New Customer Success Story: AI-Powered Exception Processing (EP) Capability Reduces Error Correction Time by More Than 40%

Exception processing is often one of the most labor-intensive parts of revenue cycle management (RCM). For healthcare providers managing complex billing across multiple systems and payors, quickly and accurately resolving claim errors is critical to protecting margins. One integrated health system-based laboratory network found a powerful solution in XiFin^® Empower RCM’s embedded, AI-powered Exception Processing (EP) capability—cutting error resolution time by more than 40% and unlocking new team productivity levels.

A Longstanding Partnership, a Collaborative Innovation

As a longtime XiFin customer, this lab network has consistently played a key role in co-developing and piloting enhancements to the Empower RCM solution. The network was the first to test Exception Processing (EP) Workgroups, a capability designed to transform how billing errors are prioritized, assigned, and resolved using embedded machine learning models and advanced workflow automation. This enhancement aims to eliminate manual error assignment processes, improve productivity, and reduce the time needed to resolve claim errors.

Rather than relying on generic task routing or manual oversight, EP Workgroups applies AI models that analyze claim-level data to predict reimbursement probability, identify the most collectible claims, and auto-assign work to the most effective team member for that specific error type. The result: more accurate task routing, faster issue resolution, and optimized team performance.

The Before State: Manual Reports, Spreadsheets, and Workflow Bottlenecks

Before implementing EP Workgroups, the customer’s billing team operated a manual, spreadsheet-based error management process

• Team leads ran business intelligence (BI) reports, sorted claims by reason code, payor, and value, and distributed spreadsheets to staff.
• Team members often had to wait for their assignments, delaying the start of their workday.
• Claims with multiple errors might require review by multiple staff members—wasting time and risking inconsistencies.
• Managers lacked real-time visibility into progress and had limited tools to track productivity or escalate priorities effectively.

This approach was time-consuming, fragmented, and vulnerable to human error—especially when managing high volumes of claims across different departments.

The Shift to Embedded AI: Intelligent Prioritization and Automated Worklists

The new AI-powered approach made error processing smarter, faster, and more consistent. Key AI-driven capabilities include:

• Claim prioritization by payment risk: AI models analyze historical data to estimate likelihood of reimbursement, claim value, and filing deadlines—helping teams focus on what matters most.
• Targeted task assignment: Claims are automatically routed to the team member with the strongest track record for resolving that specific denial type or error category.
• Automated, personalized worklists: Each team member receives a tailored task queue at the start of their shift—no more waiting on spreadsheets.
• Streamlined resolution: Multi-error claims are grouped to minimize duplicate handling, and tasks are clearly outlined to support quicker resolution.

This intelligent automation improves outcomes and frees team leads to focus on quality assurance, productivity tracking, and process improvement instead of daily task distribution.

The Impact: Measurable Gains in Efficiency and Visibility

After fully implementing the EP Workgroups capability within their Empower RCM instance, the laboratory network achieved measurable improvements across the board:

• 40%+ reduction in average time to correct claim errors—thanks to better prioritization and fewer manual steps
• Significant improvements in staff productivity and workflow visibility
• Multi-touch errors and duplicate work were minimized due to better claim routing
• Ability to scale operations by incorporating offshore resources more effectively

The lab network created a highly responsive, transparent, and performance-driven workflow by shifting from manual sorting to embedded AI.

What’s Next? Expanding AI-Driven Workflows

With this foundation in place, the lab network is exploring new use cases for AI-driven EP Workgroups—such as managing hardcopy correspondence (e.g., coordination of benefits requests) and supporting other non–revenue cycle departments that manage exception-based tasks, bringing this intelligence and automation to any process where prioritization and productivity matter.

Want to see the full results and how embedded AI is transforming RCM exception processing?

Read the full success story to dive deeper into this valuable AI-powered capability.