- Predictive analytics are key to implementing an effective and efficient claim denials management strategy that tackles the right denials at the right time, according to the Vice President of Revenue Cycle at Tennessee’s RCCH Healthcare Partners.
“Without a predictive analytics tool, you're not touching the accounts that need to be touched and especially the ones that you want to touch immediately,” Jane Motes, who oversees the revenue cycle of 16 regional health systems in 12 states, recently told RevCycleIntelligence.com. “Without the tool, you don’t know if the value of that particular account warrants a phone call today, or whatever the action would be to get started on a particular account.”
Targeting the right denials is crucial to recouping revenue. About $262 billion of out the $3 trillion in claims submitted in 2016 were initially claim denials, Change Healthcare reported. The denials put as much as 3.3 percent of net patient revenue at risk, accounting for about $4.9 million per hospital that year.
Providers can get a portion of that revenue back if they have the right claim denials management tools. About 90 percent of claim denials are preventable, the Advisory Board reported.
However, identifying the claim denials that can be overturned in-house proved to be a challenge for RCCH. Without the proper tools, RCCH facilities worked every single denial to boost their revenue even though Motes knew that not all denials are created equal.
“During our monthly conversations about outcomes with our facilities, I would oftentimes hear how we beat our denials to death,” she elaborated. “Working these denials was like a badge of honor that the business office teams wore, when in fact it really was hurting us as an organization.”
The strategy was actually a negative cost to RCCH, Motes reported. Health systems were increasing their costs and decreasing efficiency by targeting low-value denials that the system had little chance of overturning.
The low value of the denials also should not have warranted immediate action in house.
Health systems were going after every claim denial despite its value because RCCH did not have an adequate view of denials across the organization, Motes explained.
The healthcare organization supports over 2,000 affiliated physicians and mid-level providers, as well as more than 14,000 employees. RCCH prides itself on working with communities to build healthcare systems that deliver high-quality, local care.
However, the revenue cycle management differences among health systems managed by RCCH created challenges for understanding and overcoming claim denials across the entire organization, Motes explained.
“Each one of the facilities that we have is on their own system,” she said. “We outsource six of our facilities’ business offices and then the remaining facilities have their own independent business offices. Since they’re on disparate systems. No two facilities have the same system or the same version of a system.”
Without a centralized approach to claim denials management, RCCH did not have an adequate view of how denials impacted their business operations.
“We really didn't have a way to report collectively on denials and a lot of our systems didn't even provide any kind of denial information to us at all,” she stated. “For example, we had no idea about new denials on a monthly basis. It was hard for us to fully get our arms around denials themselves.”
“We really wanted to come up with a plan that would help us to look more like we're one unit rather than the multiple different systems,” she continued.
To get the health systems under one platform, RCCH partnered with Connance, Inc. to implement a predictive analytics solution. Not only would the analytics solution reduce complexity across the organization, but it would also help RCCH identify claims worth the organization’s efforts.
“The analytics showed us how to accelerate resolution on the highest value denials, which was a key driver for us,” Motes highlighted.
The predictive analytics solution identified the highest value claim denials by categorizing denials using two years’ worth of payment information from two pilot facilities. The solution then divided denials into three categories: high value, mid-range value, and low value.
“There are high-value denials, or accounts that through the data analytics show we have a high opportunity in overturning and the value of that account is also more substantial,” she explained. “Then, mid-range denials that we would definitely want to focus on and, finally, the low-value denials. We did not want our teams to focus on the low value denials.”
Daily claim denial scoring allowed revenue cycle management staff to immediately go after high-value claim denials. Using the scores, staff could then work down the high-value denial list and start tackling mid-range denials.
Low-value denials would be sent to an outsourced expert to save the health systems time and money, Motes reported.
The scoring system significantly increased denials management efficiency and brought in revenue owed to the healthcare organization.
“We work accounts immediately because they've already been scored for us. We're targeting those high value accounts first and foremost which is accelerating cash for us,” she said.
The predictive analytics tool is also creating a clearer picture of the claim denials challenges impacting health systems across the organization. With the tool, Motes and her revenue cycle management leaders at each health system are armed with the data necessary to layer denial prevention strategies on top of their payment recoupment strategies.
“We use that information to develop a strategy at our monthly denial prevention meeting that we have at each of our facilities,” she stated. “It provides us with more information than we've ever had before.”
“A lot of our systems didn't really have denial information,” she continued. “Now, we're using that data to really target the areas, departments, payers, and the reasons for the denials. We are getting ownership for those, following up, and having action items around those denials.”
The predictive analytics tool really added accountability to claim denials management, Motes added.
“For example, if authorizations are an issue according to the data, then we know the patient access department is accountable for that,” she explained. “We can then make sure that we have those authorizations for every patient that comes in the door if the authorization is required.”
“The information helps us make departments accountable and help them monitor the trends,” she stressed.
Accountability is crucial to RCCH. Using predictive analytics allowed the healthcare organization to identify claim denials worth their efforts and uncover trends with denials across their organization. Armed with data, RCCH can hold themselves and their facilities accountable for improving and ultimately preventing claim denials.