Autonomous Prior Authorization & Clinical Intake Packet Synthesizer
How healthcare providers can eliminate multi-day prior authorization delays by automatically assembling payer-specific clinical evidence packets from unstructured EHR records.
Reference Architecture Blueprint: This is a pre-architected capability blueprint showing how Nyssa AI designs and deploys this system for clients. Designed for Chief Medical Officer, Director of Patient Access, Healthcare Ops.
- •Opportunity: Clinical staff spend hours hunting through multi-year PDFs, lab results, and referral notes to prove conservative therapy history for payer prior authorizations.
- •Architecture: Document ingestion and citation-grounded extraction pipeline that maps patient chart history directly against payer medical policy checklists.
- •Human-in-the-Loop Guarantee: Every checklist item includes a clickable page-and-line citation back to the source EHR PDF so nurses can verify and sign off in under 90 seconds.
- •Deployment Window: 3–4 week sprint integrating with existing fax/PDF intake folders or FHIR DocumentReference APIs.
The Prior Authorization Friction Point#
When a specialist orders an advanced imaging study, biologic therapy, or surgical procedure, commercial payers require documented proof that the patient met specific clinical criteria (such as 6 weeks of failed conservative therapy or specific biomarker thresholds).
Today, patient access coordinators manually read through 50–200 pages of inbound referral faxes and progress notes to copy-paste clinical evidence into payer portals.
Reference Architecture Blueprint#
Instead of asking an LLM to write an unverified appeal letter, we build a policy-driven evidence assembler:
Inbound Referral & Chart Bundle (PDF / Fax / FHIR)
├── 1. Layout-Aware OCR & Temporal Chunker (Preserves dates, labs, and provider signatures)
├── 2. Payer Policy Schema Loader (Loads structured checklist for target CPT + Payer ID)
├── 3. Citation-Grounded Evidence Matcher (Locates exact dates & clinical findings per criterion)
├── 4. Gap Detector (Flags missing lab or therapy records before submission)
└── 5. Packet Generator (Produces annotated PDF packet + pre-filled portal summary)
Expected Operational Impact#
- 80% reduction in chart-hunting time for nursing and patient access staff.
- Fewer avoidable technical denials caused by missing attachment pages or overlooked conservative treatment dates.
- Zero clinical hallucination because every satisfied policy criterion is strictly bound to a verbatim source span and document timestamp.
Nyssa AI Leadership
Fractional CAIO & Lead ArchitectAdvising enterprise leadership and venture-backed teams on autonomous agent architectures and production AI engineering.
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