Why AI Automation Must Preserve the Human Element in RCM with Ken Hogue | Podcast Shorts from ArceeHQ

In this clip

Ken Hogue describes two AI technologies reshaping revenue cycle: ambient AI for reducing provider documentation burden, and two-way conversational AI for patient service. As automation handles the simpler patient questions, the ones that reach staff become harder and more nuanced, requiring teams to scale and adapt rather than shrink. He argues that organizations must design AI workflows in revenue cycle so that complex or unresolved patient interactions always reach a human quickly, because AI that cannot gracefully hand off what it cannot resolve becomes a source of frustration rather than a force multiplier.

Key Takeaway

Revenue cycle organizations deploying patient-facing AI must ensure that automation enhances the human interaction rather than replacing it. When AI resolves simple questions but creates no path to a person for the complex ones, it frustrates patients instead of helping them.

“we need to make sure we don’t lose the human element or human touch because if we do then it actually becomes a frustrator instead of an accelerator for an organization.”
Ken Hogue, Chief Revenue Officer, United Health Services

Agentic AILeadership

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Ken Hogue

Season 2 · Episode 11 · 45 min

Building a High-Reliability Revenue Cycle: Denial Prevention, Agentic AI, and Leadership at United Health Services

Ken Hogue · Chief Revenue Officer, United Health Services

Ken Hogue brings a high-reliability organization framework to claims denial management, drawing on his leadership at United Health Services, the largest comprehensive healthcare system in the Greater Binghamton, New York region, where he has led revenue cycle transformation since 2020, including a landmark Epic implementation. He explains how UHS built its denial prevention capability into the foundation of its Epic deployment rather than layering it on afterward, how the HRO model has created a prevention-first culture with a dedicated SWAT team of denial resolution specialists, and how root-cause analysis drives decisions about when to apply technology automation versus front-end process discipline. The conversation covers a recent alternate payer and third-party liability verification initiative, the touches-per-clean-claim metric as a primary measure of initiative success, and Ken's framework for navigating the tension between operational quick wins and longer-horizon technology investments through sprint-based governance and compromise models. On agentic AI, Ken is direct that organizations no longer have a choice about whether to embrace it because payers are already using it, and describes the risks of automation fragmentation when policies change, the evolution of revenue cycle management toward managing people and technology workflows simultaneously, and the clean claim pass rate as his primary daily indicator of organizational health.

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