Join XiFin at the Executive War College

Healthcare is operating at a structural breaking point. The industry’s core operational and financial processes are now strained by escalating complexity.

This keynote will examine the major systemic pressures undermining the financial stability of hospitals, laboratories, and diagnostic organizations, and why this represents both a threat and an unprecedented opportunity. Among the most pressing forces reshaping the landscape are:

• Widening coverage inconsistencies
• Increasing friction associated with prior authorization and medical necessity
• Growing bad debt
• Contracting and policy variability
• Industry-wide need for greater standardization, transparency, and advocacy.

These forces are eroding margins, consuming staff bandwidth, and constraining organizations’ ability to sustain access to care.

At the same time, a paradigm shift is underway. AI is accelerating change at a pace that is forcing leaders to fundamentally reimagine how they manage revenue, workflows, payor relationships, and access to care. The next era of revenue cycle management will not be defined by adding more staff or incremental process improvement, but by the ability to operationalize intelligence at scale.

AI, in its many forms, is revolutionizing revenue cycle management. Tangible ROI on AI investments are already being captured in areas such as patient, provider, and payor interactions, and information exchanges, even as ongoing R&D explores additional AI applications. This is “scale matching complexity.” The only viable strategy to tackle the complexity is to harness advances such as Agentic AI (agent-driven, autonomous AI applications) to address the clinical, operational, administrative, and financial bottlenecks that have long constrained productivity and profitability.

These advances can have profound implications for leaders. AI adopters are achieving dramatic improvements in payment velocity, reductions in rework and denials, and meaningful gains in staff efficiency, allowing teams to focus on higher-value, patient-impacting work. The presenters will also explore how organizations can operationalize AI, including engaging cross-functional stakeholders to mitigate risk and implementing effective governance frameworks.

In this keynote, Lâle and Kyle will equip executives with a strategic perspective on how these pressures and technologies converge, and how to leverage them as catalysts for growth. Attendees will leave with a clear understanding of the shifting economic landscape, the role of AI as a cross-functional enterprise priority, and actionable insights for building resilient, future-ready organizations.

Artificial intelligence is reshaping revenue cycle management (RCM), and the next era—agentic AI—is redefining what true automation looks like. Rather than single-purpose models or back-end analytics, agentic AI uses specialized, task-driven agents that interpret policies, analyze documentation, act on payor requirements, and execute multi-step workflows with minimal human intervention. This shift is transforming how healthcare providers manage reimbursement risk, scale operations, and respond to payor inconsistency.

Agentic AI uses specialized agents to actively address key factors that inhibit effective RCM, including:

• Policy Interpretation and Prior Authorization: Agents review payor documents, identify missing details, and predict the likelihood of successful submissions—reducing review time from more than 20 minutes to under 30 seconds.
• Appeals Generation: Agents create payor-specific appeals and evidence using policy data and patient records, increasing success rates.
• Document Intelligence: Advanced agents rapidly process payor correspondence, orders, and audit documentation, flagging exceptions for review.
• AI-Orchestrated Workflows: Agents handle order intake, claims, and appeals autonomously, speeding payments and reducing rework.

These AI applications transform scalability in revenue cycle management, reducing manual work by up to 90%, accelerating payments by over 70%, and recovering lost revenue. They position organizations to thrive amid payor inconsistency and rising documentation demands across clinical, pathology, molecular, and hospital-based laboratory environments.

In this session, expert speakers with deep experience in healthcare operations, payor strategy, genetic counseling workflows, and AI-enabled RCM transformation will share real-world lessons learned, operational implications, and an executive-level perspective on how agentic AI is reshaping reimbursement models. Attendees will gain a strategic understanding of how to leverage agentic AI to achieve more predictable and resilient financial performance.

Learning Objectives:

• Understand the distinction between AI and agentic AI—and why agentic AI is uniquely suited for RCM complexity.
• Review real-world agentic AI use cases across policy interpretation, prior authorization, document intelligence, appeals, and automated claims workflows.
• Learn how healthcare providers can materially reduce denials, accelerate reimbursement, and improve payment predictability using AI-enabled decisioning.
• Identify practical steps to prepare teams, processes, and infrastructure for agentic AI adoption.

Clinical, molecular, and pathology laboratories continue to navigate increasingly complex payor behavior, rising denial rates, regulatory uncertainty, and persistent workforce shortages. At the same time, laboratory associations have become critical drivers of policy engagement—bringing expertise, credibility, and scale that individual laboratories often cannot achieve alone.

This fireside chat brings together advocacy leaders from national laboratory associations to explore how aligned cross-association collaboration strengthens the industry’s ability to influence reimbursement, regulatory, and payor policy. Panelists will share real-world examples of how associations elevate issues such as multi-layer laboratory benefit management denials, inconsistent appeals processes, and reimbursement pressure tied to out-of-network policies—challenges that require coordinated escalation and unified messaging.

The discussion will also examine the evolving regulatory landscape following the recently vacated FDA LDT rule. Panelists will address emerging policy priorities, including the RESULTS Act, local coverage determinations and medical policies, patent eligibility considerations, workforce shortages, and how association expertise plays a critical role in shaping effective legislative and regulatory strategies.

