VOL. 4, ISSUE 3 | JUNE 2025
Precision in Every Price: How XiFin® Empower RCM’s Pricing Logic Drives Accurate Billing and Faster Reimbursement Continued from page 4 ■ Expect Pricing: Based on expected reimburse- ment, it helps align pricing with contract expectations. ■ Capitation and Per Diem: Applies when billing via fixed rates. ■ Fee Schedules: Default, client-specific, or payor- linked lists of billable prices. ■ Travel-Based Pricing: Includes mileage-based or flat travel fees. Pricing variants such as DISC (Discount) , FLAT, MIN (Minimum) , and TRADE (Trade Discount) further refine pricing based on client contracts or patient scenarios. ■ Special Pricing Tables: Client-specific pricing for client and non-client type fees, mainly test code based. While Empower RCM pricing logic may operate behind the scenes, its impact is front and center in your organization’s ability to get paid accurately and on time. With Empower RCM’s adaptive pricing logic, you get a system designed to support your financial goals and operational efficiency—ensuring every claim starts with the right price.
Unlocking Faster Payments with XiFin® Empower RCM’s Embedded AI for A-321 Claim Status Codes At XiFin, innovation is at the heart of driving better revenue cycle management (RCM) outcomes. Our latest embedded AI capability within Empower RCM is transforming how healthcare providers manage one of the most challenging payor responses: the generic “A-321” claim status code. The A-321 status code is notoriously vague—payors use it to indicate “missing or invalid information,” but without specific reasons. This lack of clarity forces manual review teams to sift through
countless ambiguous claims to figure out what’s wrong, delaying reimbursements and increasing administrative costs. How Empower RCM’s Embedded AI Resolves A-321 Ambiguity Our innovative Natural Language Processing (NLP)
specific issues—like subscriber ID mismatches or missing diagnosis codes—and automatically updates the claim status with a high-confidence prediction, enabling faster and more targeted resolution.
model analyzes unstructured payor messages attached to these “A-321” codes, intelligently translating them into precise, actionable reason codes. This AI-driven logic categorizes claims by
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