Rising denial rates, regulatory scrutiny, and workforce shortages have created a pressing need for innovative solutions that can streamline operations, reduce costs, and improve financial outcomes. Agentic artificial intelligence (AI), the next generation of digital workforce technology, offers a smarter, more adaptable, and scalable approach to automation.
Agentic AI consists of intelligent digital agents that understand natural language, interpret intent, and make outcome-based decisions. Unlike traditional automation that follows rigid scripts, these agents learn from real-world data, adapt over time, and operate with increasing autonomy. By combining AI with automation, they enable healthcare organizations to optimize workflows, improve revenue cycle performance, and free staff to focus on high-value, human-centric tasks.
What Makes Agentic AI Different
The term agentic reflects the ability of these agents to act purposefully and independently within defined parameters. They are designed to complement human teams, not replace them, creating a collaborative workforce that blends automation’s speed and scalability with human judgment and empathy. This collaborative approach is particularly valuable in healthcare, where processes often require a balance of precision and human oversight.
What Are the Benefits of Agentic AI in RCM?
Agentic AI delivers measurable advantages across the RCM spectrum, including:
- Enhanced efficiency: Automate high-volume, error-prone manual processes such as claim follow-ups, denial prevention, eligibility verification, and payment reconciliation, reducing turnaround times and accelerating collections. Digital agents can also manage more complex processes end-to-end, such as denial classification, appeal generation, prior authorizations, and patient engagement, with minimal human intervention.
- Improved accuracy: Leverage advanced AI technologies to reduce errors, increase clean claim rates, and ensure compliance with payer rules.
- Scalability: Operate around-the-clock, easing the burden on RCM teams experiencing workforce shortages.
- Cost-effectiveness: Reduce administrative expenses while improving recovery rates, helping maintain strong margins.
- Human collaboration: Function in a hybrid intelligence model that enables people to focus on high-value, complex tasks requiring nuanced judgment, including strategic escalations and patient financial engagement to improve accuracy, speed collections, and reduce administrative burden.
How to Build a Digital Workforce Strategy
Unlike traditional tools such as robotic process automation (RPA), which is rule-bound, agentic AI adapts to changing conditions, learns from outcomes, and works alongside RCM teams to achieve business goals. Key differentiators of digital agents include:
- Autonomy: Exhibit goal-driven behavior that adapts to changing workflows to make decisions to deliver tangible results.
- Advanced capabilities: Incorporate AI-driven technologies like natural language processing (NLP), machine learning (ML), and real-time analytics to handle complex workflows that previously required manual intervention, such as claim follow-ups, eligibility verification, denial prevention, and payment reconciliation, with minimal human involvement.
- Integrated decision-making: Break down silos by replicating human comprehension of workflows, enabling integrated decision-making across processes and learning from outcomes to improve their capabilities over time.
Strategic adoption involves starting with high-volume, low-complexity tasks and gradually expanding to more sophisticated workflows as the technology learns and adapts. Success depends on a culture of exploration, where healthcare organizations embrace pilot projects, refine implementations, and then scale based on measurable outcomes that can transform workflows and boost financial performance.
To explore how agentic AI can revolutionize your RCM workflows, download the white paper, A Healthcare Leader’s Guide to Building a Digital RCM Workforce, and watch for the next article in this series on preparing and implementing Agentic AI.
