8 Agentic AI Use Cases in Healthcare and Their ROI

In 2026, 43% of surveyed provider organizations were testing or piloting agentic AI.

This shows that health systems are starting to explore where agentic AI can take meaningful work off their teams across clinical, revenue cycle, and administrative workflows.

If you’re considering agentic AI for your health system, the question is where it can make a measurable difference first.

This article walks through eight agentic AI use cases in healthcare, the evidence behind them, and the metrics you can use to measure the return.

TL;DR: 8 Agentic AI Use Cases in Healthcare

Agentic AI can take on work across clinical, administrative, and revenue cycle workflows.

Here’s a quick view of the eight areas where it can have a measurable impact:

• Prior authorization and eligibility verification

• Autonomous medical coding and charge capture

• Denials management and appeal generation

• Clinical documentation and note generation

• Patient intake and appointment scheduling

• Care coordination and post-discharge follow-up

• Payment integrity and fraud detection

• Supply chain and inventory forecasting

How Agentic AI Goes Beyond Generative AI in Healthcare

Generative AI creates an output, such as drafting a clinical note or summarizing an encounter. An AI agent can additionally take action toward a specific goal, such as checking eligibility.

As you explore these technologies, you’ll also benefit from understanding the difference between agentic AI and AI agents. Basically, an agent handles a defined task, while an agentic system can coordinate multiple agents and steps across a workflow.

When comparing agentic AI vs. GenAI, the key difference is execution: GenAI creates content, while agentic AI can use that information to determine and carry out what happens next.

Let’s understand this better with this table:

What Makes a Healthcare Workflow Ready for Agentic AI

Before bringing agentic AI into a workflow, check whether the process is structured enough for an agent to take action safely and consistently.

Strong candidates for agentic AI usually have five things in common:

• High volume: If your team handles a lot of repetitive work, such as eligibility checks or routine coding, agentic AI has more opportunities to reduce the time spent on those tasks.

• Clear rules: A workflow is a stronger fit for adopting agentic AI when there’s a clear process for what happens next and when a case needs to go back to your team. That gives you a way to keep clinicians, coders, or other staff involved when a case needs review or judgment.

• Measurable outcomes: The workflow should have results you can track, such as turnaround time, cost, throughput, or revenue captured, so you can see whether agentic AI is actually improving it.

• Reliable system access: The workflow is better suited to agentic AI when the information needed to complete it is available through systems the agent can securely access, such as your EHR, payer portal, or practice management system.

If a workflow misses two or more of these, agentic AI may be better suited to a later phase.

Automating a process with unclear rules, inaccessible information, or no clear way to handle exceptions can simply shift the bottleneck elsewhere.

8 Agentic AI Use Cases in Healthcare

The best use cases for agentic AI in healthcare tend to be those workflows where your teams are already spending significant time moving information, checking requirements, and deciding what happens next.

The examples below span revenue cycle, clinical, patient-facing, and operational work, with a look at what an agent can take on and where you can measure the impact.

Here are 8 common use cases of agentic AI in healthcare:

1. Prior Authorization and Eligibility Verification

Prior authorization can keep your staff moving between clinical documentation and payer portals just to get one request through.

An AI agent can take care of the routine work along the way, from checking eligibility and payer requirements to submitting the authorization and following its status.

If the payer comes back with a routine request for more information, the agent can check what’s missing, find the relevant information in the patient record, and respond to routine requests without sending the case back to your staff. Your team can step in when the request needs additional review or a decision the agent isn’t set up to make.

2. Autonomous Medical Coding and Charge Capture

Coding teams work through large volumes of clinical notes and encounter data before assigning the right codes. With autonomous AI coding, the technology can interpret that documentation and assign codes when the case meets your organization’s confidence threshold.

Your coding professionals still handle exceptions, quality review, audits, and cases that require judgment. This means routine cases can move through autonomously, while your coders can focus their time on the cases that need their expertise.

At DeliverHealth, we help healthcare organizations connect clinical documentation, coding, and revenue cycle workflows so fewer tasks get held up between steps. For your coding team, InstaCode can automatically code encounters that meet your organization’s confidence thresholds. Cases that fall outside those thresholds go to your coders for review, and the built-in audit system records the AI output, coder changes, reasoning, and later auditor edits.

