Real-World Examples of Unverified Data Risks

Every month a report lands on your desk. A/R. Denial rate. Days in A/R. Clean claim percentage.

You budget with it. You staff with it. Sometimes you pay people on it.

Could you prove it's right?

Not "do you trust your vendor." Most people do, and usually they should. The question is narrower: if your board asked you to demonstrate that the number reflects what actually happened in your operation, could you do it?

For most organizations, no. Not because anyone did anything wrong. Because nobody was ever assigned to check.

Reconciling isn't verifying

If you recalculate a figure and land on the same answer as your vendor, it feels verified.

It isn't. Two calculations drawing from the same misconfigured field, the same undocumented filter, the same assumption about what a status code means, will agree perfectly and both be wrong.

Matching numbers prove consistency, not correctness.

Three ways a clean report misleads

Illustrative patterns, not accounts of any specific organization. The metric that means two things. Two practices both report "A/R over 90 days."

One ages from date of service. The other ages from last submission — which resets on every rebill. Same label, different populations, no way to tell from the report.

The exclusion nobody wrote down. A denial rate quietly omits a claim type. The rate looks fine. Leadership sees no reason to act. The omitted population is where the problem lives.

The status that records a keystroke. A claim marked "under appeal" might mean an appeal was filed — or that somebody added it to a worklist. The system logs the click, not the work.

None of these break the report.

Every one produces a clean, reconcilable number that misrepresents your operation.

Blank is not zero

When a system doesn't know a value, it usually records zero.

"We were allowed nothing" and "we don't know what we were allowed" are different facts. One is a loss. The other may be money you're owed.

Most reporting converts the second into the first, silently, and the difference is gone forever. The number still ties out.

We don't guess. When we can't determine something, we say so.

What you get

We rebuild your A/R independently from source data — and record our result before we look at what your vendor reported. Without that, nobody can prove the analysis wasn't tuned until it matched.

Then every finding gets classified. Not pass/fail. Validated. Reproduced but unconfirmed. Discrepancy. Evidence insufficient. Not validatable, because the workflow the metric depends on doesn't exist in your system.

That last category is usually the most valuable thing we hand you. It tells you exactly where your visibility ends.

Then experienced revenue-cycle people review the exceptions — because software catches broken math, but only someone who has worked a billing desk catches a field that says one thing and means another.

Start small

One question, answered properly: is your reported A/R supported by your own data?

We ask for a defined extract and the exact report your leadership receives. That report is the comparison target, not the source of truth.

You get findings you can act on, findings that need a decision, and an explicit list of what couldn't be determined and why.

We won't quote you a recovery figure before we've seen your data. Anyone who does is selling you a number they made up.

See the A/R Analysis assessment →

JB Human Analytica — Do not validate the report. Validate the chain that created it.