# Etl To Omop Cdm

Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP...

- Repository: https://github.com/maziyarpanahi/openmed (5755 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add maziyarpanahi/openmed --skill etl-to-omop-cdm -g -a claude-code -y`
Codex: `npx skills add maziyarpanahi/openmed --skill etl-to-omop-cdm -g -a codex -y`
Cursor: `npx skills add maziyarpanahi/openmed --skill etl-to-omop-cdm -g -a cursor -y`
Gemini CLI: `npx skills add maziyarpanahi/openmed --skill etl-to-omop-cdm -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add maziyarpanahi/openmed`

## Description

Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP CDM v5.4 clinical tables (condition_occurrence, drug_exposure, measurement) for OHDSI/ATLAS analytics. Use when the user wants to load NLP-derived facts into an OMOP database, build an OHDSI ETL from clinical notes, populate condition_occurrence or drug_exposure from text, or standardize note-derived findings to OMOP standard concepts. Covers the source-to-standard concept mapping pattern, required vs optional CDM fields, type concepts for NLP-derived rows, and the user-supplied OHDSI vocabulary (CONCEPT/CONCEPT_RELATIONSHIP). Consumes coded OpenMed analyze_text output (after SNOMED/RxNorm/LOINC linking) and produces OMOP-conformant rows.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ETL to OMOP CDM

The OMOP Common Data Model (CDM) is the OHDSI standard for observational health
data. This skill maps **OpenMed-derived clinical facts**, entities from
`analyze_text` that you have already linked to a source terminology, into the
OMOP clinical event tables `condition_occurrence`, `drug_exposure`, and
`measurement`. The NLP runs **on-device**; OMOP loading is a downstream,
deterministic transform.

## When to use this skill

After you have (a) extracted entities with OpenMed and (b) coded them to a
source vocabulary (ICD-10-CM / SNOMED for conditions, RxNorm for drugs, LOINC
for labs, see the linking skills). Use this skill to turn those coded facts
into OMOP rows. It is *not* a clinical NER skill and *not* a code-linking skill;
it assumes both are done.

## Quick start

```python
import openmed

note = "Assessment: type 2 diabetes mellitus. Started metformin 500 mg PO BID. HbA1c 8.2%."
result = openmed.analyze_text(note, output_format="dict")
# result["entities"] -> [{text,label,confidence,start,end}, ...]

# You then code each entity to a SOURCE concept using the OHDSI vocabulary you
# downloaded (see linking-umls-concepts / normalizing-rxnorm / mapping-loinc),
# and map SOURCE -> STANDARD via CONCEPT_RELATIONSHIP ('Maps to').
fact = {
    "person_id": 1001,
    "domain": "Condition",
    "source_code": "E11.9",          # ICD-10-CM, from your coding step
    "source_vocabulary": "ICD10CM",
    "source_concept_id": 45533010,    # OHDSI CONCEPT for E11.9 (lookup)
    "standard_concept_id": 201826,    # 'Maps to' -> SNOMED 'Type 2 diabetes mellitus'
    "start_date": "2024-03-12",       # from building-patient-timelines
    "char_span": (fact_start, fact_end),
}
```

OpenMed never ships UMLS/SNOMED/RxNorm/LOINC content. You supply the OHDSI
**vocabulary bundle** (Athena download) and do the lookups under your own
license. OpenMed provides the spans and labels.

## The source → standard pattern (the heart of OMOP)

The rest: https://github.com/maziyarpanahi/openmed/blob/HEAD/skills/etl-to-omop-cdm/SKILL.md