See how we can help your coding team manage growing volumes without adding more manual work. Book an InstaCode demo today.

3. Denials Management and Appeal Generation

When denials pile up, your team has to figure out what went wrong, find the supporting documentation, prepare an appeal, and track what happens after submission. Some denials can also point back to mid-revenue cycle challenges, such as documentation or coding issues.

Agentic AI can group denials by reason or payer to help surface recurring problems. It can also pull together the information needed for an appeal, prepare a draft for review, and follow the case after submission.

4. Clinical Documentation and Note Generation

Clinical documentation can leave your clinicians finishing notes between appointments or catching up after the workday. With an ambient AI scribe, the conversation can be captured during the visit and turned into a structured clinical note. Your clinicians can also use dictation when that works better for them.

Once the note is complete, coding suggestions can be surfaced from the documentation and feed downstream coding and revenue cycle workflows.

That connection is central to DeliverHealth’s approach. Our InstaNote supports both ambient and dictation workflows, using speech recognition and generative AI to create structured notes that follow your organization’s preferred formats and templates. From there, InstaCode can use the completed documentation to support AI-assisted and autonomous coding, creating a more connected path from the patient encounter through coding.

Our solutions support 60,000+ healthcare providers, and InstaNote has reduced documentation time by up to 75% while delivering $50K+ in annual savings per physician. You can see how InstaNote can give you more time back.

5. Patient Intake and Appointment Scheduling

Before a visit, your staff may need to collect registration details, confirm insurance information, and go back and forth with patients to find an appointment time. Cancellations and rescheduling add more work.

An AI agent can collect the information you need, check available insurance information, schedule or reschedule appointments, and bring your team in when something needs attention.

6. Care Coordination and Post-Discharge Follow-Up

After discharge, your care team may be tracking follow-up appointments, making calls, sending reminders, and checking whether patients have completed the next steps in their care. When patients don’t respond, or something is still pending, your staff has to follow up again and decide what needs attention.

An AI agent can take some of that routine coordination off your team by keeping up with follow-up appointments, reminders, and other next steps after discharge. If a patient hasn’t responded or something hasn’t been completed, the agent can follow up again. And when something needs clinical review, your care team can step in.

7. Payment Integrity and Fraud Detection

With a high volume of claims moving through your revenue cycle, some unusual billing patterns or payment issues can be difficult for your team to catch consistently.

Agentic AI can review claims and payment data for anomalies, such as unbundling, duplicate billing, or other patterns that may need a closer look. It can then flag those cases for your revenue integrity or billing team to review before they lead to payment issues, denials, or compliance concerns.

8. Supply Chain and Inventory Forecasting

Your supply chain teams may be spending a lot of time checking inventory across locations, reviewing past usage, and deciding when and how much to reorder. When that information isn’t current, you can end up with shortages in one location and excess stock in another.

Agentic AI can keep an eye on inventory levels and how quickly supplies are being used, so your team has a better sense of what will be needed and when. If one facility is running low, it can check whether another location has stock available before you place a new order.

And when supplies do need to be reordered, the agent can start that process within the purchasing rules and approvals you already have in place.

How to Measure ROI on Agentic AI in Healthcare

To know whether agentic AI is actually improving a workflow, you need something to compare the results against. Start with a baseline for each workflow, including how much time it takes, what it costs, and the results you’re seeing today. You can then track how those numbers change as agentic AI takes on more of the work.

The metric will depend on the workflow you automate.

Here’s what to track for each of the use cases covered above:

You’ll also need to see how much work the agent is actually taking off your team and what that work costs.

Touchless rate shows how much work is completed without staff intervention, while cost per transaction shows what that automation is actually costing you.

Don’t measure accuracy on its own either. Track downstream corrections, denials, and other rework to make sure the agent isn’t simply creating more work later.

Governance and Oversight Requirements for Healthcare AI Agents

When an AI agent works inside your EHR or other healthcare systems, you need clear boundaries around what it can access, what it can do, and when a person needs to step in.

