From Passive AI to Agentic: What the Next Evolution of AI Means for Healthcare

For years, AI in healthcare meant dashboards, alerts, and structured recommendations. A clinician would see a notification, weigh the information, and decide what to do, then handle every step that followed. That model delivered real value, but it never reduced the workload. It added information to an already full plate and asked clinicians to act on it.

That's the gap agentic AI is designed to close. The shift isn’t just about what AI can do; it’s about what AI can take off the plate entirely, so clinicians can focus on the work that truly requires their judgment.

What is agentic AI in healthcare?

There are three recognizable stages of AI maturity in healthcare, and understanding where each ends helps clarify why agentic AI is different from what came before.

Stage 1 — Passive AI: Listens and curates what it hears, organizing information without generating new outputs. Clinicians interpret and act. The system organizes information; the human decides and executes.

Ambient AI, systems that listen to clinical conversations and produce documentation, is the most familiar and successful example of passive AI. It became healthcare's gateway to AI adoption because it delivered immediate value without asking clinicians to trust the technology with decisions.

Stage 2 — Generative AI: Generates new outputs based on data—suggesting codes, drafting messages, recommending actions. These systems actively produce content and recommendations, requiring clinician review and correction before use. While this stage automates the production of information, it still leaves manual handoffs and validation steps to healthcare teams to execute.

Stage 3 — Agentic AI: Picks up where generative AI leaves off and executes the task end-to-end—taking coded documentation, checking payer rules, initiating authorizations, and routing claims—with transparency and human oversight opportunities.

Each stage has expanded what's possible, and ambient AI in particular has shown clinicians what's achievable when technology absorbs work instead of adding to it. But each stage has also exposed the gap between what AI can do and how practice workflows were actually designed. Agentic AI is different because it begins to close that gap—not by asking people to adapt to the technology, but by reshaping how the work itself flows.

How does agentic AI differ from passive or generative AI?

Most AI in healthcare tells you what should happen next. Agentic AI takes action by submitting the prior authorization, correcting the billing error, and updating the record, while clinicians stay in control of what it can and can't do.

Consider the clinical encounter from documentation to completed claim. Passive AI listens to the patient visit and captures the note—ambient AI scribing at work. Generative AI then looks at that captured note and suggests the appropriate billing codes based on the documented procedures and diagnoses. Agentic AI goes further: it checks payer eligibility based on those codes, identifies any prior authorization requirements, initiates the authorization request, and prepares the claim—all while keeping the clinician informed of what's happening and maintaining opportunities for review and override.

McKinsey estimates that AI-enabled revenue cycle management could reduce cost to collect by 30% to 60% by eliminating the manual handoffs and rework that slow reimbursement today.

This is what Novare™ by Greenway Health® delivers: native agentic AI orchestration that unifies the entire encounter-to-cash workflow, rather than a series of point solutions loosely connected after the fact.

What changes when AI becomes agentic?

• Work that once took hours happens in minutes. By orchestrating multiple workflow steps in parallel rather than sequentially, agentic AI compresses the entire encounter-to-claim timeline, freeing clinicians from administrative bottlenecks.
• Coding shifts from a manual task to a verification step with greater accuracy. AI handles the first pass; certified coders review, correct if needed, and finalize.
• Prior authorization and claim routing are initiated automatically, based on payer rules the system already knows, reducing the back-and-forth that delays reimbursement and extends approval times. This improves the patient experience by helping remove administrative actions that can delay care delivery.
• Administrative workflows run in parallel with clinical ones, rather than stacking up behind them and creating an end-of-day burden for care teams.

Why human oversight isn't a constraint—it's the point

The goal of agentic AI in healthcare is not to remove clinicians from the loop. It's to ensure they stay in the loop for decisions that require their input, and their cognition is not required for decisions that do not. A physician's judgment is irreplaceable in diagnosis, treatment planning, and patient communication. It is not the best use of that judgment to re-enter the same demographic information that already lives in three other systems.

When agentic AI is designed correctly, human oversight becomes a natural checkpoint, not an obstacle. Clinicians can see what the system did, why it did it, and override it at any point. The AI handles the predictable, structured work. The clinician handles the work that is truly clinical.

That distinction matters especially now.  According to the AMA's 2025 national physician comparison report, 42% of physicians still report burnout symptoms, with administrative burden remaining a leading systemic driver. Reducing that burden is a prerequisite for a sustainable care workforce.

Of course, any agentic system in healthcare must meet regulatory and privacy standards; HIPAA-compliant data handling, full auditability, and transparency into how decisions are made. In Novare, every AI interaction is logged and traceable, so organizations can demonstrate compliance and clinicians can trust what the system does on their behalf.

What this means for organizations evaluating AI today

If you're evaluating AI in the context of your EHR or revenue cycle, the most important question isn't whether a vendor offers AI features. Almost every vendor does. The more important question is: where does the AI live in the workflow, and are the pieces connected?

AI that sits outside the clinical record, accessed through a separate interface, or layered onto an existing system as a bolt-on, creates integration points where information flows can break down, where staff have to switch tools, and where the efficiency gains of automation are partially offset by the friction of connecting disconnected systems.

AI that is native within the platform, built into the workflow rather than appended to it, is a fundamentally different proposition. It can act on the data it has access to, without requiring exports, integrations, or manual re-entry. That architecture is what makes agentic capability practical rather than theoretical.

The shift from passive to agentic AI is not something that happens to a practice. It's something a practice chooses by deciding what kind of infrastructure it wants to build upon. The organizations that make that choice deliberately—evaluating not just current features but the underlying architecture—will be better positioned for the decade ahead than those that simply upgrade the tools they already have.