Claim Denials

What Dirt Do You Move First?

Post by
Justin Nicols

In all the white papers, case studies, blog posts and training materials about denials, there is an undercurrent that is a little discouraging, but true... expect denials. Denials will occur regardless of if you outsource your RCM, have a consistently high clean claim rate, or put considerable effort into training your teams.

The truth in revenue cycle management is that nobody has a 0% denials rate. Part of this is the game insurance payers play with their ever-changing rules (the persistent “not medically necessary” reason code) and part of it is the ever-increasing complexity of healthcare payments. While aiming for eliminating denials is a powerful strategy, it’s simply not achievable.

The MGMA benchmark for denial rate is 4%, but the average denial rate for healthcare providers is 5-10%. For large hospitals, in any region, the average claim denial rate is over 7%. 76% of healthcare providers say that denials are their biggest revenue cycle challenge. Denials cost health systems roughly 3% of their net revenue. For every $10 million in net patient revenue, that's $300,000 (and this doesn't account for the cost to work denials). As long as your denial rate is not 0%, you are missing revenue.

While prevention is important, recouping this money is essential. Healthcare providers need technology and tools that help them strategically manage and collect on these ongoing denials. There are a myriad of IT tools and resources that help prevent and minimize denials, but few providers have a strategic approach with how they attack denials.

  • If you have a 4,000 claims deficit, which dirt do you move first (what claims should work)?
  • If 20% of denials cost more to work than the cash they yield, how should you manage your team’s time?
  • Which claims should be outsourced, and which ones should your internal teams work?

Most health systems use static rules, based on their best guesses, to drive their denials workflows. They may prioritize denials based on dollar amount or age, but neither necessarily correlate to payment. Or, providers may send everything below a certain dollar threshold to an outsourced RCM, regardless of how easy those accounts may be to collect. These approaches don’t use data to drive the best course of action.

Denials represent earned revenue… Lots of it. The strategies for collecting this revenue should not be an afterthought. This part of the revenue cycle needs a better approach. Sift’s answer is predictive analytics — using historical data to predict each denials’ likelihood of being paid, regardless of dollar amount or posting date. Your team’s worklist focuses on payment likelihood and adjusts each day so you’re always addressing the denials that provide the most value.

Using predictive analytics, you pinpoint which dirt to move first. This kind of data-driven approach that strategically prioritizes denials is powerful, making your team more efficient, accelerating cash flow and capturing more of your earned revenue.

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