Your governance framework should cover:

• HIPAA and access controls: Give the agent access only to the patient information and systems it needs for the work it’s doing. If ePHI is involved, the same HIPAA safeguards apply.

• Audit trails and model versioning: You should be able to go back and see what the agent did, what information it worked from, and which model version was used at the time.

• Confidence thresholds: Some work may be clear enough to move forward without a person checking it first. Set a threshold that determines when that can happen and when someone needs to review the output.

• Escalation rules: Be specific about what comes back to your team. A low-confidence code might go to a coder, for example, while a clinical question would need the appropriate clinician.

• Performance checks: Once the AI agent is in use, check its work regularly to see where errors or exceptions are showing up and whether anything needs to be adjusted.

Your job is to give the agent enough room to handle routine work while keeping your team involved wherever review, judgment, or accountability is needed.

How to Sequence Your Agentic AI Rollout for Fastest Payback

For an early rollout, look for high-volume administrative work that takes up a lot of your team’s time. Choose a workflow with clear rules and results you can measure, then use what you learn there to plan where agentic AI goes next.

A practical rollout can look like this:

1. Start with one workflow: Identify all the routine steps that an agent can handle, the systems it needs to access, and where in particular your team needs to review or make a decision.

2. Prove the results: Measure the time, cost, touchless rate, and rework before deciding whether the workflow is ready to scale.

3. Expand from there: Once the first workflow is working reliably, you can bring the same approach into other areas where the systems, data, and oversight are ready.

Running unrelated pilots across several departments can be difficult to coordinate. Different teams may work toward different goals, use different measures of success, and compete for the same IT resources.

Keep one operational owner accountable for the rollout, with Health Information Management (HIM) and Information Technology (IT) involved as it expands. That gives you a consistent way to decide on workflow, data access, performance, and governance as you introduce more agents.

Frequently Asked Questions (FAQs)

Here are the questions health system leaders ask most often when they start scoping agentic AI work:

What Does an Agentic AI Deployment Cost a Mid-Sized Health System?

There isn’t a standard price for an agentic AI deployment.

Cost depends on the workflow, transaction volume, pricing model, systems the agent needs to work with, and the amount of implementation support required. Integration can add to the effort, although DeliverHealth’s agentic systems can work across existing platforms without a traditional API integration.

Do AI Agents Need Direct EHR Integration to Work?

Not necessarily. It depends on how the agent is designed and the systems it needs to work with.

Some agents can interact with EHRs, payer portals, and other systems through the same user interfaces your staff already use, rather than relying on a direct API integration. They still need secure access to the information required for the task and the appropriate permissions to take action in those systems.

Can Agentic AI Support Payer Workflows and Provider Workflows?

Yes. Providers can use agents for work such as eligibility, prior authorization, coding, and claim preparation, while payers can use them to review claims, payment rules, and unusual billing activity.

The two sides often meet around workflows such as claims and prior authorization, where information moves between the provider and payer.

Does Agentic AI Replace Medical Coders and HIM Staff?

No. Agentic AI can handle more of the repetitive work, while coders and HIM professionals remain involved in exceptions, audits, quality review, and cases that require judgment.

In autonomous coding, for example, lower-confidence cases can be routed to coding professionals rather than moving forward automatically.

Which Team Should Own an Agentic AI Deployment?

Give the workflow a clear operational owner who is accountable for results. IT should own the technical environment and system access, while compliance, privacy, and security teams review the controls and risks.

All of these teams need to be involved, but having one accountable operational owner keeps decisions from getting stuck between departments.

Conclusion

Agentic AI has the most immediate financial value when it takes repetitive, high-volume work out of workflows such as prior authorization, coding, and denials. That can mean fewer manual touches, faster turnaround, and more capacity for work that still needs your team’s expertise.

But those gains depend on the workflow you choose. Clear rules, reliable system access, and a measurable baseline give you a much better foundation for proving ROI than trying to automate as many use cases as possible.

DeliverHealth connects clinical documentation and autonomous coding in one workflow. With InstaNote and InstaCode, documentation captured during the encounter can move directly into coding, while confidence-based routing and built-in audit controls keep your coding team involved where review is needed.

See how DeliverHealth connects documentation and coding.