that one of Big Data Healthcare’s earliest clients was a research laboratory.

"When we first met with this customer five or six years ago, they had teams of full-time employees who were essentially manually reconciling 835 remittances back to deposits," he says. "Within a three-to-four-week time frame, FUSE brought them down to a staff of 10 full-time employees, who were then just working the exceptions and unreconciled files. Needless to say, there were substantial savings."

Supporting Growth by Acquisition Models

Big Data Healthcare has processed input files from 75 of the largest banks in the U.S., along with 13 of the largest clearinghouses. Since 2020, mergers and acquisitions of hospital systems have been on the rise, with larger providers seeking to expand their service offerings, improve access to specialists, and provide scale to reduce the costs associated with medical supplies and prescription drugs.

One of the major challenges for acquiring parties is that any acquisition typically involves inheriting legacy EHRs, banks, clearinghouses, and document management systems. Puzon says that intelligent data automation can help overcome these transitional hurdles.

Over the past five years, one of Big Data Healthcare’s largest customers has been a national and leading provider of home health, hospice, and palliative care, which has grown significantly through acquisitions.

"They were growing so fast they had no choice but to inherit those legacy banks and legacy workflows," says Puzon. "Our core competency is taking into account the legacy environment, automating manual tasks, and accelerating cash flow in any business office."

To aid larger providers and provide them with a more simplified workflow, Big Data Healthcare has also created add-on modules to FUSE such as ALLOCATE. This is an enterprise cash management solution that allows for the automated allocation of credits and debits, creating greater transparency with all bank deposits so that cash can be monitored in real time.

Another innovation is FUSE Intelligence, which builds on FUSE to provide payment and remittance reporting for any central billing office, cash posting, or finance team. By taking into account the files ingested by FUSE, it delivers a number of dynamic and interactive payment and remittance dashboards, which include cash monitoring and forecasting, provider-level balance adjustment monitoring, and employee productivity reports.

Highlighting Underpayments

Puzon says that while Big Data Healthcare handles patient and insurer files after the cases have been adjudicated, the data provided can help healthcare providers with the much-needed visibility into underpayments from insurers. Many hospital systems have traditionally lacked the resources to stay on top of this problem, but Big Data Healthcare customers are often able to redeploy staff to focus specifically on underpayments, overpayments, and credit balances.

"If an insurance claim wasn’t processed correctly from the beginning, the data we return can bring that much needed transparency back to the healthcare customer," Puzon said.

This is particularly vital in a world where healthcare systems are navigating the twin problems of declining reimbursements from both government and commercial insurance payers, along with available resource allocation, and rising expenses. With market forces increasing patient financial responsibility, making it less likely for providers to be paid in a timely fashion, hospital systems are finding they need to maximize dwindling resources.

"Professional settings and hospital systems need to do more with less," said Puzon. "They need to leverage technology and embrace automation whenever they can."

Puzon said he believes that in the coming years, the majority of manual processes associated with remittance and deposits will be ultimately replaced by automation in settings ranging from physician offices, hospitals, physical therapy, home health, dentists, laboratories, and pharmacies.

"Many of these professional and hospital systems already have 90% to 95% of their payments coming in electronically but are looking for a better automated and neutral approach for cash reconciliation, which is our core competency."

To find out more about Big Data Healthcare and how FUSE can provide reconciliation automation value to your organization, learn more about Treasury Management Services from Fifth Third Bank.

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