Deterministic ICD-10 Medical Billing Code Mapping with Python & Knowledge Graphs

In medical AI applications, hallucination in billing code assignment is unacceptable. Assigning an incorrect ICD-10-CM diagnosis code or CPT procedure code leads to immediate insurance claim rejections, financial audit penalties, and compliance violations.

In this tutorial, we will build a HIPAA-Compliant Medical Billing Pipeline using Python and a ground-truth Knowledge Graph.

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1. HIPAA Safe Harbor PHI Redaction

Before passing clinical notes to any LLM or processor, we must redact Protected Health Information (PHI) under HIPAA Safe Harbor rules:

import re

PHI_PATTERNS = {
    "ssn": re.compile(r"\d{3}-\d{2}-\d{4}"),
    "dob": re.compile(r"(DOB|Date of Birth):\s*\d{2}/\d{2}/\d{4}", re.IGNORECASE),
    "patient_name": re.compile(r"(Patient Name|Patient):\s*[A-Z][a-z]+\s+[A-Z][a-z]+")
}

def redact_phi(text: str) -> str:
    redacted = text
    for phi_type, pattern in PHI_PATTERNS.items():
        redacted = pattern.sub(f"[REDACTED_{phi_type.upper()}]", redacted)
    return redacted

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2. Ground-Truth Medical Knowledge Graph Mapping

Instead of letting an LLM guess billing codes, we anchor clinical terms directly to an immutable knowledge graph:

ICD10_DB = {
    "type 2 diabetes": {"code": "E11.9", "desc": "Type 2 diabetes mellitus without complications"},
    "essential hypertension": {"code": "I10", "desc": "Essential (primary) hypertension"},
    "chest pain": {"code": "R07.9", "desc": "Chest pain, unspecified"}
}

def map_diagnoses(clinical_text: str):
    text_lower = clinical_text.lower()
    matches = []
    for term, data in ICD10_DB.items():
        if term in text_lower:
            matches.append({"term": term, "code": data["code"], "description": data["desc"]})
    return matches

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3. Production Turnkey AST Coder Graph

For the production-ready medical billing engine featuring NCCI edit validation and first-pass claim acceptance scoring, check out Med-Verify AST Coder Graph on our store:

👉 Med-Verify AST Coder Graph ($39 USD)