Datasets:

AmareshHebbar
/

medical-billing-sft

license: apache-2.0
language:
  - en
tags:
  - medical
  - billing
  - cpt
  - rvu
  - physician-fee-schedule
  - cms
task_categories:
  - text-generation
pretty_name: Medical Billing Assistant
size_categories:
  - n<1K

Medical Billing Assistant

Part of the AxisMapper Medical AI Suite — 16 domain-specific SFT datasets for fine-tuning medical LLMs.

Built by AmareshHebbar | Studio Ilios / Humanova Minds

What this dataset does

Procedure descriptions → CPT/HCPCS code + Work RVU + billing guidance

Why download this

Automate medical billing, build procedure-to-code lookup tools, or train models to assist with outpatient coding under the Medicare Physician Fee Schedule.

Dataset stats

Split	Rows
Train	112
Validation	14
Test	14
Total	140

Data format

Every row is a messages list in chat format — compatible with Unsloth, TRL SFTTrainer, LLaMA-Factory, and any OpenAI-style fine-tuning pipeline:

{
  "messages": [
    {"role": "system",    "content": "You are a ..."},
    {"role": "user",      "content": "Procedure: Total knee arthroplasty, right knee."},
    {"role": "assistant", "content": "CPT/HCPCS: 27447
Description: Arthroplasty, knee, condyle and plateau
Work RVU: 20.19
Source: CMS PFS 2026"}
  ]
}

Data source

CMS Physician Fee Schedule 2026 — PPRRVU nonQPP (17k procedures) → https://www.cms.gov/medicare/payment/fee-schedules/physician

All data is extracted from authoritative public sources. No LLM-generated or synthetic content.

Who should use this

Medical billing companies, revenue cycle management vendors, outpatient coding teams.

Quick start

from datasets import load_dataset

ds = load_dataset("AmareshHebbar/medical-billing-sft")
print(ds["train"][0])

Fine-tuning example (Unsloth)

from unsloth import FastLanguageModel
from trl import SFTTrainer
from datasets import load_dataset

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-3B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)

dataset = load_dataset("AmareshHebbar/medical-billing-sft", split="train")

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="messages",
    max_seq_length=2048,
)
trainer.train()

Related datasets in this collection

Dataset	Task	Rows
icd10-coder-sft	ICD-10-CM coding	74.7k
symptom-diagnoser-sft	Symptom → diagnosis	119k
clinical-summarizer-sft	SOAP summarization	30k
discharge-qa-sft	Discharge summary QA	30k
pmjay-classifier-sft	PM-JAY packages	11.1k
radiology-coder-sft	Radiology coding	25k
medical-ner-sft	Clinical NER	16.7k
hindi-medical-sft	Hindi medical QA	19.7k

Citation

@misc{axiomapper2026,
  author    = {Hebbar, Amaresh},
  title     = {AxisMapper: Medical AI Fine-tuning Dataset Suite},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/collections/AmareshHebbar/axiomapper-medical-ai-suite}
}

AxisMapper is an open-source project. Star the repo, open issues, and contribute at GitHub.