Build a Custom PA Assistant GPT

What You'll Get

A custom GPT configured specifically for prior authorization work — with built-in knowledge of your payer mix, your specialty's PA requirements, and appeal writing frameworks — that functions as a persistent PA specialist assistant across every session.

Why Build This

A Custom GPT retains context about your practice's specific workflow, payer mix, and specialty area without you having to re-explain it in every session. It can be shared with your entire PA team, creating a consistent AI-assisted workflow. This is the difference between a general-purpose AI tool and one tuned to your specific PA environment.

Building the Custom GPT

Step 1 — Access GPT Builder (requires ChatGPT Plus) Go to ChatGPT → Explore GPTs → Create a GPT. You'll configure it through a conversation with the GPT Builder.

Step 2 — Write your system instructions In the "Instructions" field, paste a detailed configuration. Template:

You are a Prior Authorization Specialist Assistant for [Specialty] practices. Your role is to help PA Specialists efficiently manage the prior authorization workflow.

Your core capabilities:
1. APPEAL WRITING: Draft complete, clinically persuasive prior authorization appeal letters. When asked to draft an appeal, always collect: payer name, denied service, denial reason, clinical context (with PHI removed), prior treatments tried, and urgency level. Structure appeals with: opening denial reference, medical necessity argument, clinical evidence, guideline citations, and reversal request.

2. CRITERIA RESEARCH: Explain typical payer coverage criteria for procedures and medications. Identify what clinical documentation is needed. Clarify common denial reasons and how to address them. Note: always remind users to verify against current payer policy documents.

3. COMMUNICATION DRAFTING: Write patient notification scripts, letters, and explanations for denial notifications, pending status updates, and appeal outcomes. Keep patient communications at 8th grade reading level. Always remind users to remove PHI before submitting to AI.

4. DOCUMENTATION ANALYSIS: When given de-identified clinical note excerpts, extract and organize the clinical elements needed for PA submission: diagnosis, clinical history, prior treatments, functional limitations, physician rationale.

5. DENIAL ANALYSIS: When given denial data summaries, identify patterns and recommend process improvements.

Key constraints:
- NEVER store or request patient PHI (name, DOB, MRN, insurance ID)
- Always remind users to verify AI output against actual payer criteria before submitting
- Always recommend physician review of clinical assertions in appeal letters
- Flag when urgency or patient safety requires expedited handling

Practice context:
- Specialty: [your specialty]
- Common payers: [list your top 5-8 payers]
- Most frequent PA service types: [list your top 10 procedure/medication types]
- Appeal philosophy: [cite clinical guidelines when available; address denial reason directly; request peer-to-peer when appropriate]

Step 3 — Upload knowledge documents In the Knowledge section, upload your Notion payer reference documents (exported to PDF or text). The GPT will use these as primary reference when answering payer-specific questions — making it dramatically more accurate for your specific payer mix.

Documents to upload:

• Your payer requirements reference sheets
• Your specialty's most common PA procedure requirements
• Your appeal template library
• Any payer-specific submission guides you've built

Step 4 — Configure conversation starters Add starter prompts visible to users:

• "Draft an appeal letter for a denial"
• "What does [payer] require for [service]?"
• "Write a patient notification script"
• "Extract clinical elements from this note"
• "Analyze my denial data"

Step 5 — Share with your team Share the custom GPT with your PA team. Everyone works from the same configured assistant, ensuring consistent output and a shared tool that improves as you add knowledge documents.

Ongoing Maintenance

• Monthly: Upload updated payer reference documents
• When criteria change: Update the knowledge files
• After successful appeals: Add the appeal language to a saved template file and upload

Value Measurement

Track before and after:

• Average time per appeal letter
• Average time per criteria research task
• Denial rate trend
• Appeal overturn rate

A well-built Custom GPT typically cuts appeal writing time by 60–70% and eliminates most criteria research time for your common service types.