AI Risks Exposed in 90-Days

Corliss
Feb 12
2 min read

Your AI didn’t fail loudly.

It failed quietly—inside your revenue cycle.

AI drift, hallucinated coding, misrouted work queues, and denial spikes don’t show up as “AI errors” on a dashboard.

They show up as missed charges, delayed cash, audit exposure, and unexplained variances.

If you can’t prove your AI is behaving the way it is supposed to behave, you’re not managing innovation—you’re carrying unpriced financial risk.

📉 Revenue leakage

⚠️ Compliance exposure

📊 Decisions based on unverified outputs

Evidence-based AI oversight isn’t optional anymore. It’s a fiduciary responsibility.

The question isn’t “Do we use AI?”It’s “Can we defend what our AI is doing—today?”

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CCA, CAIMC, CAIP, CSM, CBCS, CPDC, serves as the Principal and Managing AI Consultant of P3 Quality™, a Healthcare Tech company specializing in Epic and Cerner AI in Revenue Cycle research, development, and issue resolution management. She also serves as a subject-matter expert and a member of the Volunteer Education Committee at the American Institute of Healthcare Compliance (AIHC). She is a Member of the Professional Women's Network Board (PWN). LinkedIn Profile: http://www.linkedin.com/in/ccollinsrhitcca

Disclosures/Disclaimers:

AI Risks Exposed in 90 Days. This analysis draws on research, trends, and innovations for AI in Revenue Cycle Management (RCM) functions. Some of the blog content is generated by AI. Reasonable efforts have been made to ensure the validity of all materials and the consequences of their use. If any copyrighted material has not been appropriately acknowledged, use the contact page to notify us so we can make the necessary updates.

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