11 days
The health system processed ~4,500 authorization requests per month, with a median 11-day cycle time from submission to payer decision. Roughly 30% were returned for additional documentation, adding ~4 days each.
Prior Authorization: 80% Cycle Time Reduction Using AI Agents
How a regional health system cut its prior authorization cycle time by 80% — from 11 days to 2.1 — and freed clinical staff from one of the most burdensome administrative workflows in U.S. healthcare.
Get StartedAbout the Project
Prior authorization delays care, eats clinical staff hours, and leaves patients waiting without explanation. Using an AI agent architecture, this health system cut its median authorization cycle from 11 days to 2.1 days and reduced clinical staff time spent on authorization tasks by 65%.
Industry
Healthcare / Regional Health System
~4,500 authorization requests a month across the system's specialty practices.
Business Type
Multi-Specialty Provider Network
A 14-person authorization team working payer portals across the network.
Core Offering
AI-Assisted Prior Authorization
Agents gather documentation, match payer criteria, draft justifications, and submit.
Integration
SMART on FHIR Connections to Epic
Payer document requirements mapped to Epic data-model locations for direct retrieval.
Rollout
Five Months from Criteria Library to Full Deployment
Four phases, starting in shadow mode with specialists reviewing every AI output.
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Scope AI-assisted prior authorization for your own payer mix.
The Problem: What Prior Auth Really Cost
Slow prior auth delayed care and cost revenue.
14 staff
A team of 14 specialists spent most of their day in payer portals, gathering EHR documentation and writing justification summaries. Payer-specific expertise lived in individual heads, not documented systems.
~8 days
Authorization processing added ~8 days to order-to-procedure time for the ~60% of orders requiring it. Each day of delay meant rescheduled procedures and missed clinical windows.
Cut authorization delays.
Free your clinical staff.
Get patients to care faster.
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The Implementation Journey
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Phase 1 — Payer Criteria Library (Months 1–2)
Phase 1 — Payer Criteria Library (Months 1–2)
- Structured coverage-criteria database per payer and procedure type
- Clinical & admin staff validated each payer-procedure combination
- Foundation for the agent's criteria-matching logic & ongoing upkeep
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Phase 2 — EHR Integration & Documentation Mapping (Months 2–3)
Phase 2 — EHR Integration & Documentation Mapping (Months 2–3)
- Established SMART on FHIR connections to the Epic EHR
- Mapped each payer's required docs to Epic data-model locations
- Handled doc-completeness variation across physicians & departments
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Phase 3 — Pilot Deployment & Refinement (Months 3–4)
Phase 3 — Pilot Deployment & Refinement (Months 3–4)
- Shadow mode — specialists reviewed every AI output before action
- Evaluated accuracy; caught systematic matching & documentation errors
- Refined the system before moving routine cases to exception-only review
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Phase 4 — Full Deployment & Monitoring (Month 5 onward)
Phase 4 — Full Deployment & Monitoring (Month 5 onward)
- High-confidence cases went to submission after brief specialist review
- Lower-confidence cases routed to the detailed human review queue
- Weekly tracking of cycle time, first-pass approval & return rate
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Phase 5 - Scale & Governance
Phase 5 - Scale & Governance
- Agent extended to remaining service lines once the pilot held
- Monthly governance review of accuracy, denials, and rule drift
- Ownership handed to operations with informatics on support
What the AI Agent Architecture Did
Routine cases were processed end to end by the agent; exceptions were routed to specialists with full context. That split cut average specialist time per authorization from 38 minutes to 13.
Automated Documentation Gathering
On request, the agent pulled the exact clinical documentation each payer requires for that procedure straight from the EHR — eliminating ~25 minutes of manual compilation per request.
Criteria Matching & Justification Drafting
It compared the record against payer coverage criteria and drafted a justification letter citing the specific findings that met them — flagging borderline or ambiguous cases for closer human review.
Automated Submission & Status Tracking
FHIR-capable payers received electronic submissions with automatic status checks written back to the EHR; portal-only payers got pre-populated fields, reducing data entry to a review-and-submit step.
Prioritized Human Review Queue
Borderline cases, documentation gaps, and payer info requests were routed to specialists with the AI's assessment, the issue, and the relevant criteria attached — resolving exceptions without starting from scratch.
The Results
Every number below was measured in production after launch — not projected in a pitch deck.
80%
Cycle Time Reduction — 11 days down to 2.1 days
65%
Less Clinical Staff Time — On authorization tasks
13 min
Specialist Time Per Auth — Down from 38 minutes
79%
First-Pass Approval Rate — Up from 71%
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