Prior auth automation is easy to demo and hard to trust. The failure mode isn't "can't decide" — it's an unaccountable decision about someone's care. Here's the architecture that makes it deployable in a regulated setting.
1. Ground the decision in the payer's own policy. RAG over the applicable medical-policy documents (hybrid vector + keyword retrieval, with citations). The agent decides from retrieved criteria, not model priors — and every verdict carries the exact policy clause it matched. No citation, no decision.
2. Redact PHI on the input path. Detection/redaction runs before the model sees the request, so it reasons over the clinical facts, not raw identifiers.
3. Make the human sign-off a real execution gate, not a suggestion. The agent produces a decision + citation, then halts and routes to a clinician. Below-confidence or criteria-not-met cases never auto-resolve — a human owns the final call. This is the single most important design choice.
4. Emit "what's missing," not just approve/deny. When documented criteria aren't met, the agent returns the specific gap so staff can request one more document up front — turning a future denial into a same-day fix.
5. Append-only audit trail. Every request, retrieval, decision, citation, and sign-off is logged immutably — the record you need for appeals and payer disputes.
The payoff is boring in the best way: routine authorizations clear in minutes with a reason on the record, and clinicians spend their time on the genuinely complex cases. We ship it as one of several governed healthcare AI agents in IntelliBooks Studio — more at intellibooks.ai/overview.
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