“I just don’t have the staff needed to work the mountain of denials sitting in our queue.” This is a common refrain from many revenue cycle managers, and often, they make the choice to write-off a large percentage of denied claims because the appeal window is or has closed. “We hate leaving money on the table, but just can’t keep up with the high volume” is the frequent follow-up comment. These sentiments regarding how to manage denials are echoed by far too many healthcare providers.
Often the criterion for prioritizing which denials to work first is the amount of the claim. While this sounds like a reasonable approach, upon closer examination, it may not be overly effective at saving resource time or avoiding write-offs each year. There simply must be a better way.
The answer may lie in a page taken right out of the playbook of many other industries. The title of that page? The 80/20 Rule. For those unfamiliar, the 80/20 rule states approximately 80% of business will come from 20% of customers. Using this principal, can providers collect 80% of denial recovery by working just 20% of denied claims? The short answer is, why not?!
Predictive analytics is changing the revenue cycle game in terms of managing claims and appealing denials. It can take manual processes out of the picture and replace them with an automated way of identifying which claims can be overturned based on an analysis of denial codes, payer information and history of successful appeals. This is something that just can’t be accomplished by manual efforts – unless of course you can allocate an army to piece together and analyze the necessary data from systems that don’t even talk to each other today.
A major revenue cycle vendor had been relying on basic EDI information to manage denials and assess each denied claim. This significantly limited its insight into which denials would lead to a successful overturn and how to prevent the same denials from reoccurring. Without full visibility, denial issues continued to escalate resulting in growing expenses and delays in reimbursement for its clients.
Predictive analytics on data from 837 and 835 files discovered that 91% of denial value was coming from just 33% of denied claims. The key is knowing which third will yield 91% of denial value. This is where the predictive portion of the analytics came into play. By leveraging healthcare data and identifying high value claim denials with the greatest likelihood of being overturned allowed this vendor to focus its resources on appealing those denials first. This ultimately saved their client $4 million dollars in wasted resource time.
Claims with errors are like cogs in a wheel that bring the revenue cycle to a halt. While many providers have a scrubber to catch coding errors on the front side of the submission, they don’t do anything to detect patterns that actually lead to a denial. This is where analysis of multiple data files can help. If you can know where reoccurring errors are happening, you can take preventative action. The same revenue cycle vendor used insight from the predictive analytics to help its clients avoid approximately 10% of denied claims. Imagine the impact that reducing denials by 10% can have on your reimbursements and resource time.
Time is money, and wasting time chasing denials is a luxury providers simply do not have. Predictive analytics will allow them to focus on preventing denials in the first place and more effectively manage denials when they do happen. This means improved cash flow, faster reimbursement and a healthier bottom line. And who doesn’t want that?
Cathy Boelke is a healthcare executive with 27+ years of experience in revenue cycle, finance and academic practice. She has a demonstrated record of increasing revenue, reducing costs and optimizing the use of technology (Epic) to drive revenue cycle performance in her work as a Revenue Cycle Executive with several large academic and integrated delivery organizations.
Ms. Boelke currently serves as an advisor to Wind River.