Claim Denials

3 Ways Data Can Fix Denials

Post by
Justin Nicols

The official definition of a healthcare denial is pretty cut-and-dry:

The refusal of an insurance company to honor a request by an individual (or his or her provider) to pay for healthcare services obtained from a healthcare professional.

In short, a denial is an insurance payer refusing to pay a provider. And as any revenue cycle leader can attest, denials are constant. There is no endpoint; denials only continue to increase. AHA reports that nearly 90% of health systems have seen an uptick in denials over the past three years.

For many reasons (ever-changing reasons), insurance payers deny claims, sending them back to healthcare providers to rework, chase, fight, or lose. In 2021, the average denial rate is 6-13% for hospitals. Increasingly, the rate has been trending toward the high side of the range.

The top drivers for denials are coding errors, utilization, coverage and incomplete information. Most of these are process errors and can be avoided — or caught pre-submission. But regardless of their driver, every denial represents missing revenue and/or delayed cash flow.


50%-65% of denied claims are not reworked. Many denials are just left for dead. Why? It takes time (*money*) and access to detailed data to identify root cause and do an effective rework. In fact, it costs roughly $118 for each claim that is reworked.

The basic math:

  • A 10% denial rate for a $250M health system represents $25M.
  • If only 50% of denials are worked, the health system is, at best, looking at recouping $12.5M of this *earned* revenue.
  • For a health system of this size, with ~80,000 denied claims per year, working 50% of them is a cost of $4.7M — over $262B nationwide each year.

Healthcare providers write off roughly 6% of net revenue each year. This is coupled with razor-thin margins. In 2020, amid COVID-19, the average median operating margin, with CARES Act funding, was 2.7% (without the funding, it was 0.3%). These margins leave no room for increasing denials and increasing write-offs.

The majority of revenue cycle leaders say denials are their biggest revenue cycle challenge. As a result, denials management is a clear priority for health systems and hospitals. Prevention is significant (and is most often in the spotlight), but our quick math is a reminder that strategically managing the endless backlog of denials is equally important.  

The good news is that health systems are sitting on mountains of payments data. This data can be leveraged to both prevent and manage denials. Here are three ways payments data can improve the state of denials for healthcare providers:

1) Better Denial Tracking - Payments data, when organized, can provide a complete view of denial trends. Health systems that unify and normalize their payments data across all revenue cycle vendors and technology platforms can develop a holistic view of denial patterns and their teams’ work efforts.  

2) Root Cause Analysis - Once data is unified and normalized, health systems can drill deep to identify root causes. Detailed analytics can determine where denials originate and why and can be used to track (and benchmark) payer performance.

3) Machine Learning Optimizations - Machine learning, trained on historical payments data, can flag claims pre-submission, generate claim edits, prioritize appeals based on their ROI and make workflows more efficient.

Data holds plenty of answers and is a solid guide for both minimizing denials and improving overturns. For most health systems, the data is ready. The real hurdle is committing the resources to put it into action. As margins remain tight and denials increase, innovative health systems are increasingly turning to analytics and machine learning to improve their denials management.

Learn more about how Sift Healthcare helps healthcare providers harness their payments data and integrate machine learning.

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