Rethinking Care Management with Agentic AI: The Smarter Path for Payers

The challenges facing health insurers today are mounting and multifaceted: Delivering the highest quality care cost-effectively to a growing number of members in the face of resource constraints and a rapidly evolving care landscape. 

Traditional systems of care management — most of which are built on rule-based engines, inflexible dashboards, and human-led triage — are under immense strain. Care teams can easily become overwhelmed, missing opportunities to deliver time-sensitive care because of the mountains of administrative work involved.

At the same time, healthcare is growing more complex. Clinician shortages, an aging patient population, more cases of chronic illness, and fragmented health data are making care delivery more complicated. 

There are a number of challenges impacting the effectiveness of the health insurer, and they all point to the same thing: The need for a modernized system of care management that can keep up with the fast-changing times.

That care system is not only possible but it is also being built and deployed today. And underpinning this new system is Agentic Artificial Intelligence.

What Is Agentic AI?

While AI has been around for decades, its newest form, agentic AI, is a goal-oriented technology built not just to support, but to amplify the work of care teams. By interpreting context, applying reason to complex scenarios, and taking action on its own, agentic AI holds the promise of creating proactive, intelligent operations and workflows.

A paradigm shift from traditional artificial intelligence, agentic AI “agents” go beyond offering recommendations or finding information.  They can act autonomously. Software agents can be thought of as virtual teammates, or what some call co-pilots—helpers that understand context, keep track of changing conditions, and then take steps toward improved outcomes.

Agentic AI systems can do a number of things for health plans that AI programs have struggled to do until now, including:

• Understanding a member’s changing health condition by analyzing structured and unstructured data.
• Interpreting clinical, behavioral, and environmental factors that influence member health.
• Setting and prioritizing healthcare goals.
• Initiating actions ( for example, triggering a health intervention, adjusting risk scores, or referring cases to case managers when necessary).

Agentic AI is proactive instead of reactive. It operates with intent to meet clearly defined goals, including many of the most important for health plans, like reducing hospital readmissions, addressing gaps in care, or improving Star Ratings.

A Closer Look at Agentic AI in Member Care Management

Medicare Advantage health plans are responsible for managing millions of high-risk members – those with multiple chronic conditions, recent hospitalizations, or behavioral health challenges. For these insurers, agentic AI functions as a deeply integrated force multiplier.

The AI agent’s work begins with monitoring a wide variety of data streams, including:

• Claims data
• Electronic health records 
• Pharmacy transactions
• Remote patient monitoring
• Social determinants of health 

Using Multiple Context Protocol (MCP) and other frameworks, the AI agent builds a dynamic picture of each member’s health situation. If a diabetic member, for example, misses a prescription refill and shows a spike in glucose via a wearable, the agent detects the pattern, assesses the risk, and can independently:

• Initiate a call or message to check in with a member.
• Flag the case for manager review.
• Recommend changes to care plans.
• Note the reasoning behind its actions for the sake of transparency and compliance.

Care teams have struggled to keep up with this kind of workflow, and previous AI iterations offered limited help. Agentic AI is moving the ball forward.

Tangible Benefits for Health Plans

Beyond obvious benefits to care teams, the broader payer organization also experiences significant gains from deploying agentic AI, These include:

• Operational Efficiency: Overhead costs decrease with faster outreach cycles and fewer missed health interventions.
• Quality Improvements: Better outcomes, which AI enables, can lead to improved HEDIS scores and higher STAR ratings.
• Cost Savings: With reduced ER visits and hospital readmissions, the total cost of care comes down.
• Member Satisfaction: Personalized, proactive engagement can foster trust with member and keep their loyalty.

And there are several reasons why now is the right moment for payers to embrace agentic AI in healthcare:

• Health plans can access vast amounts of member data, much of which is underutilized today.
• A shortage of nurses and case managers is straining health plan operations.
• Large language models (LLMs), multimodal AI, and contextual reasoning engines can take safe, autonomous action on behalf of care teams.
• Policymakers and regulators are increasingly open to digital health innovations.

The Future of Care Is Agentic

Agentic systems are constantly evolving in capability and sophistication. Soon, they are expected to auto-complete complex risk assessments, generate documentation and care summaries for compliance and review, learn from member responses, tailor future interventions accordingly, forecast care needs and proactively recommend upstream solutions before issues arise.

This was scarcely dreamed of a decade ago and goes way beyond simple automation — agentic systems offer intelligent augmentation. 

For health plans, agentic AI promises the proactive management of larger, more complex patient populations with precision, empathy, and efficiency. It reduces the administrative burden on care teams, lowers costs, improves member health outcomes and tailors the right health intervention to the right individual.

The future of care management isn’t just about being smarter — it’s about being agentic.