Payment integrity: The second pillar of payer data priorities
Accurate data can help plans optimize their payment integrity programs and protect against the latest fraud, waste and abuse schemes.
With healthcare spending growth in the commercial market expected to hit its highest level in more than a decade, along with less federal funding for public programs like Medicaid in FY 2024 and FY 2025, payers in many markets could face their thinnest margins after several years of relative stability.
Implementing new strategies
To help reduce medical costs and protect their missions, leading payers are prioritizing new, data-driven payment integrity strategies.
Payers that can harness technology to enhance their payment integrity efforts will have an edge over other health plans in an increasingly competitive marketplace. Among the most promising technologies to transform payment integrity is artificial intelligence. Payers could realize as much as an 11 percent in medical cost savings as well as as much as 12 percent higher revenue using current artificial intelligence technology, according to McKinsey estimates.
AI is also an essential tool for payers to uncover the latest fraud, waste and abuse schemes, including Medicaid plans, which are especially vulnerable to inappropriate claims submitted by bad actors.
Four key approaches
Here are four proactive strategies for payers to create more comprehensive payment integrity programs so they can rein in inappropriate medical costs, achieve greater value and stay competitive:
Optimize coordination of benefits with the right supplemental data sources. Effective payment integrity programs ensure that claims are paid accurately and appropriately by uncovering errors during prepay processes, such as eligibility determination and coding review as well as during post-pay processes, such as data mining. These functions rely on a multitude of data sources — from payer reimbursement policies to medical records — that can determine the success of a plan’s payment integrity efforts.
For example, fragmented member data can derail a plan’s coordination of benefits, making it difficult to determine primacy for members with multiple forms of coverage. Understanding when and how to leverage the right third-party data sources boosts accuracy, but because many coverage databases are incomplete, out of date or inaccurate, plans may generate inaccurate results and be charged data cleansing costs on top of licensing fees. It’s not uncommon for health plan executives to learn that 40 percent or more of their “commercial other health insurance” findings are on members who aren’t listed on their Council for Affordable Quality Healthcare (CAQH) file, which many plans purchase.
Beyond choosing the right data sources, plans should also establish COB processes for applying order of benefit determination rules, which should reflect the continual changes to members’ lives, including employment, family and health status.
Pinpoint overpaid claims with data mining. While prepay processes like second-pass claim editing can help plans of all sizes avoid improper payments missed during primary claims editing, post-pay processes that utilize data across multiple payers in the commercial, Medicare and Medicaid segments also enable more effective identification. For example, data-mining algorithms enable payers to scour large datasets and uncover overpayments to providers that would otherwise go unchecked, ensuring payment integrity and creating value. By focusing on claims that are most likely to yield a finding, payers can also reduce their administrative costs and avoid irritating providers with unnecessary chart requests.
Leverage AI to flag suspicious behavior. Fraud, waste and abuse (FWA) schemes are constantly evolving, and health plans need a more proactive approach to tackle this frustrating source of unnecessary spending. Combining prepay and post-pay integrity and utilizing different forms of AI — including narrow and generative AI — can provide plans with a holistic view of potential bad actors and help streamline the review process. For example, through narrow AI that is highly task-focused, payers can more easily identify patterns of aberrant behavior, which points the way to new fraudulent schemes. With generative AI, payers can create medical record summaries and other content to make FWA detection more efficient.
One Medicare Advantage plan automated its prepay process and integrated prepay claim editing with fraud prevention to help prevent wasteful spending, saving more than $1 million in less than a year, while reinforcing how AI and automation can help plans deliver more value to the entire healthcare system.
Combine data-driven technology with clinical know-how. Building truly effective machine learning models requires expertise in targeting unnecessary spending. For example, even though some claims seem reasonable to deny based on coding rules, adding clinical knowledge helps ensure the clinical scenario is understood and the billing is not a clerical error, enabling the plan to avoid unnecessary costs.
Payers should carefully vet potential partners in this arena, as technology alone is not enough to create medical cost savings that translate to real value. Even though claim editing software may include self-service tools to support users, staff need clinical experts they can turn to when addressing claim and policy issues. Otherwise, a plan’s in-house clinicians can become inundated with inquiries from claim analysts and provider relations specialists.
Outside medical experts can also use benchmark analytics to help guide payers in their policy decisions and close gaps in their payment integrity programs. For example, experienced partners can help plans narrow their scope for prepay review to focus on the most troubling claims to provide the best results. Selecting a payment integrity vendor that can create benchmarks from peer data is particularly important.
When considering software-only solutions to improve payment integrity, payers should also recognize the savings associated with such tools may be more limited than they realize. Plan leaders who are thinking about purchasing such software should also factor in the costs of overseeing contract reviews, COB employment verification and claim repricing, as well as the cost of additional personnel, software licenses and hardware.
The benefits of benchmarking
For plan leaders who want to gain a true understanding of payment trends, simply relying on their own claims dataset may not be enough. By using benchmarking and analytics from a multitude of payers, they can leverage industry-wide knowledge to optimize their payment integrity processes in this challenging climate.
With carefully vetted processes and partners, plans can also identify errors in payment caused by incorrect data sources. Furthermore, they can embrace a more holistic view of payment integrity encompassing prepay and post-pay for greater efficiency.
By prioritizing this proactive, collaborative approach to payment integrity, plans can bring more value to employers, providers and ultimately their members.
Matthew Hawley is executive vice president of payment integrity operations at Cotiviti.