Key Takeaways:

• Ask exactly what the system automates (insights, decisions, or actions) and where human review fits in.

• Look for explainable AI that shows its reasoning, allows overrides, and improves with feedback.

• A good AI tool should work within your existing systems and processes, have a short learning curve for your team, and not create another silo.

• The right tool takes on the repetitive work and frees up your staff for higher-value tasks and decisions.

If you’re looking for an AI RCM billing and coding solution, it helps to go in with the right questions.

You’ve seen the pitch decks, heard the buzzwords, like “automation,” “efficiency,” and “AI-powered.” But before you get swept up in another demo, you need a framework.

We’ve pulled together eight tough, practical questions—not from a whiteboard exercise, but straight from the conversations we’ve had with healthcare finance leaders, revenue cycle managers, coders, and billing teams across specialties.

Use them to prepare for your next vendor conversation, pressure-test claims, and figure out whether the tool in front of you will actually drive results.

Let’s begin!

1. What Exactly Is the AI Doing and Not Doing?

When evaluating an AI tool for RCM billing and coding, clarity is key. Many vendors use broad terms like “AI-powered” or “automation,” but what does that actually mean in practice?

You should ask:

• Is the tool only surfacing insights (like denial rates or common coding errors)?

• Or is it making real decisions, like assigning CPT/ICD codes, generating claims, or classifying and responding to denials?

Understanding this distinction is critical because it directly affects how much time and labor the tool can actually save.

To dig deeper, ask vendors to walk you through:

• A real claim lifecycle, and which parts are handled by the AI
• The types of outputs the AI generates (and how often humans need to review or override them)
• Whether AI actions are based on static rules or adaptive learning

2. Can We Trust the Outputs Generated by the AI Tool?

A good AI tool shouldn’t just suggest code. It should show its work. 

Look for coding AI agents that explain why a certain CPT or ICD code was assigned, based on clinical documentation or coding guidelines.

That’s where explainable AI helps. You can see the logic behind the decision and override it when something doesn’t look right.

3. Does This Tool Close the Loop or Just Create Another Data Silo?

One of the biggest frustrations with AI RCM technology is having to bounce between dashboards. 

So when you're evaluating an AI tool, it’s fair to ask: Is this going to plug into what we already use—or is it just one more thing my team has to manage?

An AI RCM platform should integrate with your EHR, practice management system, and even your clearinghouse. That way, insights don’t get stuck; they flow across intake, coding, billing, and follow-up.

Example:

A claim denied for a documentation issue should trigger a coding correction and a provider education moment. That’s only possible if data moves freely.

So, when evaluating a vendor, ask:

• How does data get in and out of your system?
• Do we need to train staff on a whole new platform? Or will this show up in our existing tools?
• Can we track a claim’s status from submission to resolution without jumping between tabs?

4. What’s the Implementation Timeline, Training Load, and When Will We Realistically See Outcomes?

When evaluating AI tools in RCM, it’s critical to dig into the rollout plan. When you're evaluating a solution, ask about the full path from kickoff to outcomes.

This includes questions like:

• Will it take weeks or months to get started?

• Can the AI work behind the scenes with minimal lift from staff?

• Can we start using our existing data formats, like spreadsheets from our billing software, as input?

• When will you start seeing improved follow-up, fewer denials, or faster payments?

5. Will This Tool Eliminate Roles or Make Existing Staff More Efficient and Proactive?

A common concern with AI in revenue cycle management is whether it’s meant to replace staff or enhance their efficiency. 

Here’s what to ask:

• Will this AI automate tasks my staff already struggle to keep up with?

• How do other organizations use it—with leaner teams, or with existing staff working differently?

In fact, whenever healthcare leaders ask us these questions, we always highlight the “human-in-the-loop” approach used by CombineHealth’s AI Agents 

Our AI handles the grunt work, such as identifying denials, fetching information from clearinghouses or payer portals, and structuring internal notes. This gives the billing and coding staff enough bandwidth to focus on the parts requiring proactive resolution.

6. How Is This AI Tool Different From My EHR?

Many billing leaders wonder why they’d need an external AI tool when their EHR already includes coding, billing, and reporting features. But in most of our conversations with RCM experts, the billing teams have emphasized one thing: Their EHRs are good at storing data, not acting on it.

Also, most EHRs are designed to store and display structured data, like claims, appointments, and codes already entered into the system. But they don’t:

• Process unstructured data from scanned EOBs, PDFs, or handwritten documentation
• Triage denials, identify patterns, or initiate follow-ups
• Flag claims for resubmission or tracking underpayments

7. What Is their AI Governance Policy for Patient Safety and Regulatory Compliance?

A revenue cycle must operate within tightly regulated environments involving PHI, HIPAA rules, and payer contracts. If an AI tool codes incorrectly, mishandles data, or miscommunicates with payers, the risks go beyond revenue. They also impact compliance, audits, and patient trust.

So, while evaluating an RCM tool, ask these questions:

• Is the system SOC 2 Type II certified or HIPAA-compliant?
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• Does it provide an audit trail of every action the AI takes?
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• Can staff review and override AI-driven actions before submission?

8. What Is Your Coding Accuracy Rate, and How Is It Measured Compared to Human Coders?

Numbers don’t tell the full story. So, the next time you’re given a percentage determining the accuracy of an AI RCM tool, ask the vendor how they’re measuring it.

Ask these questions:

• Was that figure validated through a comparison with certified human coders?
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• Are they testing the tool on actual sample charts, across your visit types and payer mix?
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• What happens when the documentation isn’t clear?

In fact, of all the charts CombineHealth processes, around 15% with ambiguous information are automatically routed for human review. That kind of fallback matters, especially if your team wants to remain confident in the tool’s decisions.

Also, ask whether accuracy is being audited over time. Can your coders give feedback on AI-picked codes? Is the system improving through real-world use, or just frozen at launch-time benchmarks?

Don’t Buy the Hype—Evaluate with Clarity

In every conversation, the same themes surfaced: teams want tools that do real work, and not just surface data. They want faster outcomes without a heavy lift. And they need AI that integrates, explains itself, and works alongside people (not around them).

Curious about how CombineHealth measures up against these questions? Book a demo and put these questions to the test.

FAQs

What is RCM in billing?

RCM (Revenue Cycle Management) is the process healthcare providers use to track patient care from registration to final payment, ensuring accurate billing, coding, and timely reimbursement.

What is an RCM code?

There’s no specific “RCM code.” The term usually refers to CPT, ICD, or HCPCS codes used within the RCM workflow to describe procedures and diagnoses.

What is the RCM billing system?

An RCM billing system manages the full financial cycle (coding, claims submission, insurance follow-up, and patient billing) to help providers get paid faster and more accurately.

What is the best software for medical billing and coding?

CombineHealth stands out for their AI-powered automation. They handle coding (ICD, CPT, HCC), claim follow-ups, and denial management—often working directly from spreadsheets or integrated systems.

What is RCM software?

RCM software automates parts of the healthcare billing cycle, including coding, claims, and denial management, to improve accuracy and cash flow.