In a recent Office Hours episode, Ashish Dua, co-founder of Glidian (now part of Infinx), shared how artificial intelligence is radically reshaping how labs manage prior authorizations.

What started as a conversation about automation quickly became a strategic blueprint for labs looking to operate leaner, smarter, and more effectively in today’s evolving payer landscape.

Whether you’re a startup lab navigating payer requirements for novel tests or a high-volume diagnostic powerhouse struggling to keep up with demand, the takeaways from this conversation offer actionable insights for building a modern, tech-enabled revenue engine.

Scaling Lab Access: From Chaos to Clarity

Whether newly launched or operating at scale, labs encounter two distinct—but closely connected—revenue cycle challenges:

  1. Understanding payer-specific clinical requirements for novel or complex tests.
  2. Handling increasing prior authorization volumes without simply adding more FTEs.

Smaller labs often wrestle with navigating the nuances of payer criteria, especially when introducing new tests.

But as volume grows, the primary challenge becomes operational: how to manage more authorizations with fewer resources.

Traditionally, labs had to expand staff as volumes increased. Today, AI and automation can change that equation—turning a process that once capped growth into a strategic advantage.

Three Key Areas Where AI Can Transform Lab Operations

Here are three promising areas where AI is streamlining the prior authorization process for labs:

  1. Pre-Submission Readiness
    AI can assess prior authorization submissions for completeness and payer compliance before they’re sent—flagging missing documentation and reducing preventable denials. This not only saves time, but also reduces costly appeal cycles.
  2. Automating Payer Interactions
    Many payers still rely on manual processes, even when claims are submitted electronically. AI can help labs parse approval letters, extract denial reasons, and track status updates—freeing up teams from low-value administrative tasks.
  3. Supporting Clinical Justification
    Rather than replacing clinical experts, AI can augment them. For example, models can identify where in a patient’s documentation the relevant clinical justification resides, helping staff construct more accurate, defensible cases for coverage.

These capabilities not only speed up the revenue cycle, they elevate the quality of interactions with payers and reduce friction across the board.

The Role of Provider Collaboration

One often-overlooked factor in prior authorization efficiency is the coordination between ordering providers and rendering labs. While payers may place the responsibility on the ordering physician, many are overwhelmed with their own administrative demands—leaving labs in a difficult position.

To improve transparency and speed, leading labs are investing in platforms and partnerships that enable better data-sharing and workflow alignment with providers. This reduces delays, clarifies responsibility, and ultimately ensures patients receive timely access to care.

AI and the Future of Lab RCM Staffing

Another major theme of the conversation was the future of staffing in lab revenue cycle management. Clinical reviewers are among the most expensive team members labs can hire, and many organizations are cautious about adding cost.

Here, AI has the potential to serve as an efficiency multiplier—not a replacement.

Instead of sifting through entire patient records, clinical staff can rely on AI to surface the most relevant information. This shifts their role from data gatherers to decision-makers. Some labs may eventually explore semi- or fully-autonomous models, but the near-term focus is on enhancing human performance.

Operational Priorities for Growing Labs

For labs just starting to offer high-complexity testing, a practical roadmap:

  • Start in-house to understand payer behavior. In the earliest stages (under 50 prior auths per month), learning from real submissions helps shape long-term strategy.
  • Separate clinical from administrative workflows. Define who will answer clinical questions and who handles documentation and submission.
  • Scale with automation, not headcount. Once volumes hit 100–200 prior auths per month, it becomes critical to deploy automation tools to avoid bottlenecks and burnout.

Labs that follow this path are better positioned to grow sustainably—and ensure the patient experience isn’t derailed by operational delays.

Closing Thought: AI as a Partner, Not a Replacement

A common misconception about AI is that it’s designed to eliminate jobs. In reality, the most effective deployments empower teams to work faster, with more clarity, and greater impact.

In the lab setting, that means reducing administrative overhead, accelerating payer responses, and freeing staff to focus on high-value clinical and financial work.

AI isn’t just about automation—it’s about unlocking capacity in the system.

Want to dive deeper?

Watch the full Office Hours episode: Lab Revolution in the Age of AI and Access.