--> --- name: 'prior-auth-coworker' description: 'Prior Auth Review' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- This skill acts as an automated utilization management reviewer. It takes unstructured clinical notes and a procedure code, compares them against internal policy criteria (e.g., conservative therapy failure), and renders a decision.
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# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
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---
name: 'prior-auth-coworker'
description: 'Prior Auth Review'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# Prior Authorization Coworker
This skill acts as an automated utilization management reviewer. It takes unstructured clinical notes and a procedure code, compares them against internal policy criteria (e.g., conservative therapy failure), and renders a decision.
## When to Use This Skill
* When a user asks to "review a prior auth request".
* When checking if a patient qualifies for a specific procedure (e.g., MRI).
* When you need to generate a structured approval/denial letter justification.
## Core Capabilities
1. **Policy Matching**: Checks against specific criteria (e.g., "Pain > 6 weeks").
2. **Trace Generation**: Produces an "Anthropic-style" `<thinking>` trace for auditability.
3. **Structured Output**: Returns a JSON object with decision, reasoning, and timestamps.
4. **Microsoft Payer-Side Multi-Agent Review**: Supports Microsoft Agent Framework architecture patterns with four Foundry Hosted Agents for compliance, clinical, coverage, and synthesis review; gate-based decision rubrics; MCP healthcare data access; confidence scoring; audit trails; Azure Container Apps deployment via `azd`; and human-in-the-loop escalation.
## Workflow
1. **Extract Data**: Parse the clinical note and procedure code from the user's input.
2. **Execute Review**: Run the coworker script.
3. **Present Decision**: Output the JSON decision and the reasoning trace.
## Example Usage
**User**: "Check if this patient qualifies for an MRI of the Lumbar Spine: Patient has had back pain for 2 months, tried PT but it didn't work."
**Agent Action**:
```bash
python3 Skills/Clinical/Prior_Authorization/anthropic_coworker.py --code "MRI-L-SPINE" --note "Patient has back pain > 2 months. Failed PT."
```
## Supported Policies
* `MRI-L-SPINE` (Lumbar Spine MRI)
## References
* https://github.com/microsoft/Prior-Authorization-Multi-Agent-Solution-Accelerator
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