Worked example. This is an illustrative worked example, not a client result. The clinic group and every number in it are assumptions. It shows AI for prior authorization: software that reads a patient chart, pulls the facts a payer asks for and assembles the packet. It does not decide medical necessity or submit on its own. A coordinator approves.
Picture a multi-clinic group where prior-auth packet prep is slow. For illustration, assume coordinators spend their time copying facts between a chart, a payer portal, and a checklist. This post walks through the design and what to measure. It reports no result.
The group is hypothetical, so there is no client to name. One detail matters more than a name: the pipeline extracts and assembles, but it never auto-submits. A coordinator approves every packet, and every run keeps an audit log of what the model read and what it produced.
#Where does the time go in a prior-auth case?
Start by measuring, not building. Watch coordinators prep a sample of cases and time each step. The writing is fast. The finding, reading, and re-typing is slow.
- Reading the chart to locate the diagnosis, the tried-and-failed medications, and the ordering physician's notes.
- Matching those facts to the specific payer's requirements, which differ per plan.
- Re-typing everything into the packet and the portal, then double-checking it, spread across interruptions.
- Waiting. A half-built packet sits until the coordinator gets back to it.
So the target was never "write faster." it was "stop making a person hunt for facts that are already in the chart."
#How does AI for prior authorization reduce prep work?
How does AI reduce prior-authorization prep time?
It reads the chart, extracts the clinical facts the payer requires, and drafts the packet so a coordinator reviews instead of retypes. A clinic group would measure prep time and coordinator hours before and after. The coordinator still approves every submission before it reaches the payer.
The pipeline runs in four steps: extract the relevant facts from the chart, assemble them into the payer's packet format, validate that required fields are present and internally consistent, then route the packet to a coordinator's approval queue. Nothing leaves that queue without a human clicking approve.
The design uses the same document-processing pattern as our logistics claims and onboarding worked examples, tuned for a regulated-healthcare vertical. The shape is described on our AI implementation page: extract, validate, route, human approval queue. The difference here is the audit log. Because it is healthcare, record every field the model read and every field it wrote, so a reviewer can trace any line in the packet back to its source in the chart.
#What happens when the AI gets a fact wrong?
It gets caught, because a person reads it. That is the whole point of the approval queue. Expect some share of drafted packets to need a coordinator to fix or add a field, and track that rate. That is not a failure of the pipeline. That is the pipeline doing its job: surfacing a draft fast so a human spends their time reviewing instead of hunting.
We are
Stennir builds a document-processing pipeline that drafts prior-auth packets for a coordinator to approve, with a full audit trail.
We aren't
Stennir is not an auto-submission bot that decides medical necessity or files claims with a payer on its own.
There is no client quote here, because there is no client. The test of the design is whether coordinators stop retyping the chart while the model decides nothing.
#What would a clinic group measure?
Packet prep time and coordinator hours, before and after, on the same clinics, the same coordinators and the same payer mix, with the baseline taken before anything changes. That is how you tell a real improvement from a demo. No result is claimed here.
If you run operations at a clinic group and your team is retyping charts into payer portals, that is the shape of work this fits. We start by measuring, the way this example does, before we build anything. You can read how we frame that on our AI consulting page, and you can see how we think in the rest of the journal. If this sounds like your week, tell us what you're building.
