Turnaround time is on every AI vendor’s QBR slide. It improves whether your workflow is working or failing, which means it cannot tell you which one you are looking at.
When review is thorough, items take longer. When review is skipped, items move faster. Both show up as a better number.
But speed is not the real problem. Speed is what makes the real problem hard to see.
Think about how you check this work today. Someone pulls thirty items a month, reviews them, and if they come back clean you assume the rest are fine too.
That has served you well, and there is a reason it worked. Twenty coordinators make twenty different mistakes. One misreads a payer rule, one keys the wrong date, one misses a note in the chart. The errors are scattered. Pull thirty at random and you get a fair picture of the whole.
An AI system does not make mistakes that way. It is working through thousands of items at once, and whatever it gets wrong, it gets wrong across all of them. It may be getting several things wrong at the same time, and each of those repeats on every case that fits the pattern. If it misreads how one referring practice documents a diagnosis, it misreads every chart that practice sends you, identically, all day, for as long as nobody catches it.
So thirty clean items no longer mean what they used to mean. The mistakes are not scattered anymore. They are concentrated on particular kinds of cases, and your thirty may not contain a single one of them.
Nothing about your QA process got worse. The work it was checking changed underneath it, and the sample never did.
Most of these items will be fine. That stays true even when the system is working exactly as intended. But an error that repeats does not land on a random few patients. It lands on everyone who shares whatever the system got wrong. Everyone from that practice. Everyone with that diagnosis. Everyone whose coverage is recorded that way.
Many will never notice. Some will carry something in the record that does not matter until the day it does. A few are denied or delayed now, in a way that looked completely ordinary when it happened.
What that costs you is real. Checking has to run continuously instead of at quarter end, and it has to keep landing on the cases most likely to break rather than on whatever comes up. That is money spent on something that will never make the turnaround chart look better.
It is the difference between knowing your error rate and knowing where your errors are.
Xillium helps define how repeated errors surface in AI-supported workflows. What that looks like in practice is on our Solutions page.
