The case that many startups are making for automating the healthcare revenue cycle is pretty straightforward. Claims follow rules, payers adhere to guidelines, and most of this work is repetitive enough that an algorithm should be able handle it.
Todd Manion, revenue cycle chair at Mayo Clinic, agrees with parts of that argument — but he draws a line at full automation.
Clinical complexity doesn’t compress neatly into the structured data that automated systems require, Manion said this month during an interview at HFMA’s annual conference in National Harbor, Maryland.
For instance, he described a scenario he said he has witnessed many times in his career: a physician uses precise clinical language that doesn’t translate directly into billing codes. A patient might receive every medication and treatment associated with pneumonia, but if the physician documents the condition as a “pulmonary infiltrate” rather than pneumonia, the coder’s hands are tied, Manion explained.
Even though payers can see the evidence, they can’t really act on it, he noted. Only an explicit, signed diagnosis from a clinician, entered in a specific part of the medical record, can actually appear on a claim.
“Sometimes I think there’s a misunderstanding that the entirety of the medical record can be used to, yes, treat the patient — but unless that diagnosis is in a specific place, we can’t apply it to the claim without then going back to the provider and querying,” Manion stated.
He wasn’t dismissive of what AI can do, though. At Mayo, he said the technology is already starting to prove its value in the revenue cycle — especially in areas where workflows are repetitive, such as checking claim statuses, flagging outstanding remits and following up on payments that are sitting longer than contractual timelines allow.
Tasks that once required a staff member to wait on hold with a payer can now be handled automatically, freeing that staff member to work on other tasks that actually need their judgment, Manion noted.
“I don’t need people waiting on hold to figure out where a claim’s status is with the payer. There are simplistic tasks that are repetitive that we’ve used AI to simplify so that we can elevate our people toward more complex patient issues,” he remarked.
In general, Manion said he doesn’t think the revenue cycle done is about chasing claims or closing payment gaps. To him, he goal is to accurately reflect the care that was actually delivered.
“If we can do that and do it accurately, everything else falls into place,” he declared.
Photo: metamorworks, Getty Images
