AI for Treatment Coordinators: Turning Raw Practice Data into Conversions

Most Treatment Coordinators (TCs) are sitting on a gold mine of raw data:

• OrthoFi / Ortho2 exports
• Phone system CSVs
• Insurance/benefits reports
• Portal screenshots and random spreadsheets

The problem: none of that data calls a patient, explains benefits, or fills a chair on its own.

AI can take that raw mess and turn it into:

• Patient-specific scripts (with actual math in them)
• Priority call lists sorted by urgency and impact
• Practice-specific responses that sound like your doctor, not a robot

This guide is written for TCs and practice leaders who want something practical: copy/paste prompts, a mental model for how to chain tools, and a sense of what a productized version can do for you.

1. What “Using AI in the TC Workflow” Actually Means

You don’t need to “learn AI.” You need AI to:

1. Read the messy stuff (CSVs, exports, notes).
2. Do the math (down payments, monthly payments, benefits, urgency).
3. Draft the words (SMS + email scripts).
4. Respect your rules (tone, discounts, chair time, doctor preferences).

Think of AI as an assistant that handles the parsing, calculating, and drafting, so you can focus on what TCs do best: connecting with patients and closing treatment.

In practice, this looks like:

• Data Parsing
  Drop in raw exports (CSV/XLSX/portal dumps); the model identifies key fields: plan total, benefits, balances, last contact, visit type, and status.

• Intelligent Scripting
  Scripts are generated with the numbers already inside:

  • Down payment
  • Monthly payment
  • Benefits amount and expiry date
  • Two realistic time options to offer

• Call Prioritization
  AI ranks patients by urgency and opportunity:

  • Who might say “yes” this week
  • Who is stalling but salvageable
  • Who needs a doctor-backed nudge

• Practice-Specific Knowledge (RAG)
  Using RAG (Retrieval Augmented Generation), AI can be loaded with a private “knowledge pack” for your practice:

  • Tone and voice
  • “Do say / never say” phrases
  • Discount rules
  • Scheduling rules by doctor/chair

Try It Yourself:
Use free tiers of Gemini, ChatGPT, and Grok side by side. You don’t need a full build—just try one small workflow.

2. Using Multiple Models Together (Even on Free Plans)

You don’t have to marry one model. You can chain them and use each where it’s strongest:

Example “Chain” in Plain English

1. Gemini: “Here’s my CSV. Sort patients by urgency and output a structured list with suggested down payment, monthly payment, and benefits data.”
2. ChatGPT: “Here’s Gemini’s list. Write one SMS + one email per patient, in our tone, including the numbers.”
3. Grok: “Shorten each SMS to under 200 characters but keep the key numbers.”

You’re basically telling the models:

• Gemini → “Be my data analyst.”
• ChatGPT → “Be my script writer.”
• Grok → “Be my SMS editor.”

3. DIY Quickstart: Copy/Paste Workflow for TCs

If you want to experiment without buying anything, here’s a simple weekly routine. It looks like work—because it is—but it’s a good way to understand what’s possible.

Step 1: Export the Right Data

From your PMS / OrthoFi / Ortho2 / phone system, export a CSV with:

• Patient Name
• Plan Total / Treatment Fee
• Benefits Remaining + Benefits Expiry Date
• Consult Date or Last Visit Date
• Last Contact Date / Status (Pending Start, No-Show, etc.)
• Preferred Doctor (if relevant)

Step 2: Paste into Gemini (Data Structuring)

Prompt example:

“You are helping an orthodontic Treatment Coordinator.
I will paste a CSV.

1. Group patients by main blocker: cost, timing, insurance, or confidence (guess based on the columns you see).
2. For each patient, suggest:

• a reasonable down payment (5–8% of the plan total),
• a monthly payment that finishes in 18–24 months,
• urgency score from 1–5 (higher for expiring benefits or long pending),
• two realistic appointment times this week.

3. Output as JSON with fields: name, blocker, dp, monthly, benefits_amount, benefits_expiry, urgency, time_option_1, time_option_2.”

Copy the JSON output.

Step 3: Paste JSON into ChatGPT (Script Drafting)

Prompt example:

“You are writing outreach for an orthodontic practice.
I will paste JSON with patient info and suggested payments.
For each patient, create:

• 1 SMS script (friendly, concise, patient-first),
• 1 email script (a bit more detailed),
• 1 chart note sentence to paste back into the PMS.
  Use the practice name: [Your Practice Name].
  Tone: warm, confident, not pushy.
  Always include: down payment, monthly payment, benefits amount and expiry, and the two time options.”

Copy the scripts.

Step 4: Paste SMS Scripts into Grok (Tighten for Text)

Prompt example:

“You are cleaning up SMS for patients.
Shorten each of these messages to under 200 characters.
Keep all numbers (down payment, monthly payment, benefits amount, and dates).
Keep the tone friendly and clear.”

You’ll now have message-ready scripts you can use in your normal workflow.

Step 5: Send & Log

• Send the SMS / email using your existing tools.
• Paste the chart note into the patient’s record:
  • 12/06 – SMS: $220 DP, $155/mo, benefits $1,200 exp 12/31. Offered Thu 3:30 / Fri 9:30. – [TC initials]

If this felt like a lot of copy/pasting, you’re not wrong. That’s exactly what productized tools automate.

