What Real Practice Conversations Reveal About Prior Auth, Denials, Staffing, Interoperability, and AI Readiness
Radiology revenue cycle leaders are under pressure from every direction. Prior authorization continues to slow down scheduling and patient access. Denials are often rooted in upstream documentation, eligibility, medical necessity, and authorization issues. Staffing gaps make it harder to keep up with daily volume, while payer policy changes and modality-specific rules add another layer of complexity.
At the same time, many radiology organizations are trying to modernize workflows across disconnected systems, payer portals, RIS/PACS environments, practice management systems, referral sources, spreadsheets, and reporting tools. The result is a revenue cycle that often depends on manual workarounds, tribal knowledge, and staff effort to bridge gaps that technology and process design should be solving.
In this session, Stuart Newsome will share common themes emerging from recent conversations with radiology practices and enterprise imaging organizations. The discussion will move beyond generic AI buzzwords and focus on the real operational breaking points radiology leaders are trying to solve today: prior authorization backlogs, preventable denials, workflow visibility gaps, staffing constraints, interoperability challenges, payer policy whiplash, and change management barriers.
The session will also explore where automation and AI agents can make a practical difference—not by replacing people, but by helping teams create capacity, standardize workflows, route work more intelligently, identify exceptions sooner, and surface the patterns that lead to revenue leakage.
Attendees will leave with a practical diagnostic framework for evaluating their own revenue cycle operations and prioritizing where to focus first.
Learning Objectives
After attending this session, participants will be able to:
- Identify common operational breaking points in radiology revenue cycle workflows, including prior authorization delays, denial drivers, staffing constraints, interoperability gaps, and workflow visibility challenges.
- Evaluate how these challenges change across different radiology organization sizes and operating models, from smaller practices managing limited staffing to larger organizations standardizing workflows across multiple sites, modalities, and payer rules.
- Apply a practical framework for prioritizing revenue cycle improvements, including where automation, analytics, AI agents, and human expertise can help reduce avoidable work, improve visibility, and support more scalable operations.