RPA vs AI in Revenue Cycle Management: A Buyer's Guide for 2025

Introduction

In the ever-evolving world of healthcare revenue cycle management (RCM), automation is no longer optional - it’s essential. But not all automation is created equal. For over a decade, robotic process automation (RPA) has helped RCM teams mimic human keystrokes and clicks to handle repetitive workflows. In 2025, though, that’s no longer enough.

Enter artificial intelligence (AI).

AI is fundamentally reshaping how healthcare providers manage eligibility, coding, prior auth, denials, and collections. And for buyers, the question is no longer "should we automate?" but *"should we choose RPA or AI?"

This guide breaks down the difference between RPA and AI in RCM, shows you when to use which, and offers a real-world case study to help you make the right investment.

What is RPA in Revenue Cycle Management?

RPA (Robotic Process Automation) is software that mimics user actions:

• Clicking buttons on payer portals
• Copy-pasting data between systems
• Downloading or uploading PDFs
• Sending templated emails or faxes

It’s fast, rule-based, and deterministic.

But RPA is brittle. If a payer changes their portal layout, or a PDF template shifts, the bot fails.

RPA is ideal for:

• Stable, repetitive tasks
• Rules that don’t change often
• Scenarios with low variability

Example:

• Logging into a portal daily to download remittance advice PDFs
• Updating a claim status in the EHR

What is AI in Revenue Cycle Management?

AI goes beyond keystrokes and manual tasks like data entry. It understands and learns.

It uses natural language processing (NLP), computer vision, and machine learning (ML) to:

• Interpret unstructured documents like clinical notes or insurance cards
• Predict payer behavior (e.g., denial likelihood)
• Extract and normalize messy data
• Adapt to changes in forms, templates, and claim logic

AI is probabilistic and intelligent. It improves over time.

AI is ideal for:

• Complex, high-variance workflows
• Extracting insights from documents or EHR notes
• Denial prediction and prevention

Example:

• Auto-filling prior authorization forms based on EHR data
• Detecting errors in claims before submission

Comparison Table: RPA vs. AI in Revenue Cycle

Real-World Case Study: RPA vs AI for Eligibility Verification

Background: A multi-state outpatient network relied on RPA bots to check insurance eligibility daily.

Challenges:

• Payer portals often changed, breaking RPA scripts
• RPA couldn’t handle scanned insurance cards or handwritten forms
• Staff had to intervene frequently, causing delays

Solution: The team switched to Nanonets Health’s AI-powered eligibility verification agent:

• OCR extracted details from any insurance card format
• AI queried payer APIs in real-time
• Errors flagged instantly, with suggested fixes

Results:

• 80% reduction in manual verifications
• 65% drop in coverage-related denials
• Staff reallocated from data entry to patient communication

When to Use RPA vs When to Use AI

Bottom line: Use RPA for stable, rules-based tasks. Use AI when you need flexibility, intelligence, and learning.

How to Future-Proof Your RCM Automation Stack

1. Start with a Hybrid Approach

Many providers succeed by combining RPA and AI:

• RPA handles the handoffs
• AI handles document reading, decision-making, and predictions

2. Pick AI Vendors That Integrate Easily

Modern AI platforms like Nanonets don’t require deep IT work. Look for:

• API-based integration
• EHR compatibility (e.g., with Healthie, athenahealth, Epic)
• Pre-built models for eligibility, auth, coding, and denials

3. Prioritize Use Cases with High ROI

Good starting points:

• Eligibility verification
• Prior authorization
• Denial prediction
• MDS form completion for PDPM billing

4. Monitor and Retrain Periodically

AI models need feedback to improve. Work with vendors who offer:

• Model retraining
• Human-in-the-loop moderation
• Transparent performance dashboards

Frequently Asked Questions

Q1: Isn’t RPA cheaper than AI?

Short-term, yes. But RPA bots break often and require high maintenance. AI costs more upfront but delivers higher long-term ROI.

Q2: Do I need a data team to use AI?

No. Tools like Nanonets are designed for non-technical RCM teams. Setup takes days, not months.

Q3: What happens if the AI makes a mistake?

You can set up moderation rules, thresholds, and fallbacks. The AI can flag uncertain cases for manual review.

Q4: What about compliance and HIPAA?

Reputable AI vendors offer full HIPAA compliance, audit logs, and data encryption.

Q5: Can I use both RPA and AI together?

Absolutely. Many of the most effective RCM workflows combine both: RPA for data movement, AI for intelligence.

Final Thoughts: Choose AI for the Next 10 Years

If your revenue cycle strategy is still built on RPA alone, you’re solving yesterday’s problems.

In 2025, AI offers:

• Better accuracy
• Resilience to change
• Faster collections
• Happier staff

Whether you’re scaling an outpatient clinic or running a multi-state SNF network, the question isn’t "RPA or AI?" - it’s *"Where can I deploy AI today for the most impact?"

Let Nanonets Health show you how.

🚀 Want to see how AI outperforms RPA in your RCM workflows?

Request a live demo and automate eligibility, authorizations, denials, and more.

Tags: Revenue Cycle Management, RPA vs AI, Medical Billing Automation, Denial Prevention, Eligibility Verification, Nanonets Health