Value-based care has been a central goal of healthcare reform for more than a decade.
The premise is straightforward: reward better outcomes rather than higher volume. Yet despite broad agreement on the goal, value-based care has been difficult to scale.
The challenge has never been philosophical. It has been operational.
Value-based care breaks down at the point of execution
Value-based models demand accurate documentation, proactive patient identification, continuous engagement, and timely insight into risk and utilization. For many organizations, those requirements exceed available resources, staffing, and technology.
In a recent Modern Healthcare Healthcare Insider podcast conversation, Dr. Rob Bessler, CEO of Honest Health, challenged how value-based care is commonly framed, urging leaders to refocus on what the model is actually meant to do.
“When I think about value-based care, what I really mean is getting paid for keeping people healthy — or for better outcomes — versus getting paid to see more patients,” Bessler said.
The difficulty is not agreeing with that goal but executing it consistently across diverse patient populations and care settings.
Manual processes cannot support risk at scale
In risk-bearing models, success depends on early identification of high-need patients, accurate risk adjustment, and proactive intervention. Historically, much of this work has relied on manual chart review, fragmented data, and episodic outreach — approaches that do not scale.
AI changes that dynamic.
By automating chart analysis, surfacing risk indicators, and preparing documentation in advance of visits, AI enables care teams to focus on decision-making rather than data hunting.
Bessler offered a concrete example of how this shift plays out operationally.
“We went from having a coder review 20 or 30 charts in a good day to being able to generate tens of thousands of pre-visit plans in a single day,” he said.
That scale is essential in value-based environments, where small documentation gaps can have outsized financial and clinical consequences.
Proactive care requires continuous insight
Value-based care works best when organizations move from episodic interactions to continuous engagement. That requires timely insight into which patients need attention — and when.
AI enables this by helping teams stratify populations, prioritize outreach, and allocate resources more effectively. Instead of waiting for utilization spikes or hospital admissions, care teams can intervene earlier.
As Bessler noted, value-based success depends on identifying patients who need more frequent touchpoints — “the patients that need to be seen every quarter, not just once a year for a wellness visit.”
This shift supports both improved outcomes and more efficient use of clinical resources.
Accurate documentation is foundational to taking risk
Taking on risk without accurate documentation is unsustainable. Incomplete or outdated records undermine both care coordination and financial performance.
AI supports risk-bearing organizations by improving documentation accuracy and ensuring clinical complexity is reflected appropriately — while still maintaining human oversight.
“If we can solve for making sure the documentation is accurate and the risk you’re taking on that patient is appropriate,” Bessler said, “then clinicians can focus on what is the best care for that patient.”
That balance, automation with accountability, is what makes scale possible without sacrificing clinical integrity.
AI makes value-based care executable, not just aspirational
Seema Verma, executive vice president and general manager of Oracle Health and Life Sciences, reinforced this point during the podcast discussion, noting that value-based care has struggled to scale in part because providers have lacked the necessary tools.
“Most providers on the front lines haven’t had the technology, interoperability, or real-time decision-making tools they need,” Verma said. “That’s why AI has the potential to help value-based care scale in a way it hasn’t before.”
AI does not simplify value-based care by removing complexity — it makes complexity manageable.
Scaling value-based care requires practical enablement
The future of value-based care will not be driven by better intentions or new terminology.
Instead, it will be driven by practical enablement — technology that supports clinicians, strengthens documentation, and allows organizations to manage risk confidently at scale.
When applied thoughtfully, AI helps value-based care function as it was intended: rewarding better outcomes, supporting proactive care, and allowing clinicians to focus on patients rather than process.
This perspective was discussed by Dr. Rob Bessler and Seema Verma on the Modern Healthcare Healthcare Insider podcast. Listen to their full conversation.