VBC under pressure: Five insights from the front lines

The demands of value-based care have made the day-to-day reality of running a healthcare organization increasingly challenging. At the same time, AI in healthcare has matured far beyond mere novelty. The question is no longer whether AI can help, but where it can make the greatest impact, and how to ensure clinician trust and adoption.

These developments were at the center of a recent fireside chat at the AMGA Fall Council Summit, where Dr. Bhavana Vora, Family Physician and Board Chair at Summit Medical Group sat down with Dana McCalley, our VP of Value-Based Care. Drawing on their experience navigating value-based care both with and without AI, they offered a grounded, unvarnished look at what’s changing, and what those changes mean for healthcare organizations trying to survive and succeed under growing pressure.

Here are five takeaways that stood out.

1. Value-based care is becoming financially unforgiving

Healthcare organizations spent years preparing for the transition from CMS-HCC V24 to V28. That shift alone has forced difficult recalculations around risk, documentation, and revenue.

But earlier this year, the landscape shifted yet again. When CMS announced plans to audit health plans retroactively, the financial risk profile of value-based care changed almost overnight. The possibility of large-scale takebacks is no longer abstract, and the ripple effects are expected to reach well beyond payers.

“Unfortunately, I do believe that will roll down to the provider groups that don't have that niched out on their contracts,” explained Dana McCalley. “So imagine you took the hit in [the transition from] V24 to V28, and now CMS is going back seven years and saying, ‘hey, P.S., by the way, we want X more dollars back from the health plan.’”

The result is a value-based environment with far less margin for error, and far greater consequences when documentation, coding, or risk capture fall short.

2. The annual mechanics of VBC amplify the pressure

Financial strain is only part of the story. The structure of value-based care itself creates an administrative burden that compounds year after year.

Risk adjustment resets annually. HCCs must be re-documented and re-submitted. Preventive screenings and quality measures restart on January 1. For care teams, that means the work never truly carries forward. It restarts.

Dr. Vora captured the lived experience of that reset bluntly. “January first. I hate that day,” she said. “Back in the last EMR we had, everything would turn from red to green. Come January, it's all red [again, as if] nobody's had a mammogram, nobody has cancer, nobody has anything wrong with them. And I have to start all over again.”

In a system that demands continuous performance but evaluates it in annual snapshots, the operational burden feels endless, contributing to major strain across teams.

3. Burnout is a major driver of AI adoption

AI may promise benefits for organizations and patients, but its success ultimately depends on clinicians. If clinicians don’t engage, tools don’t scale. And the strongest motivator for engagement today is not innovation for its own sake, but burnout relief.

Administrative overload has pushed many clinicians to a breaking point. In that context, AI earns attention because it can realistically take work off their plate.

“I was tired of this crazy administrative hamster wheel that you cannot get off no matter how hard you try,” Dana McCalley said, reflecting on her previous administrative role. “But I could pull in artificial intelligence to help us win at improving outcomes, coding to the highest level of specificity, and then I could repurpose staff to do things that they actually wanted to do.”

The implication is clear: AI adoption depends on whether it can meaningfully reduce the friction clinicians feel every day.

4. Ease of use begins with onboarding 

Ease of use is often discussed in vague terms: intuitive interfaces, clean design, smart workflows. But one of the most concrete signals of usability is how long it takes clinicians to learn how to use a tool, and whether that learning curve fits into real clinical schedules.

“You know, we were scheduled for an hour of training [on] how to get used to Navina, how to do it,” Dr. Vora recalled. “It literally took me five minutes… And I showed it to my advanced practitioners and they're like, ‘Yeah, yeah, yeah. Got it, got it.’”

Short onboarding isn’t just convenient. It accelerates adoption, lowers resistance, and allows value to show up quickly. That matters a lot in environments where clinicians are already stretched thin.

5. AI benefits the entire team (not just clinicians)

While much of the conversation naturally revolves around doctors, value-based care is fundamentally a team effort. Nurses, care coordinators, pharmacists, and VBC teams all shoulder administrative work that directly affects outcomes and performance.

When AI streamlines workflows effectively, its impact compounds across roles.

“We had to get something that could help us ease our burden for the physicians and for our staff … whether it's my VBC team, my nurses, my care coordinators, my pharmacists,” Dr. Vora said. She gave one nurse as an example: “She was spending an hour [on chart prep]; now she spends 30 minutes. And for me, my nurse being happy goes a long way.”

Beyond the time saved, that newfound efficiency stabilizes teams, improves morale, and makes sustained performance under value-based contracts more realistic.

The bigger picture

Taken together, these insights reflect a clear shift in the value-based care landscape. Financial risk is rising. Administrative demands are intensifying. Tolerance for inefficiency is evaporating.

In that environment, AI is becoming an operational imperative: a way to make annual requirements manageable, documentation defensible, and care teams more resilient.

As CMS decisions continue to increase pressure on healthcare organizations, adapting will require smarter (not harder) work: embedding clinical AI into daily workflows, reducing friction at scale, and making it possible for clinicians and teams to focus on care rather than cleanup.

And that, increasingly, is what separates value-based care programs that struggle from those that endure.