4. How We Productize This (So You Don’t Have To)

The DIY chain is great for testing. In real life, most practices don’t want their TC managing three AI tabs and JSON every week.

A productized version automates the entire chain behind the scenes:

• Data Ingestion (Read-Only)
  Connect securely to your PMS / OrthoFi / Ortho2 / phone data. Pull key fields nightly or weekly.

• RAG Corpus Build
  We build a private RAG knowledge base for your practice using:

  • Doctor and TC “voice” examples
  • Existing email/text templates
  • Discount rules and payment policies
  • Scheduling rules by doctor / chair / location
  • “Never say / always say” phrases

• Automated Model Chain
  Behind the scenes, we run something like:

  • Large-context model (Gemini-like) → read and summarize patients
  • ChatGPT-like model → draft scripts in your tone
  • Grok-like layer → compress into SMS format as needed

• Outputs for the TC

  • Scripts with math included: DP, monthly, benefits, dates, times
  • Priority ranking so TCs work top-down
  • One-line chart notes ready to paste back

No JSON, no manual chains, no technical setup for the TC.

5. Why RAG Matters (And What We Actually Load)

RAG is how we keep AI from sounding generic or making up policies.

You can think of it as: “Look at our handbook and templates first. Then answer.”

What Goes into Your RAG Pack

• Practice DNA

  • Voice and tone (formal, relaxed, playful, etc.)
  • Doctor preferences (e.g., “Don’t say ‘cheap’; use ‘manageable’.”)
  • Brand phrases you like to use

• Protocols & Guardrails

  • Discount limits and who can offer them
  • Rescheduling and no-show policies
  • Guardrails for minimum DP / monthly payments

• Objection Handling Library

  • Pre-approved responses for:
    • Cost
    • Timing
    • Insurance/benefits
    • Fear/confidence
  • Variations for SMS vs. email vs. phone

• Scheduling Logic

  • Days/times each doctor prefers starts
  • Chairs or rooms reserved for certain procedures
  • Rules like “no new patient starts after 3:30pm on Fridays”

When AI writes scripts, it pulls from this private pack first, then fills in patient-specific details from your data. The goal: powerful, personalized, and safe.

6. What the TC Actually Receives (In Plain English)

At the end of all this, the TC doesn’t see APIs, models, or RAG diagrams.

They see something like:

• Two Scripts per Patient

  • Cost-first version: focuses on affordability and payment options
  • Benefits-first version: focuses on “don’t lose your dollars” urgency
  • Each includes: DP, monthly, benefits amount/date, and two time slots

• Priority List

  • Patients sorted by urgency + opportunity
  • Flags like: “Needs doctor note about aligners vs braces” or “Check insurance again”

• Chart Note Snippets

  • Example:
    • [12/06] Cost-focus. $220 DP + $155/mo. Benefits $1,200 exp 12/31. Offered Thu 3:30 / Fri 9:30. – [TC]
  • TCs don’t have to invent notes on the fly.

The net effect: less thinking time, more talking time.

7. Light Technical Sketch (For the Nerds in the Building)

If you’re a technical practice owner, manager, or vendor, here’s the high-level architecture:

1. Ingest & Normalize

   • Pull CSV/JSON from PMS, OrthoFi/Ortho2, call logs.
   • Normalize into patient, plan, benefits, contact history.

2. Tag & Compute

   • Detect blockers (cost/timing/insurance/confidence).
   • Compute recommended DP and monthly payment (within your guardrails).
   • Compute urgency (expiring benefits, time since consult, no-show streaks).

3. RAG Pipeline

   • Embed practice handbook, policies, and templates.
   • For each patient, run a vector search for relevant snippets.
   • Feed those snippets + patient data into the model.

4. Model Chain

   • Large-context model → produce structured summary per patient.
   • ChatGPT-like model → generate email + SMS + chart note.
   • Grok-like layer → compress SMS to ideal length.

5. Output Delivery

   • Weekly TC brief, daily queue, or on-demand “script generator.”
   • Optional push into your existing tools (email platform, CRM, PMS notes, etc.).

If you want to go deeper, skim:

• Intro to RAG
• OpenAI function calling
• Gemini file API
• Grok API docs

Final Choice: DIY Experiments vs. Done-For-You

If You Want to DIY

• Export weekly patient data with fees, benefits, and visits.
• Decide your guardrails (e.g., DP between 5–8% of fee; max 24 months).
• Give the models your doctor’s tone and some “do/don’t say” examples.
• Ask for two scripts and two time options per patient.
• Use a simple priority score (urgency + value) to sort.
• Repeat weekly and refine prompts as you go.

This works, and it’s a great way to understand what AI can do for your practice—if you enjoy tinkering and spreadsheets.

If You’d Rather Skip the Grind

We:

• Ingest your data once (read-only)
• Build your RAG knowledge pack
• Chain the right models behind the scenes
• Deliver ready-to-send scripts, with math and times included, sorted by urgency

Your TC stays the human in the loop.
The AI does the parsing, math, and typing.

From there, this plugs directly into tools like the SmileCare AI Weekly TC Brief, so your team can spend more time talking to patients and less time wrestling with exports.