The Business of Healthcare Podcast · Center for Healthcare Leadership and Management

The State of AI in Revenue Cycle Management

·32 min·2 clips

AHA data says health systems spent almost $20 billion in 2022 to overturn denied claims.

1. The Business of Healthcare Podcast episode focuses on the state of AI in revenue cycle management at the University of Texas at Dallas. 2. Daniel Carnuta hosts the conversation, and David Dawson, Senior Vice President of Client Success at Conifer Health Solutions, explains why Conifer works with hospitals, health systems, and large physician groups. 3. The episode asks how AI can help the back-office side of healthcare delivery by reducing revenue cycle errors and improving reimbursement. 4. Dawson defines revenue cycle management as the administrative and clinical work that starts with scheduling and pre-registration and ends with billing, denials, and appeals. 5. He says accurate demographic data, insurance selection, and authorization at the front end shape how claims adjudicate later. 6. He uses the example of a Blue Cross plan mismatch to show how the wrong plan can cause a denial or an unexpected patient bill. 7. Dawson describes revenue cycle as a system where, "on our best day, what we do is complex" and "on our worst day, it's impossible." 8. He points to turnover in patient registration, coding, and billing as a staffing problem that complicates consistency. 9. He also names cybersecurity, predictive analytics, interoperability, and payer rule variation as technology and operations challenges. 10. Dawson says Medicare, Medicaid, Blue Cross, Cigna, Aetna, and Humana all apply different rules, which makes standardization difficult. 11. He frames AI in revenue cycle as still "in its infancy stage" and says it should eliminate tasks, automate work, educate staff, and adjudicate claims faster. 12. He highlights scheduling, capacity management, plan code selection, eligibility checks, coding support, collections, and revenue forecasting as the main provider-side use cases. 13. He says natural language processing can analyze clinical documentation to improve coding accuracy and speed. 14. He says predictive analytics can segment patients by likelihood to pay and guide whether a letter, text, or phone call is most effective. 15. He says AI can automate prior authorization, detect denial patterns, and support bulk appeals when a payer shows repeated behavior. 16. He says AI can tell staff when not to follow up too early, such as when a payer typically does not pay within 14 days. 17. The tone is conversational and practical, with Daniel Carnuta pressing for examples and Dawson answering in operational terms. 18. The format is an interview with occasional references to conference panels, published articles, and direct experience on both provider and payer sides. 19. Healthcare revenue cycle leaders, hospital finance teams, and operations managers would find this useful. 20. Listeners wanting clinical AI or policy deep dives may skip this one.

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