Applying AI to RCM: How One Large Lab Further Reduced Error Correction Time by 50%

How Embedded AI* Helps Further Streamline Exception Processing, Improve Productivity, and Accelerate Reimbursement

As revenue cycle management (RCM) becomes more data-intensive and reimbursement pressures increase, healthcare laboratories are re-evaluating how they manage claim exceptions. One high-volume regional laboratory, a longtime XiFin customer, recently partnered with us to implement an AI-enabled workflow strategy that transformed how its exception processing is managed.

This collaboration offers a valuable case study on how even high-performing labs can gain additional operational and financial improvements with embedded AI in RCM workflows—without adding new layers of complexity.

*AI is a broadly used term. As it relates to the AI involved in this capability, we are referring to the use of ocular character recognition (OCR) and natural language processing (NLP) machine learning. It does not involve generative AI or agentic AI.

The Starting Point: A High-Transaction Environment with Opportunities for Optimization

The laboratory processes over five million claims annually and manages more than $500 million in expected billing. Like many complex lab operations, it navigates high-volume, low-dollar claims, diverse client relationships, and a wide range of denial and error scenarios.

The lab retained XiFin for its specialized capabilities in diagnostic billing—an intentional choice that reflects the value of a purpose-built platform. But with the scale and complexity of its operations, the lab saw an opportunity to further streamline how exception workflows were triaged and resolved. Task assignment could be made more efficient, high-risk claims more easily prioritized, and management oversight more strategic.

Rather than pursuing bolt-on automation or custom development—options that risked duplicating data or creating workflow silos—the lab chose to partner with XiFin to embed intelligence directly into the core workflow.

Rethinking Exception Processing with Embedded AI

The laboratory provided critical insight to XiFin as part of the development and testing of the AI-powered exception processing (EP) prioritization and workgroups capability that is embedded directly into the RCM system. Rather than applying machine learning externally or relying on robotic process automation (RPA), this approach focused on integrating intelligence into the core workflow.

Two Capabilities Were Prioritized:

1. Payment Risk Analysis
   Machine learning models evaluated claims based on attributes like procedure type, diagnosis code, payor, and historical adjudication patterns to predict the likelihood of reimbursement. This enabled the system to prioritize claims that were both actionable and likely to yield payment.
2. Work Assignment Optimization
   Based on past performance data, machine learning matched specific error types with the staff members or teams best equipped to resolve them. This automated task routing reduced redundancy and ensured worklists were targeted and manageable.

Key Results and Operational Insights

While AI often generates buzz, the goal of this initiative was to deliver measurable, operational impact—and it did. Key outcomes observed during and after implementation included:

• 50% reduction in time to correct billing errors
• Increase in staff productivity and number of claims resolved per day
• Elimination of duplicate work across queues
• Managers shifted focus from manual triage to strategic coaching
• Improved net collections, though precise figures were not publicly disclosed

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Automation also eliminated the need for daily manual worklist creation. Instead, staff members began their day with a curated, prioritized list of actionable tasks. Over time, performance stabilized at higher efficiency levels, with reduced error turnaround time and improved throughput.

Cultural and Management Impacts

The introduction of machine learning-driven work queues didn’t just change how work was assigned—it changed how it was managed. With more consistent and transparent workflows, team leaders could shift from reactive “firefighting” to proactive performance management.

By embedding reason codes and tracking each resolution attempt, the lab could:

• Identify fixes that didn’t result in payment
• Understand trends in non-cash activity
• Hold teams accountable for outcomes—not just output

These insights also led to better routing of sensitive workflows, like transplant and research billing, ensuring they reached the most knowledgeable staff from the start.

Looking Ahead: Broader AI Applications in RCM

Building on this foundation, the lab and XiFin are exploring additional AI use cases, including:

• Insurance card scanning and payor mapping
• Coverage limitation detection for improved patient estimates
• AI-generated appeal letters to reduce manual burden and improve overturn rates
• Translation of vague payor response codes into actionable insights

These innovations aim to solve persistent RCM challenges—reducing manual touchpoints, accelerating reimbursement, and enhancing payor communication clarity.

Final Thought: Embedding AI for Scalable RCM Success

This success story shows that applying AI in RCM doesn’t require layering on complexity or disrupting workflows. When embedded within the right architecture and powered by structured, high-quality data, AI can support measurable improvements in speed, productivity, and financial performance.

For labs evaluating AI, the takeaway is clear:

Prioritize embedded intelligence that works within your existing systems and choose a partner—like XiFin—that understands the nuances of RCM and how to apply AI where it matters most.

To learn more about applying AI to your lab’s RCM workflows, connect with our team today.

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