AI Agents vs automation in healthcare

The healthcare industry is being transformed by technology, with artificial intelligence driving positive change across patient access, experience, and outcomes. This is just the beginning of the AI revolution, with the global AI in healthcare market projected to grow from $39B in 2025 to $504B by 2032.

However, with new technologies and startups appearing at breakneck speeds, it can be difficult to discern between the various options available. If you’re a healthcare executive planning an AI strategy, you may have more questions than answers. What type of AI technology is the newest and best? Should you be implementing agentic AI or automation in your business? What’s really the difference between the two?

Notable’s engineering experts clarify all of your questions in this article. 

What is automation in healthcare?

Imagine automation as a set of instructions that tells a system what to do, step-by-step. Automation works best for simple, repetitive, and predictable tasks that need to be performed with speed and accuracy. 

"Automation is used in healthcare organizations because it brings consistency," says Ryan Pfeffer, Head of Engineering at Notable. "It's like a robot assistant meticulously following a defined sequence of steps."

Some examples of healthcare tasks that can be automated include: 

• Appointment and PCP outreach: Your system can be automated to send messages reminding patients of upcoming appointments or helping them get assigned to a primary care doctor. 
• Care gap outreach: Automated care gap processes can identify patients who are behind on certain care, like breast cancer screenings, and send automatic reminders to help close those gaps. 

These automations are quick and reliable, but they don’t have decision-making capabilities or the ability to adapt as things change.

What are AI Agents in healthcare?

AI Agents, a form of applied agentic AI, go beyond just following instructions. They use artificial intelligence to understand, learn, and make decisions, similar to how a human would. AI Agents can even read documents, understand language, and hold conversations. 

“While automation is like a robot following a checklist, you can think of AI Agents as digital coworkers that help healthcare staff by performing more dynamic administrative tasks,” adds Pfeffer.

AI Agents can be used for a wide range of multi-faceted functions, including: 

• Front desk workflows: A Front Desk AI Agent automatically handles patient registration, enters information into the EHR, and verifies insurance details before appointments. It works just as a front desk employee would, only faster and around the clock. 
• Revenue cycle management workflows: An RCM AI Agent reviews documentation and codes medical procedures accurately, and an Authorizations AI Agent accelerates authorization approvals and reduces submission times and denials. Both Agents streamline workflows to optimize revenue capture for the organization. 
• Digital assistant workflows: An AI Agent Digital Assistant can hold start-to-finish voice conversations with patients that span different departments, helping patients refill prescriptions, schedule appointments, and pay bills all during the same phone call. It’s like speaking with a real support representative, without being transferred between departments for different tasks. 

In short, automation is like a fast worker following instructions, and AI Agents are smart, digital teammates who can understand, adapt, and solve more complex problems. 

Combining automation and AI for healthcare workflows 

When it comes to implementing AI in your organization, you may think it’s a choice between automation or AI Agents. However, the real power in AI for healthcare organizations comes from combining both automation and agentic AI to streamline tasks and make decisions. Notable uses both to transform healthcare operations for its customers.  

Take the referral process, for example. A workflow for patient referral management aims to reduce referral turnaround time, improve care coordination, and decrease revenue leakage by ensuring the patient doesn’t go somewhere else on their own. To achieve this for our healthcare customers, Notable’s AI Platform uses a combination of AI Agents and automation to carry out the steps: 

1. Referral intake and triage: A Care Coordinator AI Agent uses natural language processing (NLP) and document processing to automatically extract relevant information from referral documents, including the diagnosis, referring provider, and urgency of the order. In this step, automation triages the referral based on clinical protocols and rules built in the AI Platform. 
2. Insurance and eligibility verification: Next, a Front Desk AI Agent verifies the patient’s insurance coverage and benefit eligibility using the EHR and payer integration. Automation flags mismatches or coverage issues to prevent downstream billing issues. 
3. Patient outreach and scheduling: A Contact Center AI Agent then engages the patient via SMS, portal, or phone call to schedule the referral appointment. It uses large language models (LLMs) to personalize responses. Automation offers available time slots, confirms the appointment, and writes the information back to the EHR. 
4. Referral status and follow-up documentation: A Care Coordinator AI Agent steps back in to continuously monitor the referral status and send automated updates to referring providers. It also ensures all documentation is uploaded to the EHR. Automation sends reminders and ensures timely follow-through without manual intervention from human staff. 

This workflow demonstrates how AI Agents collaborate and intelligently automate various touchpoints across departments. It combines real-time decision-making (agentic AI) with structured task execution (automation) to reduce the referral order turnaround time, improve the patient experience, and decrease the manual effort required by staff across departments. 

When Montage Health automated its referral process, it achieved an 83% reduction in referral order turnaround time, from 21 days down to 3.6 days between the referral received and the appointment scheduled. This resulted in a 96.8% patient satisfaction rating and 1,670 FTE hours saved for every 10,000 referrals. 

The future is in the balance

“In today’s fast-paced healthcare technology environment, choosing between automation and AI Agents isn’t an either-or decision—it’s about finding the right balance between the two to meet your organization’s needs,” concludes Pfeffer.

Automation excels at handling repetitive, rule-based tasks with speed and precision, while AI Agents bring intelligence, adaptability, and decision-making to more complex workflows. Together, they form a powerful engine for operational transformation. 

As the healthcare industry continues to embrace AI, the path forward lies in adopting intelligent, integrated solutions that enable your teams to do more with less.