Healthcare & MedTech · Use Case
Prior Authorisation Processing: 80% Faster Turnaround.
Every prior authorisation request means someone opening the payer’s policy manual, finding the right criteria, checking them against the clinical record, and writing a determination. A retrieval-grounded agent does the reading and the first draft instead. It cites the exact policy paragraph it relied on, and a nurse or medical reviewer checks the reasoning and signs before anything goes back to the provider.
The Problem
Policy lookup is the slow part, not the decision.
A prior-auth determination is mostly document work. The medical-necessity call is bounded by the payer’s published criteria, so the time goes into locating the right policy, reading it against the submitted clinical notes, and writing the determination in a defensible form. Volume climbs, turnaround slips, and providers wait.
The clinical judgment stays with a licensed reviewer. What a model can take on is the retrieval and the drafting that sit around that judgment.
Drafting, not adjudicating
The agent proposes a determination with the policy citation attached. A licensed reviewer accepts, edits, or overturns it. No authorisation or denial is issued without that sign-off.
The Approach
Retrieval over the payer’s own policy set.
We build a retrieval pipeline over your published medical and pharmacy policies, then wrap it in an agent that pulls the criteria relevant to a request, lines them up against the clinical documentation, and drafts a determination with each criterion marked met or not met. Every point links back to the paragraph it came from, so a reviewer can check the source in seconds. Agentic AI covers how the workflow and its human-in-the-loop gates are built.
Because the reasoning is grounded in retrieval rather than a model’s memory, a policy update flows through by re-indexing the document, not by retraining anything.
Citations, not confidence scores
Retrieval-augmented generation means every line of the draft points to a specific policy passage. A reviewer audits the citation, not a black-box score.
Guardrails
The same delivery standard as every clinical engagement.
This ships under supervised delivery. A licensed reviewer signs every determination, so nothing is autonomous. The pipeline runs in your cloud tenant on infrastructure you control. PHI in the clinical documentation stays inside your environment, and the agent is red-teamed before it reaches production.
Every clinical statement of work carries a mandatory human-review clause and a Clinical Governance Acknowledgement, set out in our Clinical AI Governance Protocol.
Supervised delivery, always
Clinical AI at Nyveon.AI ships as supervised POC and validation work with a licensed reviewer in the decision loop. That’s the published boundary, not a project-by-project call.
Who This Is For
Payers and delegated entities carrying a PA backlog.
Health plans, third-party administrators, and provider revenue-cycle teams that submit or process high volumes of prior-auth requests, where turnaround targets are contractual and every determination has to trace back to a written policy.
FAQ
Common questions about prior authorisation processing.
Does the AI approve or deny prior-auth requests?
No. It drafts a determination with the supporting policy citation attached. A licensed reviewer accepts, edits, or overturns it, and signs. No authorisation or denial is issued without that sign-off.
How does it handle a policy change?
The policies are retrieved at request time, not memorised. Re-index the updated document and the agent applies the new criteria on the next request. Nothing is retrained.
How much faster is turnaround?
Around 80% faster prior-auth turnaround, from automating the policy lookup and the first-draft determination.
Where does the clinical data go?
Nowhere. Retrieval and drafting run inside your cloud environment. PHI in the clinical documentation is zero-egress by architecture.
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