Payer claims & prior authorisation

A mid-to-large payer touches millions of claims and prior-auth requests a year. Most of the work — pulling clinical records, mapping to policy, escalating the edge cases — is repetitive but consequential. Cycle time is measured in days; provider abrasion is measured in NPS damage.

Generic RPA was the wrong tool: it broke every time a payer-rule changed, it could not reason over clinical context, and it never reduced the work the nurse reviewer was actually doing. Generic GPT is the opposite problem — fluent but unaccountable, no audit trail a regulator will accept.

• iWhich prior-auth and claim categories in your book carry enough policy consistency to automate to a >90% first-touch decision?
• iiWhen a case does need your nurse reviewer, what context do they need pre-assembled so it stops being a fresh read?
• iiiWhat does an audit trail look like that your compliance team — and a CMS auditor — can read clause by clause?
• ivWhich provider-facing metric moves first for you — cycle time, peer-to-peer call volume, or NPS on PA decisions?

We deploy an agentic workflow that ingests the claim and clinical record, normalises against the payer's current policy library, and adjudicates the routine path autonomously. Complex cases route to the nurse with the entire reasoning chain prebuilt. Every automated decision ships with a clause-level trace and a human override path. The nurse stays in the loop where it matters; everywhere else, the loop disappears.

Our healthcare squads have shipped production payer workflows under regulator scrutiny. We know what the audit trail has to look like, where nurse-reviewer trust falls apart, and how to scope a first deployment narrowly enough to ship in eleven weeks. Former payer operators sit on every pod, not as advisors but as people writing acceptance criteria with your team.