Gating theAuthorization
How a health insurer pre-processes prior authorization requests, and holds the pipeline until a clinical review system approves.
Reviewers evaluate pre-filled cases instead of hunting through PDFs. The pipeline holds until a decision arrives, then routes accordingly. Every decision auditable.
A health insurer receives thousands of prior authorization requests per week. Each request arrives as a bundle of medical document PDFs (a referral form, clinical notes, lab results, sometimes imaging reports), faxed or uploaded from provider offices across the country. A clinical reviewer must evaluate whether the requested procedure is medically necessary. Today, the reviewer opens each PDF, manually finds the diagnosis, the requested procedure, and the supporting evidence, then types the relevant details into the review system. Most of the time is spent locating information, not making decisions. With a federal mandate requiring responses within 7 calendar days, the backlog is becoming a compliance risk. And because every bundle contains PHI, teams evaluating intelligent document processing AWS services like Textract face a second constraint: patient data routed through US clouds.
Ingestion
Provider portals and fax-to-digital gateways deposit prior authorization bundles into the pipeline via HTTP API and S3. Medical OCR turns faxed pages and scanned PDFs into machine-readable text before classification begins. From there, medical document processing runs as a pipeline: classify, extract, verify.
Classification
A Structure Classifier identifies each document in the bundle: referral form, clinical notes, lab report, imaging report. Each match assigns the corresponding extraction schema.
Extraction
Each classifier feeds its own Extractor. The referral extractor pulls the requested procedure, diagnosis codes, and referring physician. The clinical notes extractor captures relevant medical history and clinical justification. The lab extractor pulls specific test values cited as supporting evidence. Each extractor maps its output to the review system's expected format.
Review Gate
The extracted data is delivered to two destinations simultaneously. The clinical review system receives a pre-filled case: the reviewer sees the requested procedure, diagnosis, supporting lab values, and clinical justification already populated, with each value linked to its source document. At the same time, an API Acceptance Gate holds the extract in a pending state, waiting for an external signal.
External Verification
The clinical review system processes the pre-filled case, whether that's a nurse on the insurer's staff evaluating it, an internal rules engine checking against medical policy guidelines, or a third-party clinical decision support system. The review system sends a callback to the API Acceptance Gate with the result: approved or denied.
Gated Delivery
On approval, the gate releases: the authorization decision, the extracted clinical data, and an audit trail are delivered to the provider notification system via HTTP API. Denied cases route to a separate denial workflow. Every decision is logged to PostgreSQL with the full extraction, the reviewer identity, and a timestamp.
Pre-Filled Review Queue
Extracted data is delivered to the reviewer's system ready to evaluate: procedure, diagnosis, and supporting evidence already populated. The reviewer makes decisions, not data entry.
API Acceptance Gate
The pipeline holds extracted data in a pending state until an external system sends an approval signal. Processing pauses mid-flow; output only happens after confirmation.
Decision-Driven Routing
The review system's decision determines what happens next. Approvals flow to the provider. Denials route to a separate workflow. One gate, two paths.
Gated processing: extraction delivers to a review queue and an approval gate simultaneously. The pipeline pauses until an external decision arrives, then routes based on the outcome.
Medical Questions
What healthcare teams ask about processing patient documents with AI.
Yes, that constraint is the point of this pattern. Medical OCR and extraction run with local AI models on your own infrastructure; diagnoses, procedures and lab values are extracted and delivered internally, and no patient data ever reaches an external AI service.
Diagnoses with codes, procedures, medications, lab values with units and reference ranges: the fields a prior-authorization decision actually needs, pulled from referral letters, discharge summaries and lab reports into one structured record.
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