In today’s healthcare environment, efficiency and precision are no longer optional—they’re essential. Revenue cycle teams are grappling with rising costs, evolving payer requirements, and increasing patient volumes. Automation can alleviate some of the strain, but what happens when workflows demand reasoning, adaptability, or action?
Automation agents and AI agents both play critical roles in healthcare revenue cycle management (RCM). For providers looking to streamline operations, the key is knowing when to use each solution—and how to combine their strengths to achieve the best results.
The role of automation agents in revenue cycle workflows
Automation agents are designed for predictable, repetitive workflows that follow structured rules. They operate with speed and precision, making them the go-to solution for tasks like submitting claims, verifying patient eligibility, or reconciling payments.
For example, one of our clients used automation agents to revamp their insurance verification process. By eliminating manual data entry and automating eligibility checks, they significantly reduced errors and cut processing times in half—improving both accuracy and efficiency.
While automation agents are invaluable for structured workflows, they have limitations. Because they rely on templates and static rules, they struggle with tasks that involve variability, decision-making, or unstructured data. That’s where AI agents take over.
What makes AI Agents different?
AI agents go beyond automation by combining reasoning and action to handle complex, dynamic workflows. Powered by advanced technologies like machine learning (ML), natural language processing (NLP), and generative AI, they can analyze data, adapt to variability, and execute tasks without relying on rigid templates.
For example, our Document Capture AI Agent is transforming referral workflows for some clients. By extracting key details from scanned referral documents and automating data entry into their EMR systems, it eliminates manual effort while improving accuracy. This allows teams to focus on higher-value tasks like patient care and case follow-ups.
AI agents excel in workflows that require:
- Analyzing unstructured data: Such as clinical notes, scanned faxes, or referral documents.
- Dynamic decision-making: Navigating payer-specific rules or resolving claim denials.
- Taking action: Querying databases, updating EMR systems, or initiating prior authorizations.
Unlike automation agents, AI agents don’t rely on templates. They adapt to real-time changes and act as intelligent decision-makers, ensuring workflows keep moving—even in highly variable scenarios.
AI agents as orchestrators of intelligent workflows
One of the most powerful aspects of AI agents is their ability to orchestrate workflows. They act as central decision-makers, dynamically assigning tasks to the most appropriate resource: automation agents for rule-based processes or human agents for nuanced decisions.
For example, while automation agents might process high volumes of claim submissions, AI agents can analyze claim denials to identify trends and recommend resolutions. In situations requiring empathy or specialized expertise, such as patient outreach or appeals, human agents take over—completing the ecosystem of intelligence, automation, and human touch.
By seamlessly coordinating between technology and human agents, AI agents ensure every task is routed to the right resource. This creates a hybrid workflow that maximizes efficiency, scalability, and accuracy across the revenue cycle.
Choosing the right agent type for your RCM workflow
When deciding between AI agents and automation agents, it’s important to evaluate the complexity of your workflows:
- Automation agents are ideal for repetitive, structured processes like eligibility verification or claims submission.
- AI agents thrive in dynamic, decision-intensive workflows where adaptability and reasoning are critical, such as prior authorization or denial management.
The best outcomes are achieved by combining both technologies in a unified system. For example, our Revenue Cycle Agent Platform integrates AI agents, automation agents, and human agents to create a seamless workflow that meets the unique needs of healthcare providers.
Looking to add digital agents to your team
At Infinx, we’ve seen firsthand how the right combination of AI, automation, and human expertise can transform revenue cycle workflows. From automating referral document processing with our Document Capture AI Agent to improving prior authorization efficiency with AI-driven reasoning in the prior authorization module of Patient Access Plus, our solutions are designed to address the specific challenges of healthcare RCM.
If you’re ready to streamline your revenue cycle with AI and automation agents, schedule a demo today and discover the future of healthcare workflows.