Prior authorization is document-heavy, time-sensitive, and consequential. A denied or delayed auth can delay care. A poorly documented approval creates audit exposure. This guide walks the full reference architecture — from intake to audit trail — and explains where Claude does the work, where a human must own the decision, and what a compliance-conscious design actually requires.
A single prior-auth request can span a clinical note, a formulary policy document, a payer coverage determination, a diagnosis history, and a benefits summary. A clinician or reviewer has to hold all of that simultaneously to make a defensible decision. That is exactly the kind of multi-document synthesis task where Claude's long context window earns its place.
Short-context models force chunking. Chunking loses the cross-document reasoning that matters most — for example, matching a specific ICD-10 code in the clinical note against an exception clause buried in the payer policy. Claude can read the full submission and the full policy in a single pass and surface the relevant alignment or gap.
That capability is necessary but not sufficient. The workflow still needs structured retrieval, citation discipline, a defined human gate, and a complete audit trail. Long context is the engine. Governance is the chassis.
The workflow begins when a prior-auth request arrives — typically a PDF or HL7 FHIR payload from an EHR. The intake layer parses the submission into structured fields: member ID, requesting provider, procedure or drug code, diagnosis codes, and any attached clinical notes. This structured extraction feeds downstream retrieval and ensures the audit record starts with clean, traceable inputs.
Claude is prompted to extract and confirm these fields, flagging missing or ambiguous values rather than inferring them silently. Ambiguity at intake is surfaced to a human coordinator before the workflow advances. This prevents downstream errors from compounding.
Once intake is clean, the system queries a policy retrieval layer — a Regulated RAG index built from the relevant payer coverage determination documents, formulary rules, and clinical criteria sets. The retrieval step returns the specific policy passages that govern the requested procedure or drug, ranked by relevance.
Claude receives both the structured intake data and the retrieved policy passages. It is instructed to ground every claim in a cited source. The output is not a narrative opinion — it is a structured comparison: what the policy requires, what the submission provides, and where the gap or match exists. Each point is tagged to the exact policy document and section.
Citation discipline is non-negotiable in a compliance context. If a reviewer or auditor later asks why a request was approved or denied, the system must be able to show the specific policy language that drove the recommendation. Hallucinated or uncited reasoning is a liability, not a convenience.
The gap analysis feeds a concise evidence summary — a one-page structured document that a reviewing clinician can read in under two minutes. It presents the clinical context, the applicable policy criteria, the alignment assessment, and any missing documentation. It does not contain a recommendation to approve or deny. That decision belongs to the clinician.
This is the human gate, and it is the most important design choice in the entire workflow. Claude prepares the decision package. A licensed clinician makes the authorization decision. The system is designed so that the AI output is an input to human judgment, not a substitute for it. The approval or denial is recorded as a clinician action, not a system action.
The gate is not just a compliance formality. It is where clinical context that did not make it into the submission — a phone call with the requesting provider, knowledge of a patient's prior treatment history — can be factored in. The AI layer reduces cognitive load and surfaces relevant policy. The clinician applies judgment.
Every step of the workflow generates a logged record: the raw intake payload, the retrieved policy passages with relevance scores, the Claude prompt and response, the evidence summary presented to the clinician, and the final decision with clinician ID. These records are stored in an append-only log that supports downstream audit, appeals, and compliance review.
The audit trail is designed to support frameworks like HIPAA and the documentation expectations that accompany utilization management programs. AI Definitive's Governed-by-Design framework requires that client data is never used to train models, that all outputs are cited and logged, and that access controls restrict who can query the system and who can view patient-level records. These are architectural commitments, not policy statements.
The Model Governance & Audit Pack formalizes this layer — defining the model version in use, the retrieval index version, the prompt templates, and the change-control process for updating any of those components. When a payer policy updates, the retrieval index is versioned and the change is logged. Reviewers can reconstruct exactly what policy language was in effect at the time of any prior-auth decision.
Prior authorization is not a good use case for AI that operates without oversight — the stakes are too high and the documentation requirements too specific. It is a strong use case for AI that is governed end to end: retrieval that cites its sources, summaries that surface gaps rather than hide them, a human gate that owns the decision, and an audit trail that can answer every question a compliance reviewer will ask.