Revenue Cycle Optimized

Determining whether documentation truly supports coverage is one of the most complex and costly challenges in revenue cycle today. In this episode, Navaneeth Nair and Yagna Velu discuss the early vision for using AI to strengthen medical necessity review, reduce avoidable denials, and improve decision-making before claims move forward.

Visibility into unpaid claims and time to pay remains a major struggle for revenue cycle leaders. This segment focuses on how AI should support prioritization and clarity without layering on more tools, dashboards, or operational complexity. 

This episode was recorded live from Infinx’s GTM Growth Summit in Denver. Navaneeth Nair, Chief Product Officer, joins us to discuss a new AI-driven approach to medical necessity review. We’ll explore how provider teams can catch coverage gaps earlier, reduce manual policy lookups, and route exception cases to the right human experts before delays and denials take hold. Brought to you by www.infin...

In this episode of Revenue Cycle Optimized , we explore how a Charge Capture AI Agent inside the Document Capture Plus platform helps turn unstructured healthcare documents into cleaner, more actionable billing data. Product leaders Neelam Yadav and Jainil Pariya, along with Jamie Campagna from MedReceivables Advisors, discuss how AI, business rules, duplicate detection, and human review work toge...

E&M coding performance rises or falls on ten predictable breakpoints that drive payment, denials, rework, audits, and compliance risk. Join our panel for a practical discussion on what fixes those breakdowns and how AI supports scalable oversight through pattern detection, documentation review, exception based workflows, and autonomous E&M coding with human specialist exception handling. B...

Pharmacy prior authorization is far more complex than most teams realize, especially across long-term care, specialty, compounding, and infusion environments. In this episode, Derek Taylor explains how those workflows really function, where the operational friction lives, and why getting the process right is critical to faster medication access and cleaner coordination between pharmacies, payers,...

Eligibility errors, missing documentation, and prior authorization gaps may seem small in the moment, but they quietly erode cash flow, inflate AR, and drive avoidable denials. In this episode, we break down why front-end breakdowns still matter most and how AI can either strengthen your foundation or expose the cracks in it.

In this episode, Jonathan Aguiar, Senior Solutions Engineer, walks through prior authorization verification and demonstrates how the Follow Up AI agent works within the workflow. You will see how cases move through entry and validation, triage, payer review, pending initiation outreach, exception handling, and final status. Learn how automation and human expertise work together to deliver real tim...

E&M coding can look routine, but it quickly becomes a judgment-heavy exercise driven by documentation quality, medical necessity, payer policy, and audit risk. Julie Graham and Bo Bowman explain where autonomous coding and AI agents thrive in repeatable workflows, and why complex E&M decisions still need expert human review and provider education to stay compliant and fully reimbursed.

Denials are often blamed on payers or staffing shortages, but the true root causes usually begin much earlier in the workflow. This segment explores how diagnosing denials correctly allows AI to move from reactive appeals to proactive prevention and measurable financial improvement.

In this episode, Natalia Arzeno-Gonzalez, Chief Data Scientist at Infinx, helps revenue cycle leaders cut through the noise by setting realistic expectations for AI in RCM. The discussion explores what AI does well today, where it falls short, and why data readiness, workflow maturity, and integration matter more than buzzwords. Brought to you by www.infinx.com

Even high-performing hospitals leave revenue on the table due to complexity, outdated charging methodologies, and process blind spots. In this episode, Anthony Amaya explains how disciplined charge capture optimization can deliver a measurable one to four percent net revenue lift without adding volume or increasing costs.

As AI becomes embedded into revenue cycle workflows, accountability can no longer be treated as a black box owned by technology. This episode explores how leaders should set expectations, measure outcomes, and stay responsible for results even as automation scales.

This episode, recorded live from the Oregon HFMA Winter Conference, reflects on how change shows up across healthcare revenue cycle operations and how augmented intelligence has become a recurring thread connecting those experiences. We’ll explore why some parts of healthcare evolve quickly while others resist change, and what that means for the people navigating it every day. Brought to you by ww...

In this episode of Revenue Cycle Optimized, Jennifer Glockzin, Senior Director of Patient Access at Infinx, walks through the real-world prior authorization process from intake to determination and appeals. Her breakdown highlights how disciplined workflows, supported by AI agent automation and coordinated human in-the-loop, protect revenue and reduce preventable denials.

When leaders ask “Why AI?” in revenue cycle, they’re often reacting to growing complexity, staffing pressure, and loss of control rather than chasing innovation. This segment reframes the AI conversation around stability, predictability, and restoring confidence in day-to-day operations.

In this episode, Jaideep Tandon, CEO of Infinx, shares a sensible, leadership-driven perspective on the evolving role of AI in RCM. Rather than focusing on technology for technology’s sake, the conversation centers on outcomes: improving efficiency, reducing revenue leakage, and supporting better financial decision-making across increasingly complex healthcare environments. Jaideep addresses how r...

In this segment from What’s My Tagline? with host Carol Flagg, Stuart Newsome discusses why AI messaging in healthcare is hitting a wall and how skepticism is forcing a shift toward outcomes-based positioning. The conversation explores what revenue cycle leaders actually care about in 2026 — trust, specificity, governance, and real operational impact.

AI agents create the most value in revenue cycle workflows when they operate as a coordinated system rather than isolated tools. This episode explores how multi-agent orchestration combined with a tech-enabled human layer delivers reliability, accountability, and real operational outcomes in RCM.

EMR upgrades, clearinghouse changes, and AI are reshaping revenue cycle operations, but they can also disrupt cash flow if not carefully managed. This episode focuses on maintaining revenue continuity during system transitions, highlighting where delays commonly occur, how AI fits into modern workflows, and what operational safeguards help ensure revenue continues to flow through change. Brought t...

In this episode, Navaneeth Nair, Chief Product Officer at Infinx, explains what it really takes to make AI work in revenue cycle management, from decomposing workflows into task-level agents to maintaining human-in-the-loop oversight. The conversation explores why effective RCM automation depends on orchestration, agent lifecycle management, and tech-enabled workflows designed for real operational...

In this RCMinutes episode of the Revenue Cycle Optimized podcast, we unpack what healthcare teams truly trust about AI, and what still makes them hesitate. Drawing from real conversations with providers, this episode explores why reliability, transparency, and workflow alignment matter more than flashy automation.

AI can extract data from documents—but making that data usable, reliable, and scalable is where most efforts fall apart. In this episode, Charu Nevatia explores why AI document capture isn’t plug and play and what it really takes to operationalize it in healthcare. Brought to you by www.infinx.com

Ambulatory and acute care billing share many of the same building blocks, but the way data, workflows, and reimbursement are structured creates very different operational realities. In this episode, we explore how those differences shape not only revenue cycle strategy, but also how automation and AI should be applied effectively with these considerations across care settings.

Healthcare’s cautious approach to AI isn’t resistance to innovation, it’s a response to real operational, clinical, and financial risk. In this episode, we unpack why moving slowly is often the smartest move, and how intentional adoption builds trust, safety, and long-term value.