Why denials management requires a three-tiered approach

Jai Pillai: ©Red Sky Health

In health care, denials can significantly impact a provider's revenue cycle. Each year, more than $4.5 trillion in claims are submitted to insurance carriers in the U.S. alone. Yet despite this, health care providers continue to see a rise in claims being denied. In 2022, 42% of respondents said denials were increasing but that number has grown significantly to 77% in 2024 according to a recent survey. The time it takes to be reimbursed is also increasing, which may be why 84% of health care organizations said they would make reducing denied claims a top priority.

However, effective denials management requires a comprehensive approach that addresses denials at various stages. While often seen as a normal cost of doing business, denials impact not only the provider's revenue cycle but also patient care, which is why the amount of money left on the table is hard to ignore. The Journal of Managed Care & Specialty Pharmacy reported that the burden of denied claims totals around $260 billion annually.

The impact of claim denials goes far beyond affecting the patient experience and revenue cycles by also burdening staff and draining resources which contributes to even more losses. As a result, optimizing financial performance while focusing on a patient's needs and outcomes requires effective denial remediation strategies.

Three critical components to reduce claim denials

To help health care providers understand and plan for improvements, they must first address each of the three key components: denials identification, denials management, and denials remediation. But this is not easy due to the sheer number of factors that play a role in the denial management process. As a result, many health care providers and hospitals rely on innovative technology that incorporates AI and machine learning to identify issues, suggest fixes, and programmatically re-submitting those claims effectively across each of the following areas:

• Denials management: Typically involves analyzing claims after adjudication to assess their payment status. Claims can fall into three main categories: fully paid, fully denied, or partially paid. By tracking the count and total amounts of claims in each category, providers can gain insights into their revenue cycle performance. However, it's important to consider the consistency of measurement, using metrics such as billed amount, allowed amount, and paid amount. These claims can further be sub-grouped or viewed by denial reason types, sorted in descending order of payment value denied or by insurance carriers to understand patterns.
• Denials identification: Proactive prevention denial identification goes beyond post-adjudication analysis. Rather it involves using algorithms and rules to proactively flag claims that are likely to be denied before submission or resubmission. This can be achieved through AI-powered pattern recognition or rule-based evaluations of factors like member eligibility and coverage. While these methods can help identify potential denials, they still require human intervention for review and resolution. And, of course, any time people are brought into the loop, additional cost and error potentials arise.
• Denials remediation: The process of addressing denied claims to maximize payment. Traditional methods generally involve manual review and correction, often by already overworked office staff, which can be time-consuming and error-prone. Applying technology such as machine learning (ML) algorithms offers a unique and automated approach that identifies the root causes of denials and suggests the necessary revisions at a higher level of precision. Using ML not only speeds up the remediation process but also ensures accuracy and consistency. And, much like the human brain, the more the algorithm sees and ‘learns’ from the data, the better and faster it responds.

Denial remediation as a cash flow opportunity

In 2022 alone, hospitals and health systems spent an estimated $19.7 billion trying to overturn denied claims, and more than half of which ($10.6 billion) was wasted arguing over claims that should have been paid at the time of submission according to a report from the group purchasing and consulting organization Premier. This unfortunately also translates to a loss of cash on hand for health providers, and increases in the cost of care for patients. By implementing effective denials remediation strategies, providers can significantly reduce their denial rates, better manage active cash flow and improve their bottom line.

While looking at denials remediation as a cash flow opportunity may seem unlikely at first, it can help an organization to improve income consistency, and become part of their revenue generation activities. With increased claim reimbursement potentials from accurately identifying and addressing the root causes of denials, providers can increase the likelihood of successful resubmissions and appeals, leading to higher reimbursement rates.

Improved coding accuracy gained by analyzing denied claims and identifying patterns can allow providers to improve their coding practices and reduce future denials. And finally, denials may often be caused by billing errors or omissions. By implementing more robust billing processes and training staff to use tools driven by machine learning, providers can minimize the risk of denials and improve their overall revenue cycle management.

Jai Pillai is COO of Red Sky Health, creators of a proprietary AI platform called Daniel that makes recommendations to reduce claims denials. Daniel identifies claims issues, provides guidance to fix them, and programmatically resubmits the claim. Formed by healthcare and technology startup veterans, the Company’s mission is to ensure that healthcare providers are properly paid for their services by making sure that health insurance claims denials are rapidly and comprehensively resubmitted and paid. To learn more, visit them on the web at Red Sky Health or follow them on LinkedIn