This session highlights how collective advocacy, aligned priorities, and strategic collaboration across associations can accelerate policy change, protect patient access, and ensure the long-term sustainability of diagnostic services.

Participants will be able to:

1. Identify the most significant reimbursement, regulatory, and workforce challenges currently impacting laboratories.
2. Understand how coordinated advocacy across laboratory associations strengthens influence with payors, regulators, and legislators.
3. Explain the implications of the blocked FDA LDT rule and how we can move forward under updated CLIA regulations.
4. Evaluate major policy initiatives affecting diagnostics, including the RESULTS Act, payor-driven denials, and patent eligibility concerns.
5. Apply strategies for framing laboratory challenges as broader public policy and patient access issues through aligned association advocacy.

As reimbursement pressure intensifies and payor scrutiny increases, access to reliable, transparent payor rate data has become crucial for providers seeking to protect their margins, strengthen market access, and negotiate from a position of insight. Yet many organizations still lack visibility into how their contracted rates compare across payors and national benchmarks.

This session explores how healthcare providers can leverage the Payor Rate Transparency Monitor (PRTM) from XiFin to translate national contracted rate intelligence into actionable strategy. Attendees will learn how to utilize nationwide in-network reimbursement data from the largest commercial payors to benchmark existing contracts, assess competitive positioning, and identify opportunities to improve financial performance.

Through practical examples and a real-time demonstration, speakers will illustrate how transparency data can inform smarter contracting discussions, support data-driven negotiation strategies, and align market access decisions with broader financial goals—while accounting for variation and benefit design factors that influence net reimbursement.

1. Benchmark Contracted Rates with Confidence

   Use national payor data from the PRTM to evaluate how existing contracted rates compare to market benchmarks and identify areas of underperformance or opportunity.

2. Strengthen Market Access and Financial Strategy

   Understand how payor rate transparency supports broader market access planning, including identifying gaps, inconsistencies, and strategic priorities that impact reimbursement sustainability.

3. Build Data-Driven Negotiation Strategies

   Leverage national rate intelligence to support informed, defensible payor negotiations—while considering real-world variables such as geographic variation, benefit design, co-pays, and deductibles.

4. Apply Transparency Insights Through a Real-World Use Case

   Review a practical example of how providers have used payor transparency data to assess market position, refine contracting strategies, and optimize payor relationships and financial outcomes.

5. See PRTM in Action

   Gain a high-level, real-time demonstration of the PRTM, highlighting how providers can efficiently navigate the tool and extract insights to support strategic decision-making.

The financial success of diagnostic providers increasingly depends on their ability to navigate ever-evolving payor policies. XiFin’s analysis of millions of claims reveals that while denial volumes have stabilized, their complexity has intensified—with “Procedure Code Not Paid Separately” denials up 51% for hospital outreach and clinical labs, 66% for molecular labs, and an extraordinary 181% for pathology groups since 2018.

Drawing from XiFin’s Payor Denial Impact Report, this session will uncover the latest denial and appeal trends shaping diagnostic reimbursement and share practical, data-driven strategies to mitigate revenue risk. Attendees will gain payor-specific insights, understand which denial categories have the greatest financial impact, and explore how automation, AI-enabled workflows, and system configurability are transforming denial management.

The session will also highlight the importance of financial modeling when launching new tests—assessing CPT-level denial trends, payor rate transparency, and propensity-to-pay analytics to ensure sustainable reimbursement. Participants will leave with actionable insights to strengthen claim accuracy, streamline appeals, and improve collections in 2026 and beyond.

Learning Objectives

This year’s session delivers the most data-rich and forward-looking analysis yet, incorporating over five years of data comparison, payor benchmarking, and real-world AI outcomes. After attending, participants will be able to:

• Analyze evolving denial and appeal trends across hospital, molecular, and pathology billing environments to identify key risk areas.
• Identify payor-specific denial drivers using benchmarking data and apply targeted strategies to increase appeal success rates.
• Benchmark organizational appeal performance against national trends to uncover areas for improvement.
• Quantify the financial and ROI impact of an effective appeals management process.
• Leverage embedded AI tools, hidden denial and underpayment insights, and reimbursement readiness analytics to minimize denials, accelerate appeals, and strengthen reimbursement outcomes.

In today’s margin-tight laboratory environment, financial success depends on understanding where value is created—and where it’s lost—across three critical stakeholders: patients, providers, and payors. Each represents both opportunity and risk in the revenue cycle. This session explores how data-driven insights can uncover hidden inefficiencies, highlight performance gaps, and drive strategic decisions that improve overall financial health. Attendees will learn how to apply key financial analytics to evaluate provider referral patterns, patient payment behaviors, and payor performance trends. Through practical examples, participants will leave with a framework for transforming operational data into actionable intelligence that supports stronger margins, better relationships, and a more resilient revenue cycle.

Learning Objectives:

• Evaluate financial performance across the patient, provider, and payor segments to pinpoint sources of revenue leakage.
• Identify underperforming referring providers and develop strategies to increase their contribution to lab profitability.
• Use patient communication and payment analytics to accelerate collections and minimize outstanding balances.
• Analyze payor trends and denial patterns to negotiate more effectively and streamline reimbursement.
• Design and implement a KPI-driven financial scorecard to continuously monitor and improve performance.