Commentary|Videos|June 26, 2025

AI hype vs. reality in health care billing

Author(s)Doug Marcey

Doug Marcey, CTO of Coronis Health, breaks down where AI fits into revenue cycle management — and why the right processes need to come first for automation to truly deliver results.

Episodes in this series

Artificial intelligence (AI) won’t solve bad workflows — but it can help optimize good ones. In “AI hype vs. reality in health care billing,” part of Medical Economics’ June 2025 Practice Academy, Doug Marcey, chief technology officer of Coronis Health, explains how artificial intelligence and automation can take repetitive, error-prone tasks off staff plates and improve revenue cycle performance.

Marcey walks through practical AI use cases across the RCM process, including eligibility checks, charge capture, denial analysis, coding support and patient collections. But to get results, he cautioned, practices need to start with clean data and clearly mapped processes. Automating a broken system only scales the problems.

Instead, he encouraged leaders to pinpoint bottlenecks and inefficiencies, then apply automation strategically — with oversight and quality checks in place. AI isn’t about replacing staff, but empowering them to spend less time on routine work and more time on what matters.

About the presenter

Doug Marcey, chief technology officer, Coronis Health

Doug Marcey joined Coronis Health in 2023, having served as CTO of the MiraMed family company Plexus Technology Group since 2016. Mr. Marcey leads our IT teams to provide the technology needed to support the Coronis mission, including stable infrastructure, flexible cloud computing, powerful custom software, and innovative automation and machine learning platforms. Mr. Marcey has had a diverse background across a number of verticals, including government/defense contracting, entertainment, financial institutions, and NGOs. He started his career in healthcare technology as an NIH contractor in 2004 and has been working on EMR/EHR and RCM solutions since 2006. He studied computer science, and artificial intelligence at George Mason University.

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