Strategies for Minimizing Overpayment Risk

Applying technology to optimize VBC performance for health plans and providers

Value-based care (VBC) is experiencing an accuracy crisis, and the implications are severe. The Centers for Medicare and Medicaid Services (CMS) finalized the Risk Adjustment Data Validation (RADV) rule in 2023, which triggered Office of Inspector General (OIG) audits. RADV audits seek to identify potential overpayments by verifying that every diagnosis submitted by payers for risk adjustment aligns with the information in the enrollee’s medical records. If an audit reveals a mere 10% of the records have evidence of overpayment, CMS can extrapolate to the full contract, recover overpayments and impose fines. 

The OIG has implicated several major national insurers in fraud lawsuits, accusing them of obtaining Medicare Advantage overpayments through upcoding. This practice involves using codes to exaggerate patients’ illnesses, leading to higher government reimbursements. In a recent instance, the Cigna Group agreed to a settlement of over $170 million to resolve allegations of submitting incorrect Medicare Advantage patient data.

Heightened scrutiny on risk adjustment, especially on the specific Hierarchical Condition Categories (HCCs) mentioned in recent rulings, has payers on edge. To successfully manage VBC contracts, including assuring the accuracy of risk programs, payers need reliable ways to assimilate and utilize vast amounts of data. Putting the focus on the overall effectiveness of VBC management can mitigate risk as well as improve member outcomes and financial performance. 

Key culprits of overpayment risk

Incorrect coding results in incorrect payments and a higher risk of overpayment. There are multiple causes of incorrect coding. In some cases, people simply make mistakes, such as a provider who is unaware of the clinical documentation required to support a specific diagnosis. In other cases, clinicians and payers may push boundaries – either intentionally or unintentionally. Common culprits include a focus on high-revenue HCCs without proper documentation, and organizations lacking robust data analytics for adequate quality and review processes. Payers can reduce these occurrences by fostering collaboration with providers, aligning coding and documentation processes and making data readily accessible. 

Building a culture of collaboration between payers and providers

Strong collaboration starts with a focus on common ground among payers and providers: First and foremost is member health, followed closely by CMS compliance. 

To improve member health, proactively mitigating risk is critical. First, health plans should understand their members’ health status, population health risks, and how members use provider network services. Next, health plans should identify existing barriers to gaining that 360-degree view. For example, if providers are not documenting and reporting correctly, payers won’t have a complete picture of a member’s status. In addition, historically, everything has been managed retrospectively using claims. Today, instead of focusing on high-value diagnoses or “chasing codes,” payers and providers can collaborate to seek care gap closure, which improves member health and financial performance. 

Proactive collaboration around member health naturally facilitates compliance with CMS regulations. Understanding the nuances of the submission sweep periods makes getting it right the first time more crucial than ever. AI and NLP technologies make that level of reliability possible today. These technologies can aggregate and synthesize clinical and claims data across disparate systems – from electronic health records (EHRs) to health information exchanges (HIEs) and more – for both prospective and retrospective analysis. Honed to cover more ground within member records faster and more accurately, these technologies can identify trends that predict risks, so they can be managed proactively. 

Giving providers accurate, actionable insight at the point of care when they see patients makes it possible to close care gaps immediately. For example, if a patient is being seen for the first time in over a year, the provider can address all the patient’s conditions – from congestive heart failure to back pain – in one visit while complying with VBC best practices. Having this level of insight at the point of care improves member health, increases accuracy and reduces the risk of overpayment. 

Arming providers to operationalize with best practices

Payers can improve overall performance by meeting providers where they are to help them embrace their contribution to successful VBC. Some providers may require more ongoing guidance, while others are confident about fulfilling contract requirements. Although it often seems that health plans and VBC providers are jumping through hoops to meet requirements for government health plans, it’s important to help providers see that these are good plans that are equipped to improve member health. Assisting providers to understand the best practices that operationalize these plans advances successful VBC. 

Here is where payers need to meet providers where they are to improve compliance with best practices. For example, some providers understand VBC concepts and are confident applying data-driven insights for accurate diagnosis and treatment. Others rely too heavily on general diagnosis, such as “unspecified” diagnosis codes, which increases the risk of underpayment. Payers can help providers understand how to use pertinent information at the point of care to improve coding accuracy and reduce risk. On an ongoing basis, payers can analyze providers’ performance and offer education tailored to fill knowledge gaps, improve the use of technology tools, and increase accuracy – all geared toward improving member health under VBC contracts. 

Putting data to work to enable VBC

Recent rulings by CMS reinforce the need for more holistic, proactive care – integral to mitigating risk and achieving success with VBC. And it’s no secret that the healthcare industry is drowning in the data needed to achieve that goal; it’s just spread across siloed systems. AI and NLP technologies are proven to aggregate and synthesize clinical and claims data, removing the silos that currently hinder VBC. This data can be used at the point of care to close care gaps, inform decisions that improve member health, and increase accuracy throughout the process – eliminating current concerns about overpayment risk.