04/11/2026
Touchless Revenue Cycle
Healthcare revenue cycle management (RCM) remains one of the most labor-intensive and costly aspects of healthcare delivery. It accounts for 3–4% of a typical health system's net revenue, translating to more than $140 billion annually across U.S. systems. Traditional automation has delivered incremental gains, but persistent challenges such as high claim denial rates (averaging nearly 20%, with up to 60% never appealed), fragmented technology vendors, manual workflows, and rising labor costs continue to strain operations and delay cash flow.
Enter agentic AI, a significant evolution beyond generative AI. While generative AI primarily creates content or offers advisory insights, agentic AI consists of autonomous AI agents that can perceive, reason, plan, make decisions, and execute complex, end-to-end processes with minimal human supervision — essentially acting as a digital coworker. These agents learn from patterns in rules-based tasks, handle interconnected workflows, and operate under human oversight for exceptions, compliance, and refinement. In RCM, this enables a vision of a "touchless revenue cycle": a self-running system where administrative tasks flow seamlessly with little to no manual intervention, from front-end scheduling through mid-cycle documentation and coding to back-end claims and collections.
In their 2026 report, McKinsey points out that agentic AI offers the first credible path to truly tech-enabled RCM by shifting from narrow task automation to holistic workflow ex*****on. Initial deployments often focus on the back end — where processes are highly rules-governed, labor-intensive, and lower-risk — before expanding across the full cycle. Key use cases include:
Accounts receivable follow-up: Automating high-volume claim status monitoring and outreach to reduce labor hours while processing more claims accurately.
Underpayment and denials management: Identifying patterns, assembling appeal documentation, drafting submissions, and improving overturn rates.
Cash posting and collections: Handling routine payments and patient reminders, freeing staff for strategic work.
These applications leverage agentic AI's ability to orchestrate multi-step tasks, integrate with existing systems, and adapt based on outcomes. Over time, interconnected networks of agents could span the entire cycle — eligibility checks, prior authorizations, coding from clinical documentation, and patient billing — creating a more unified, efficient engine.