For years, the measure of a medical coder was throughput. How many charts are processed? How fast? How accurately? That productivity frame is giving way to a more strategic approach at UC Davis Health in Sacramento, Calif.
Tami McMasters Gomez, executive director of mid-revenue cycle at UC Davis Health, oversees a team that spans coding, clinical documentation integrity, health information management, revenue integrity, denials management, coding education, auditing and physician training. The breadth of her accountabilities reflect the future of the revenue cycle workforce.
“Historically, coding has been viewed primarily as a transaction function, productivity based,” Ms. McMasters Gomez said during an episode of the “Becker’s Healthcare Podcast.” “Over the next few years, I see coding professionals evolving into more strategic reviewers, auditors, upscaling them into educators, data analysts, maybe even AI validators.”
The catalyst is a compound pressure: a national shortage of experienced coders that shows no sign of easing, paired with AI adoption moving faster than the industry has seen in decades. UC Davis Health has responded by implementing autonomous coding backed by a governance framework, not as a workforce reduction strategy but as a way to keep pace with organizational growth without a proportional increase in headcount.
“As we grow, our biggest challenge is labor — finding labor and finding talent to augment that growth,” Ms. McMasters Gomez said. “And so we look to other creative ways to do that, and that is to bring in AI.”
The approach is deliberately structured. Autonomous coding handles volume; human expertise handles complexity, quality review and the judgment calls that AI cannot reliably make. But Ms. McMasters Gomez is clear that the human role is not simply residual, but evolving into something that requires new credentials and new skills.
“That future workforce will need to both have that healthcare expert background and maybe a good technology steward would be a skill set we would be looking for,” she said.
In practice, that means upskilling coders into AI second-level review work, credentialing staff in emerging competencies and reorienting the team’s focus away from volume-based productivity toward analytical and compliance functions. The combination of AI and advanced analytics with clinical expertise creates an opportunity to reduce administrative burden, improve documentation quality and generate insights for providers and operational leaders in ways that a purely transactional model never could.
The workforce design question connects directly to how Ms. McMasters Gomez thinks about the value her team delivers to the broader organization. Revenue cycle has historically been siloed from clinical operations; she sees the integration of coding, CDI, compliance and clinical teams as essential to what comes next.
“I think it’s a combination of human expertise with responsible automation, and being able to be more scalable and adaptive, and at the same time being able to improve financial and clinical outcomes,” she said.
The balance is the hard part. Moving too quickly into automation without governance infrastructure creates compliance exposure. Moving too slowly leaves efficiency and revenue on the table. Ms. McMasters Gomez has landed on a strategy where the human stays in the loop and the technology earns its place through measured outcomes and the role of the coder keeps expanding.
“Without engagement, you’re going to lose your workforce,” she said. “So all of those variables that now you can focus on for retention and engagement — and not have to spend your time on the data and the monotonous sort of day-to-day things that AI can really come in and help you do.”
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