Automate Prior Authorization with AI Agents - Now Available as a Foundry Template

· Source: Microsoft Foundry Blog articles · Field: Health & Wellbeing — Healthcare Systems & Policy, Medical Devices & Health Technology · Depth: Intermediate, medium

Summary

Microsoft has released the Prior Authorization Multi-Agent Solution Accelerator as a Microsoft Foundry template, designed to automate and streamline prior authorization (PA) processes in U.S. healthcare. This solution, available via Azure Developer CLI, deploys four specialist Foundry hosted agents (Compliance, Clinical Reviewer, Coverage, and Synthesis) that operate in a parallel-then-sequential pipeline. Internal testing with synthetic data showed the pipeline reduced review workflow to under 5 minutes per case, addressing the significant administrative burden of PA, which currently averages 39 requests and 13 hours of staff time per week for physicians, costing an estimated $35 billion annually. The template is designed to help health plan payers meet new CMS-0057-F mandates for electronic PA with 72-hour urgent responses starting in 2026, offering a structured recommendation with confidence scores and a full audit trail.

Key takeaway

For health plan payers seeking to automate prior authorization workflows and comply with CMS-0057-F, deploying the Microsoft Foundry template offers a robust starting point. Your team can quickly establish an AI-powered PA pipeline, reducing processing times and administrative costs, while maintaining human oversight and generating audit-ready documentation. Focus on customizing the agent's policy rules and integrating with your existing EHR systems to maximize its impact.

Key insights

AI agents can significantly reduce prior authorization processing times and administrative burden in healthcare.

Principles

Method

The solution uses four specialized AI agents (Compliance, Clinical Reviewer, Coverage, Synthesis) in a parallel-then-sequential pipeline, leveraging structured outputs and real-time healthcare data for automated PA review.

In practice

Topics

Code references

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Microsoft Foundry Blog articles.