AI is turning the healthcare revenue cycle into an operating system
STAT News
While clinical uses of artificial intelligence (AI) grab headlines, the fast-growing technology’s first big, measurable win in healthcare is already happening within the revenue cycle. The key question now facing healthcare organizations is how best to prioritize the needs and interests of various stakeholders, including administrators, clinical staff, patients and payers, when deploying AI.
A hospital can save your life in four hours and wait three months to get paid for it
Tech Crunch
Roughly 10-20% of all healthcare claim dollars are denied on first submission. The final denial rate, after appeals, is below 2%. That means about 80%-90% of initial denials are eventually overturned. The care was right. The paperwork just had to be fought over, sometimes for months, by teams of people whose entire job is to argue with insurance companies over money the hospital has already earned.
Medicare Advantage insurers get a 2.5% payment increase
Healthcare Finance News
Medicare Advantage insurers are getting a 2.48% payment increase for 2027, which is more than the 0.09% increase proposed in the January Advance Notice. The final policies in the 2027 rate announcement are expected to increase insurer payments by over $13 billion in 2027.
How a practical AI use case supports the healthcare revenue cycle
Health Data Management
Healthcare executives sit through meeting after meeting in which artificial intelligence is described in terms that sound impressive but land without meaning. Agentic AI. Orchestration layers. Multi-agent systems. Sub-agents. The vocabulary keeps growing, and the gap between the language and the actual work keeps widening.
Agentic AI evolution begins to pave way for autonomous revenue cycle
Tech Target
Artificial intelligence is already a staple for revenue cycle leaders, but emerging capabilities through agentic AI could bring automation to a whole new level: autonomous revenue cycle. Autonomous revenue cycle brings together AI, machine learning and robotic process automation to handle end-to-end processes with very limited human intervention.
