By - December 06, 2021
Before the pandemic, many laboratories were already facing financial challenges due to severe downward pricing pressure caused by multi-year price cuts to the Medicare Part B clinical laboratory fee schedule. Entering the COVID-19 era, many labs saw daily volumes dry up and turned to COVID testing to remain in business. According to Jordan Levitt, President and CEO of Waveland Technologies, this shift often meant capital investment, more work for less revenue and reimbursement, increased staff expenses, and a long list of unknown challenges for an unknown duration.
The challenges were exacerbated when the pandemic caused a rapid drop-off in routine and specialty testing. Some laboratories were able to stand up new COVID-19 testing capabilities, but this was an expensive undertaking not feasible for many smaller labs. Even those that were processing COVID tests were impacted by lower reimbursement rates while also shouldering increased administrative burden and expenses related to testing volume and deployment of pop-up labs.
Verifying patient demographic information has been particularly challenging in this scenario. Patients often arrive at testing sites without their health insurance or Medicare card. As Levitt notes, “Labs stepped up to play their role while taking on the downstream administrative burden of processing claims with only insufficient and very poor-quality patient demographic and payer data to work with.” Not surprisingly, this scenario has led to even more challenging billing.
During the public health emergency (PHE), the government Health Resources and Services Administration (HRSA) program will pay for COVID testing if there is no other payer to reimburse the laboratory. If a laboratory submits a claim to HRSA and the patient is found to have applicable insurance, the claim is denied, leaving the lab to pursue reimbursement from the payer. Further complicating the claims process, HRSA’s patient registration portal and the rules behind it have been modified frequently since inception. Billers are challenged to stay abreast of the rapid changes, and as a result, have experienced delays in patient registration, claims submission, and payment.
This is where demographic verification and insurance discovery tools, and process automation can play a critical role by providing accurate patient information and identifying billable insurance or lack thereof (as mandated by HRSA) up front. Doing so accelerates claim processing, enforces compliance, and mitigates the risk of reduced reimbursement.
For its part, Waveland Technologies uses regression analysis to track rule changes and quickly apply learnings from one lab’s situation to many others. “This process helps our customers stay ahead of emerging issues,” says Mr. Levitt. “It reduces the time and effort billers spend on reworking claims and keeps cash flowing. We’re seeing reimbursement captured 80 percent faster for our lab customers compared to labs using traditional methods to bill.”
If HRSA denies a claim, insurance discovery can also be used by the lab after the fact to find the correct payer so that the claim can be resubmitted. Accounts receivable (AR) optimization technology automates the capture of patient demographic and insurance data and dramatically increases the completeness and accuracy of the information. When Waveland Technologies processes patient records, a pathway to either traditional insurance or HRSA reimbursement is created. The result is reconciled registration, claim submission, and compliance that mitigates accidental errors that can lead to inadvertent fraudulent claims under HRSA.
Insurance discovery and demographic verification tools relieve much of the administrative burden by automating the process of capturing, discovering, verifying, and enhancing patient information. Using AR optimization technology, it takes mere seconds to discover and verify patient and payer information — a process that would take several minutes to accomplish manually, assuming you have accurate demographic information in the first place. Leveraging these tools has been found to reduce errors and administrative burden by more than 30 percent. Labs of any size will benefit from streamlined workflows, but smaller labs that are trying to scale and grow have much to gain from improving efficiency through automation.
Unrelated to HRSA, a general best practice is to verify the patient’s unique financial characteristics for potential financial assistance. Doing so enforces consistency and non-discrimination in the discounting process. It also prevents inadvertently running afoul of the False Claims Act.
Improving the patient experience and engagement with self-pay analysis A recent innovation in the billing and revenue cycle management (RCM) process is the idea of treating every claim uniquely—meaning claims can bypass or be added to any step in the workflow that adds value. Using AR optimization tools streamlines accurate identification of each patient’s unique financial characteristics.
Self-pay analysis tools are especially helpful, in combination with accurate patient and payer data, to reveal how likely the patient will be to pay, given the right payment plan. Having this information up front helps labs manage patients’ expectations regarding their portion of the financial responsibility. ZOLL Data Systems found that securing financial buy-in at the beginning of the patient encounter can increase uninsured patient participation in paying what is due. It also doubles participation for insured patients. With more self-pay and high-deductible insurance plan patients than ever walking in the door, self-pay analysis is clearly a best practice with significant revenue enhancement potential.
While no lab of any size can control the volatility in laboratory testing volume—during a pandemic no less—all labs can benefit from additional operational efficiency and reimbursement. The revenue- enhancing best practices articulated here can help ensure the right data is collected for full reimbursement of each claim submitted.
This content is sponsored by ZOLL Data Systems.
ZOLL Data Systems