normalizing-rxnorm

Normalizing drug mentions to RxNorm

Map free-text medication mentions that OpenMed extracts to RxNorm — the U.S. National Library of Medicine's normalized drug nomenclature. The unit of meaning is the RxCUI (RxNorm Concept Unique Identifier): a stable integer that ties together brand, generic, ingredient, strength, and dose form.

RxNorm and the RxNav REST API are fully public and free: no API key, no license agreement, no rate-limit registration for normal use. Of every skill in this terminology batch, this one has the highest value-to-friction ratio — start here when grounding medications.

When to use

• A clinical note names drugs ("metformin 500 mg", "Lipitor", "amox/clav") and you need one stable code per drug for storage, analytics, or interoperability.
• You must distinguish ingredient ("metformin", IN) from a prescribable product — SCD (Semantic Clinical Drug, generic) or SBD (Semantic Brand Drug) — e.g. "metformin 500 MG Oral Tablet".
• You need to de-duplicate brand/generic synonyms onto one concept.
• You need NDC codes (package-level) for a product, or a US Core Medication/MedicationRequest coded with RxNorm.

If the source text is non-English or you need ATC/SNOMED links instead, see mapping-to-snomed; RxNorm itself is U.S.-centric.

Quick start (real RxNav API calls)

Base URL: https://rxnav.nlm.nih.gov/REST. No auth. JSON via ?...&... paths ending in nothing or .json depending on endpoint; the REST root returns XML by default, so request JSON explicitly.

import requests

BASE = "https://rxnav.nlm.nih.gov/REST"

def rxcui_for(name: str) -> str | None:
    """Exact-match RxCUI lookup for a normalized drug name."""
    r = requests.get(f"{BASE}/rxcui.json", params={"name": name}, timeout=10)
    r.raise_for_status()
    ids = r.json().get("idGroup", {}).get("rxnormId", [])
    return ids[0] if ids else None

def approximate(name: str, max_entries: int = 3) -> list[dict]:
    """Fuzzy match for misspelled or abbreviated drug text."""
    r = requests.get(
        f"{BASE}/approximateTerm.json",
        params={"term": name, "maxEntries": max_entries},
        timeout=10,
    )
    r.raise_for_status()
    return r.json().get("approximateGroup", {}).get("candidate", [])

print(rxcui_for("metformin"))                       # -> '6809' (ingredient)
print(approximate("metformin 500"))                 # fuzzy -> candidate RxCUIs