Four revenue cycle leaders at the Becker’s 11th Annual Health IT + Digital Health + RCM Conference are shedding light on how denial rate – once a most trusted scorecard in revenue cycle – is a metric that no longer tells the entire story. Moreover, they said that leaning on this alone could cost hospitals extra money.
“If you’re not pushing for appropriate reimbursement, then, yeah, you can have a great denial rate,” Tanya Sanderson, RN, senior director of denial management at Palo Alto, Calif.-based Stanford Health Care, said when asked which industry-standard KPI she now considers outdated or misleading. “It’s not really telling the full story.”
Debi Lasswell, vice president of service operations at Longtail, an acute utilization company, concurred. She tied the shift to how technology has evolved the economics to fight denials.
Ms. Lasswell said many organizations still run “under the assumption that every denial costs $25 to work,” assuming that certain denials are not worth pursuing. It’s an assumption she said is not necessarily true anymore.
“If we have AI and we have [additional documentation request] denials that we can automatically respond to or we can do things differently, we’re lowering that cost to respond to things,” she said. “It’s not necessarily bad to force a denial for the right reimbursement. If we’re just looking at these rates as the number that we need to get down and that’s going to solve all our revenue problems, that’s not accurate.”
Cindy Otero, senior director of revenue cycle for Naples, Fla.-based NCH Healthcare System, said her team has found that low-dollar denials, including routine ones like venipunctures, should receive the same attention as bigger claims. “It all adds up,” she said.
Jody Hinton, vice president of medical revenue operations at Henderson, Nev.-based PDS Health, argued that no single metric is inherently antiquated or outdated.
“It’s more making sure that you have today’s context and lens,” she said. “Nothing stands alone [or] independent anymore.”
All four panelists conveyed a broader message: the metrics used by revenue cycle teams to define success must evolve just as quickly as AI.
“You can have good [AI] and you can have bad,” Ms. Hinton said. “If you don’t understand your current workflows and what it takes to do something today and you try to bring in AI, it will be worse than where you sit today. You have to fully understand the workflow and understand what you’re trying to bring AI in to do and work at it very iteratively. It’s not a big bang solution.”
