The following is a guest article by Robert Laumeyer, Chief Technology Officer at Availity
Healthcare automation has long been sold as a cost-saving means of increasing efficiency, delivering better care, and reducing errors. But when faced with the ambiguous realities of the day-to-day clinical workflow, most of the proposed approaches have failed in the past. That’s because the majority of systems are still based on an explicit programming style: the if X, then Y rule-based logic.
Organizations need a more flexible, contextualized, and autonomously intelligent approach to automation to truly modernize some of the most complex and abrasive manual processes that continue to plague the industry. Agentic AI delivers this blend.
Even though agentic AI may feel new, it’s been around for many decades. It first emerged in the 1960s when computers were small, slow, and had limited capabilities. Early AI pioneers needed a way to break down large, complex problems into manageable pieces.
To do this, they developed systems composed of smaller, autonomous agents that could work both jointly and independently. This was the start of what we now call agentic AI. This autonomy was a key component of the system’s value and making them smaller allowed them to be realized on computers of the day.
A Team-Based Approach for Solving Complex Healthcare Processes
Agentic AI models mirror the way people work in teams: dividing responsibilities, learning in context, and adapting as conditions change.
This team-based approach makes it a good tool for simplifying complex healthcare processes, such as determining medical necessity.
For instance, each task within the prior authorization process can be controlled by a separate agent that’s responsible for:
- Collecting and analyzing a patient’s clinical data
- Comparing clinical data against payer-specific medical policy
- Recommending an auto-approval for straightforward cases
- Forwarding complicated cases for human review
By having each task managed and completed simultaneously by separate AI agents, recommended approvals can be given in seconds.
For payers, providing real-time decisions that are accurate and easy for providers to understand means fewer appeals and improved trust. For providers, it means less administrative burden so they can focus on higher value tasks. But perhaps most importantly, the value gained through intelligent automation flows down to the patient.
When providers are able to get a decision back on a prior authorization request at the point of care, appointments can be scheduled more quickly, which means patients get their care faster. So why aren’t more health plans using agentic AI to streamline prior authorization reviews? Well, there are a few considerations health plans must first work through.
Challenge-Solution Framework & the Importance of Governance and Transparency
Although AI has the potential to fundamentally change healthcare, most organizations are still developing a base of knowledge and tools to do so responsibly. This is why implementing effective agentic AI is about more than the technology.
Start by identifying the bottlenecks you want to fix with AI before choosing or building a model. Second, ensure human oversight remains in the loop, especially when it comes to high stakes clinical and financial decisions. Agentic AI should be used to augment lower-level decisions and empower experts and clinicians to remain in control of critical cases.
Third, good governance matters more than how advanced the AI model is. This means defining what AI can and cannot decide, maintaining a log of decisions, and monitoring AI performance for bias or mistakes to be transparent.
Lastly, organizational readiness is often the largest challenge. Your legal and operations teams may not be prepared to handle AI-specific contracts and workflows, which could cause significant delays.
The good news? For many healthcare organizations, deploying AI follows the same familiar path as past software rollouts.
As the industry modernizes and seeks relief from administrative burden, agentic AI presents not just a tool for automation, but a scalable path to fundamentally elevate how healthcare operates.
About Robert Laumeyer
Robert Laumeyer is the Chief Technology Officer at Availity. He is a seasoned technology leader and inventor who has dedicated his career to bringing innovative technologies to fields needing improvement and has pioneered new technology categories across a spectrum of industries, from embedded software to finance.
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