Tri-Annual Diagnostic Coverage & Reimbursement Conference
Policy-Aligned Reimbursement Strategies for Diagnostic Leaders
As coverage policies evolve, audit activity intensifies, and payor requirements grow more complex, diagnostic providers need more than incremental improvements—they need a smarter, policy-aligned approach to reimbursement.
At this year’s Tri-Annual Diagnostic Coverage & Reimbursement Conference, XiFin, Inc. will be onsite to connect with executives focused on market access, reimbursement strategy, compliance, and payor relations. As AI becomes a strategic priority across the industry, we’re focused on how AI-driven policy intelligence can support proactive payor strategy, strengthen contract performance, and reduce preventable denials.
For organizations navigating PAMA implications, evolving CMS rules, prior authorization scrutiny, and increasing audit activity, we look forward to exchanging insights on reimbursement strategies grounded in policy insight and real-time payor intelligence—while improving revenue predictability and protecting reimbursement.
Let’s connect in San Diego to discuss your reimbursement priorities, share what you’re seeing in today’s policy environment, and explore practical strategies tailored to your organization.
Featured Session
Transforming Revenue Cycle Management with Agentic AI: A New Model for Laboratory Reimbursement
Session Overview
- Artificial intelligence is reshaping revenue cycle management (RCM), and agentic AI represents a shift from automation to intelligent execution.
- Agentic AI leverages specialized, task-driven agents that interpret coverage policies, assess documentation sufficiency, apply payor-specific logic, and execute multi-step reimbursement workflows with minimal manual intervention.
- For laboratories facing increasing payor scrutiny, policy variability, and audit activity, this model enables real-time risk mitigation and scalable operational consistency.
- The session will integrate a revenue and payor optimization perspective, demonstrating how AI-enabled execution directly impacts denial reduction, contract performance management, and revenue predictability.
Learning Objectives:
- Identify high-impact RCM workflows where agentic AI can materially reduce preventable denials and rework.
- Understand how AI-driven policy intelligence supports proactive payor strategy and contract performance optimization.
- Evaluate practical implementation considerations for deploying agentic AI within laboratory reimbursement operations.
