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Automating insurance verification
Insurance verification in Healthcare Admin. Liability-gated, 85% repeatability, Serious-tier risk. Here is what the framework says about the shape of this work, and what supervision it would need if you ran it.
What the shape of this task tells you
With the shape of this task alone, the readiness score lands between 22 and 40 out of 100. Where it falls inside that range depends entirely on your volume: the economics half of the score is worth 45 points and it is earned at the scale you actually run. Enter your numbers to fix the score.
Repeatability 85%. Most instances follow the same path, which is what makes the work tractable. Check the figure against a real sample of fifty items before trusting it — nearly everyone overestimates this, because the exceptions are the part you remember.
Judgment medium, risk Serious. Together these set both the readiness penalty and the supervision this work needs. Errors cost trust or a client. The framework penalises this heavily and the sampling rate stays high indefinitely.
The failure mode to test for
Coverage confirmed from stale eligibility data
This is the one to build your sample around. Errors that survive review are the ones that look correct, so a review pass that only inspects items flagged as unusual will systematically miss it.
The oversight contract
Delegation level 2 of 4 — Draft and approve. Read production, write to a draft queue a person releases.
| Period | Sample | Step down when |
|---|---|---|
| Weeks 1–2 | 100% | Error rate under 1% for the full period |
| Weeks 3–6 | 66% | Error rate under 1% for the full period |
| Week 7 onward | 27% | Steady state — do not reduce further |
Step down on measured performance, never on the calendar date. If the error rate breaches the ceiling at any point, go back up a step.
Sample adequacy. Seeing no faults in a sample of n only rules out error rates above roughly 3 ÷ n. To have evidence for a 1% ceiling you need about 300 reviewed units a month. At a 27% steady-state rate that means a monthly output of roughly 1,129 units before a clean month means anything. Below that, treat a clean month as an operational check rather than proof.
Liability-gated
This verdict rests on who is accountable when the work is wrong. A more capable model does not change who answers for it, so this one is structural and expected to hold.
Get the full verdict for your numbers
The score above is the shape of the work. Enter headcount, salary, hours and output and the audit returns the readiness score, three costed scenarios and a supervision schedule sized to your volume.
Audit this taskCommon questions
Should insurance verification be automated?
It depends on your volume as well as the shape of the work. On shape alone this task scores between 22 and 40 out of 100, with 85% repeatability, medium judgment and Serious-tier risk. The remaining 45 points of the score come from your cost per unit and whether your manual spend covers the fixed costs, so a low-volume version of this task can score well below a high-volume one.
How much output would need to be reviewed?
At steady state, about 27% of output, after a ramp starting at 100% for the first fortnight. The error ceiling is 1% — above that, automation pauses.
What is the most likely way this goes wrong?
Coverage confirmed from stale eligibility data.
Will a better model change this verdict?
This verdict rests on who is accountable when the work is wrong. A more capable model does not change who answers for it, so this one is structural and expected to hold.