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Home / Use cases / Prior authorisation
Use case · health plans

Prior authorisation, without making the nurse do the reading.

Most of a prior auth case's cycle time is spent locating and reading clinical evidence. The determination itself takes minutes. We put an agent on the reading and leave the determination exactly where regulators expect it.

The problem

Why this queue costs what it costs.

Where the time actually goes

A UM nurse opens a case and starts hunting: the request, the attachments, the member's history, the applicable medical policy. Reading and assembling can take the better part of an hour. The clinical judgment takes a fraction of that.

Why more headcount doesn't fix it

Volume rises with membership and with every new policy. Turnaround SLAs are set per state and don't move. Hiring your way out means hiring licensed clinicians to do document retrieval.

How it works

What the agent does, and where the human stays.

Step 01

Retrieve and match

The agent pulls the request and attachments, identifies the applicable medical policy, and extracts the supporting clinical facts with a citation for each.

Step 02

Recommend, never decide

It produces an evidence summary and a recommended determination. Cases that clearly meet criteria can be auto-routed for approval; anything adverse goes to a licensed clinician, always.

Step 03

Request what's missing

Where the file is incomplete, it drafts the missing-information request rather than sitting in a queue.

Step 04

Learn from the override

Every nurse or medical director decision — accept, edit, reject — is captured, analysed for patterns and fed back into thresholds.

Guardrails

The constraints that make it deployable.

These are not aspirations. They are enforced in the build, checked by the eval suite and visible in the audit trail.

01Never issues a denial. Adverse determinations require a licensed clinician, without exception.
02Turnaround SLAs tracked per state rule, with alerting before a breach.
03Full audit trail per case: inputs, model, evidence, recommendation, approver.
04PHI stays in your environment; Argos pins the use case to approved models and regions.
05Fairness monitoring across member groups on approval and escalation rates.
Measurement

What the sponsor sees every month.

The baseline is agreed with Finance before we start. These are the lines on the console — and what our outcome fee is read from.

Turnaround time per case

Auto approve rate on clearly meeting cases

Appeal overturn rate

Cost per approved determination

Nurse hours returned to clinical work

Agents involved
Prior Authorisation AgentChart Intelligence ExtractorRegulatory Exposure MapperArgos Token SentinelDrift & Quality Monitor
See each agent's inputs and guardrails →
Typical timeline

12 weeks to first production outcome

Blueprint and eval scaffolding by week 2, shadow mode by week 8, approve mode with a measured result by week 12 — then autonomy expands on evidence.

How AIM sequences it →
Let's talk

Tell us the number you need to move.

A 45-minute working session with an operator who has run the kind of work you are describing. You will get an honest read on where your programme stands and what it would take to move it.