Summary
This report examines how health systems are approaching mid-revenue cycle management during a period of widening distance between strategic ambition and operational execution. Each section centers on a core dimension of MRCM strategy, from the associated trade-offs of outsourcing to the organizational and financial prerequisites to deploying AI in the mid-cycle effectively. Additionally, the report gives particular attention to how EHR roadmaps and buying committee dynamics filter AI investment decisions, how outsourcing arrangements shape upstream prevention capacity, and why conventional ROI framing understates AI's sustained value. The insights presented result from a mixed-method effort conducted jointly by THMA and SmarterDx, synthesizing findings from a survey of 81 senior revenue cycle leaders across U.S. health systems. These quantitative findings were augmented with five in-depth interviews with C-suite executives to provide nuance and contextual depth.
Key Takeaways
AI augments existing architecture; it does not replace it.
Systems that sequence revenue integrity infrastructure and identify inefficiencies before layering AI realize sustained value over time. Those that deploy AI before building the foundation miss the compounding returns of an already optimized mid-cycle.
Outsourcing trades operational burden for strategic distance.
The decision to offload mid-cycle functions has meaningful downstream consequences for visibility, alignment, and AI readiness. A growing subset of systems is reclaiming strategic ownership while maintaining external execution.
Durable ROI requires separating lift from long-term value.
Accurately capturing the value of AI investment requires measuring long-term performance gains over traditional ROI metrics like revenue lift. Systems that conflate the two risk underinvesting in mid-cycle AI and underestimating the cost of persistent gaps.
Payer-provider friction increases the cost of inaction.
Payers are automating their offense at a pace reactive mid-cycle postures cannot match. Health systems staying ahead of the curve share a common sequencing logic: build a robust revenue integrity foundation first, then deploy targeted AI to address specific pain points human intervention alone cannot fix.