Prior Auth That Moves: Evidence-Backed Extraction for Medical Necessity Packets and Denials

Prior authorization and denial appeals are rarely blocked by lack of information.

They’re blocked by lack of verifiable information:

• the payer can’t find the diagnosis in the notes,
• the CPT/procedure doesn’t match the supporting documentation,
• the timeline is unclear,
• the clinical criteria aren’t explicitly documented,
• or the packet is incomplete.

Teams end up doing expensive, repetitive work:

• reading clinical notes,
• assembling packets,
• re-stating facts in forms,
• then re-opening documents to prove the same facts during appeals.

The high-value unlock is simple:

Extract the facts you need, and attach evidence so reviewers can verify in one click.

That’s what citation-backed extraction is for.

Note: This post is about document workflow automation—not medical advice.

The “packet” reality of prior auth

A prior auth request is almost never a single form. It’s a bundle:

• prior auth request form
• referral documentation
• chart notes / H&P
• imaging reports (radiology)
• lab results (sometimes)
• medication history (sometimes)
• payer clinical criteria references (for appeals)
• denial letters and EOB-like artifacts

Treating these as isolated PDFs is the fastest way to create rework. Treat them as a document pack and build a workflow around:

• extraction
• evidence
• reconciliation
• exception routing

High-value fields to extract (the ones that drive decisions)

Administrative identifiers

• patient name (or internal ID)
• payer name / plan
• member ID
• provider NPI (if present)
• ordering provider
• facility

Request details

• requested procedure / service (CPT/HCPCS if present)
• diagnosis codes (ICD) if present
• requested dates (start/end)
• place of service

Clinical support anchors (the “why”)

These are often the blockers:

• symptom duration and severity
• prior conservative therapy and timelines
• imaging findings relevant to criteria
• failed treatments / contraindications
• functional limitation statements
• medication trials (when relevant)

Denial and appeal fields

• denial reason code/category (when present)
• criteria cited by payer (if included)
• required missing documentation callouts
• appeal deadline dates (if present)

Why citations are the difference between “data” and “useful”

It’s easy to extract:

• “Symptoms: 6 months”
• “MRI shows tear”
• “Failed PT”

It’s hard to make that extraction defensible.

Citations make each claim reviewable:

• page
• exact region highlight
• supporting snippet (carefully controlled)

So when a reviewer needs to confirm a statement, they don’t search. They click.

And when the payer challenges the packet, your team can quickly:

• confirm what’s documented,
• identify what’s missing,
• and assemble a stronger appeal.

A workflow that improves speed without adding risk

1) Ingest the full packet

Group documents by case/request. If you can, classify by document type:

• chart note
• imaging report
• denial letter
• form

Classification makes downstream extraction more reliable and reduces noise.

2) Extract a “prior auth schema” + citations

Keep it focused on decisions and appeals:

• request identifiers
• requested service
• documented clinical support items
• denial elements (if present)

3) Surface a “packet completeness” checklist

Instead of making staff read everything first, show:

• which required fields are present with evidence
• which are missing or ambiguous
• which conflict across documents (e.g., dates)

4) Route exceptions, not everything

Use confidence and field criticality:

• auto-accept low-risk admin fields when evidence is strong
• require review for high-impact clinical criteria statements
• flag missingness explicitly (present / not present / ambiguous)

5) Build an appeals-ready record

Store:

• extracted fields + citations
• reviewer verification/corrections
• timestamps
• notes on what documentation was added

That becomes your defensible audit trail for internal QA and external disputes.

Schema sketch: prior auth packet extraction

{
  "schema": {
    "payer_name": { "type": "string" },
    "member_id": { "type": "string" },
    "patient_name": { "type": "string" },
    "provider_name": { "type": "string" },
    "requested_service": { "type": "string", "description": "Procedure/service description as written" },
    "requested_cpt_hcpcs": { "type": "string", "description": "CPT/HCPCS code if present" },
    "diagnosis_codes": {
      "type": "array",
      "items": { "type": "string" }
    },
    "requested_start_date": { "type": "date" },
    "clinical_support_statements": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "category": { "type": "string", "description": "e.g., symptom_duration, failed_therapy, imaging_finding" },
          "statement": { "type": "string", "description": "Short extracted claim; must be cited" }
        }
      }
    },
    "denial_reason": { "type": "string", "description": "If denial letter included" },
    "appeal_deadline_date": { "type": "date" }
  },
  "options": { "confidence_threshold": 0.85 }
}

What to measure (the KPIs that move)

• time from intake → submission
• time from denial → appeal submission
• reviewer time-to-verify key criteria
• % packets flagged incomplete before submission
• rework rate due to missing documentation

Prior auth doesn’t get faster by “summarizing notes.” It gets faster by making proof easy and missingness visible.

If you’re building workflows around prior auth or denials, treat citations as the primitive. They’re what turns extracted fields into evidence your team can act on.