Many startups advocate automating the healthcare revenue cycle, arguing that claims adhere to defined rules, payers follow established guidelines, and the repetitive nature of much of this work makes it amenable to algorithmic handling.
Todd Manion, who chairs the revenue cycle at Mayo Clinic, concurs with certain aspects of that viewpoint but draws a firm line against complete automation.
Manion emphasized that clinical complexity does not translate cleanly into the structured data required by automated systems. He voiced this perspective during a recent interview at the HFMA Annual Conference in National Harbor, Maryland.
For example, he recounted a situation he has observed repeatedly: a physician employs precise clinical terminology that does not map directly to billing codes. A patient may receive all treatments associated with pneumonia, yet if the physician documents the condition as a “pulmonary infiltrate” rather than pneumonia, coders are unable to assign the appropriate code.
Although payers can view this evidence, they cannot act on it directly. Only an explicit, signed diagnosis entered in a designated section of the medical record can be incorporated into a claim.
Manion added, “I believe there is a misunderstanding that the entire medical record can be utilized for treatment, but unless a diagnosis appears in a specific location, we cannot apply it to a claim without first contacting the provider for clarification.”
He is not dismissive of AI’s potential. At Mayo, Manion noted that the technology is already demonstrating value in the revenue cycle, particularly for repetitive workflows such as monitoring claim statuses, identifying outstanding remits, and following up on payments that exceed contractual timelines.
Automation of tasks previously performed by staff waiting on hold with payers enables those employees to focus on responsibilities that require human judgment, Manion observed.
He stated, “I do not require staff to remain on hold to determine a claim’s status with a payer. By automating such repetitive tasks with AI, we can elevate our personnel to address more complex patient‑related issues.”
Manion explained that the revenue cycle is not merely about chasing claims or filling payment gaps; his objective is to accurately reflect the care that was delivered.
He concluded, “If we can achieve that accurately, everything else will fall into place.”
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