The US healthcare system is at a decisive inflection point. Converging regulations from the Centers for Medicare & Medicaid Services (CMS), eroded member & provider trust and rising healthcare costs are making value-based care and contracting models a priority for healthcare executives. Unlike fee-for-service models, where providers bill for each individual service rendered, value-based care ties provider reimbursement to patient health outcomes and the overall cost of care.

For health plans, there has been an execution gap for value-based care models due to complex contracts, inconsistency in AI and data governance and data interoperability challenges. Fragmented workflows and communications for members and providers have also been a challenge.

Advancements in human-centered AI and value-based operations design are changing that. By putting humans at the center, they’re making the experience more seamless for the people doing the work and driving behavior changes that make value-based care more effective.

The time is now

CMS has set a goal of moving 100% of traditional Medicare beneficiaries into an accountable care relationship by 2030, with over 50% projected by the end of the year. For health plans, successfully making this transition is a governance, data and timing problem, which is exactly where AI has genuine value to add.

At the same time, value-based care is, at its core, relationship-based. It succeeds when a care team understands a patient's whole situation and a health plan understands a provider's whole population. Key to making it work is designing the adoption layer that changes member behavior to improve member health outcomes. AI that simply automates analytics and reporting doesn’t do that. Human-centered AI paired with process redesign does.

Where AI genuinely advances value-based care

Providers have moved quickly on AI to combat labor shortages and margin pressure, while health plans are moving toward more deliberate, governed, use-case-specific deployment.

When applied to value-based care, three principles separate human-centered AI from automation:

• Real-time risk and gap closure over retrospective reporting. The highest-value AI use case is surfacing a care gap or rising-risk signal while the clinician or care manager still has the patient, not months later in a scorecard.
• Augmented clinical judgment over algorithmic denial logic. AI should flag documentation gaps or at-risk quality measures to support clinical decisions, not just automate utilization denials in place of them.
• Whole-person context over single-encounter data. Value-based outcomes depend on social context, caregiver support, and cross-setting history. AI's role is assembling that fuller picture, not replacing clinical judgment about it.

Outcomes across the people who make value-based care work

Care coordination is a human skill that AI can support but not replace. A truly human-centered approach must improve the daily experience of the people in the system: the members, caregivers, providers and administrators.

Members experience care that feels coordinated

It’s a familiar experience for patients: you see a new provider and have to fill out the same paperwork you’ve filled out numerous times. Your PCP, specialists, and insurance company rarely see the same picture of your health journey, so every visit starts from zero. This is frustrating friction that drives many people away from the healthcare system, worsening outcomes and impacting margins. With human-centered AI, a member's care plan reflects what their full care team already knows about them, including goals they have stated themselves.

Caregivers become genuine partners in the care team

Family caregivers are often the only constant across a patient's care team but rarely have visibility into the care plan itself. AI-supported care coordination can share risk alerts and next steps directly with caregivers, reducing their burden and improving outcomes. This kind of simple coordination can help prevent avoidable hospitalizations, the single biggest lever in most value-based contracts.

Clinical teams get risk and quality insight while it can still change the visit

Timing is critical in value-based care. AI that surfaces a likely missed diagnosis, an overdue screening, or a documentation gap inside the clinical workflow allows the clinician to act on it during the encounter, which is a critical moment that can change the patient's care and the organization's bottom line.

Internal employees can focus on targeted intervention

Healthcare teams spend an enormous amount of time on administrative and data summarization and consolidation tasks. AI-assisted population stratification offers a solution. It can tell care managers which members need a call this week, not just which ones have an open care gap on record. That lets a finite staff put their time where a conversation can effect change.

What’s next for Health Plans

Between CMS mandates and federal goals, value-based care and contracting is moving forward with or without the operational readiness to support it. The industry is in early days of applying bundled accountability structure beyond primary care and into specialty territory (oncology, cardiology, nephrology, orthopedics), where the costs are higher and the variation in how physicians treat the same condition is much wider. This is a land-grab opportunity for plans that move first. Health plans that integrate behavioral health, social risk, and caregiver context into risk models and care plans have a real cost and quality edge that most competitors still aren't capturing.

While no model is perfect, value-based care does a better job of putting humans at the center, aligning incentives and beginning to repair trust in the system. A member’s lived experience and a health plan’s financial performance are tied to the same thing: better care and a better journey that produces a better result.