In the complex world of clinical laboratory reimbursement, having access to real-time data helps medical labs optimize revenue and potentially keep up with competitors elsewhere in the community.
One of the most significant challenges labs face is the lack of transparency in private payer reimbursement rates. XiFin, a revenue cycle management company in the clinical lab market, is tackling this issue with its Payor Rate Transparency Monitor, a free online tool designed to help laboratories compare in-network reimbursement rates across multiple payers.
This article explores strategies labs can use when evaluating reimbursement rates, the potential benefits of XiFin’s tool, and the role artificial intelligence (AI) plays in shaping revenue strategies.
Challenges of reimbursement transparency
Until the last few years, labs struggled to access meaningful payer reimbursement data. Private insurers historically kept negotiated rates confidential, forcing labs to rely on guesswork or limited benchmarking when negotiating contracts.
That changed with the release in 2022 of the Centers for Medicare & Medicaid Services’ (CMS) Transparency in Coverage rule, which requires insurers to publish in-network rates. However, the sheer volume of this publicly available data—spanning numerous carrier websites and documents—makes it difficult for labs to gather and analyze this information effectively.
XiFin’s Payor Rate Transparency Monitor simplifies reimbursement comparisons by aggregating data from multiple payers into a single platform. True, this data is already publicly available, but finding and analyzing it is time-consuming.
The tool provides the following:
- A centralized repository of in-network reimbursement rates.
- A subset of key Current Procedural Terminology codes to give labs a representative sample of rates.
- Data visualization tools to simplify complex comparisons.
XiFin customers have access to additional data insights through analytics products.
The February update of the tool compares rates from Aetna, Blue Shield of California, Cigna, and UnitedHealthcare (Humana data was not available). As an example, CPT code 88305 for pathological tissue examination showed an incredible span of reimbursement rates, ranging from 1 cent to $977.34 per test. The weighted average among those four carriers was from $89 to $99, helping labs to recognize outlier rates.
“Some of that difference is regional,” says Clarisa Blattner, senior director of revenue and payer optimization at XiFin. “Cost of living [varies] in different types of areas within the United States.”
Strategies for comparing and negotiating reimbursement rates
With access to reimbursement rate data in a practical format, clinical labs can take the following strategic steps:
Benchmark against industry averages
By evaluating their own contracted rates to industry-wide averages, clinical labs can identify reimbursement gaps from payer to payer, such as whether they are paid too little (or too much) for a test.
“It’s really valuable information,” Blattner says. “It benefits the provider to compare their lab’s contracted rates against the industry benchmark.”
Identify regional variations
Reimbursement rates can differ by areas of a state or the country due to variations in cost of living and market competition. Understanding these differences enables labs to negotiate more effectively with insurers.
Labs know the benefits of a test or service they provide in their community, and with reimbursement rate data, laboratory leaders can tie those rates to costs of doing business in an area, Blattner notes.
Strengthen negotiation power
When equipped with detailed payer reimbursement rate information, laboratories can present stronger cases for better reimbursement rates to insurance companies.
“Data can help labs strengthen gaps within their own subset of contracts,” Blattner says. “A lab can say to a payer, ‘Here’s this data that we’re seeing from the transparency rule that CMS had released. We want to negotiate a better rate.’”
Evaluate in-network contracts
Many labs, particularly smaller ones, accept lower rates to secure coveted in-network status with a payer. However, access to comparative data can highlight contracts that need rebalancing during in-network discussions. This approach may be particularly effective for labs that generate high test volume based on their size, offer unique services, or provide help to underserved areas.
“A lot of the smaller labs don’t have the knowledge, expertise, or resources to do market analysis,” Blattner explains. “So, they maybe look at Medicare allowables and use that to gauge what they’re doing [with payer contracts].”
The Payor Rate Transparency Monitor can inform labs about average reimbursement rates as opposed to a less reliable starting point for private payers, such as the Medicare allowable rate. “From there, you can negotiate with third-party commercial payers, whereas with Medicare pricing, you can’t,” she adds.
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Role of AI in reimbursement strategies
Regardless of where a clinical lab gets its payer data from, AI can be a critical ally in analyzing the information and streamlining reimbursement processes.
“AI helps aggregate massive amounts of raw data and transform it into actionable insights,” Blattner explains. For example, AI assists in identifying patterns in reimbursement rates and payer behavior, which allows labs to make data-driven decisions that can increase financial stability.
For its part, XiFin uses AI-driven analytics to provide:
- Predictive insights into reimbursement trends.
- Automation of rate comparisons across multiple payers.
- Identification of potential underpayments and denials.
By integrating AI-powered analysis with the information provided by a tool such as the Payor Rate Transparency Monitor, labs can navigate payer negotiations with greater confidence and clarity, Blattner says.
“It provides expert insight,” she concludes. “Labs get ammunition to build a case for themselves to ask for higher reimbursement. And it helps with understanding the market value of tests and services.”
This content includes text that was generated with the assistance of AI. G2 Intelligence’s interviews and edits are done by humans.