/etl-to-omop-cdm

name: etl-to-omop-cdm
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."
license: Apache-2.0
metadata:
  project: OpenMed
  category: analytics-reporting
  pairs: after
  version: "1.0"

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

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)

Every clinical event row carries **two** concept ids:

1. `*_source_concept_id` — the OHDSI CONCEPT for your original code (e.g. the ICD-10-CM or RxNorm code your linking step produced). 2. `*_concept_id` — the **standard** concept, obtained by following `CONCEPT_RELATIONSHIP.relationship_id = 'Maps to'` from the source concept. Conditions standardize to **SNOMED**, drugs to **RxNorm**, measurements to **LOINC**. If no mapping exists, set the standard id to `0`.

Domain → table → fields

See `references/omop_cdm_v5_4_fields.md` for the full per-table field list. Core mapping by OpenMed entity domain:

| OpenMed entity domain | OMOP table | Standard vocab | Key date / value fields | | --- | --- | --- | --- | | Disease / Condition | `condition_occurrence` | SNOMED | `condition_start_date`, optional `condition_end_date` | | Drug / Medication | `drug_exposure` | RxNorm | `drug_exposure_start_date`, `drug_exposure_end_date`, `quantity`, `sig` | | Lab / Measurement | `measurement` | LOINC | `measurement_date`, `value_as_number`, `unit_concept_id`, `value_as_concept_id` |

Type concepts: mark rows as NLP-derived

Every event row needs a `*_type_concept_id` recording **provenance**. For facts derived from clinical text, OHDSI uses the type concept **32831 "EHR episode record" / "Note"** family — specifically prefer a *"...from note"* / *"NLP"*-flavored standard type concept from the `Type Concept` vocabulary in your bundle. Do not invent ids; resolve the type concept against the vocabulary you loaded so cohort builders can filter NLP-derived rows.

Workflow

1. **Extract & code.** `analyze_text` → entities; link each to a source code (linking skills). Resolve `source_concept_id` and the `'Maps to'` standard concept from your Athena vocabulary. 2. **Resolve dates.** Attach start (and end, where known) dates per `building-patient-timelines`. OMOP date fields are `DATE`; keep the matching `*_datetime` only if you truly have a time. 3. **Assign `person_id`.** Join to your `person` table by an internal key — not by any PHI string. De-identify upstream. 4. **Build rows** with required keys, the source+standard concept pair, the NLP type concept, and a unique surrogate `*_occurrence_id` / `*_exposure_id` / `measurement_id`. 5. **Stage `*_source_value`** (the raw surface string, after de-id) for QA traceability — but never put raw PHI there. 6. **Conform & validate.** Run OHDSI **Achilles**/**DataQualityDashboard** on the loaded CDM before analytics.

Hand-off to / from OpenMed

• **From OpenMed:** `analyze_text` entities (offsets + labels), `deidentify`

upstream, and the per-domain linking skills (`linking-umls-concepts`, `normalizing-rxnorm`, `mapping-loinc`, `mapping-to-snomed`, `coding-icd10`).

• **To OHDSI:** loaded `condition_occurrence` / `drug_exposure` / `measurement`

rows are consumed by ATLAS, Achilles, and cohort definitions — and by `computing-ecqms` for measure denominators/numerators.

Edge cases & gotchas

• **No license bundling.** OpenMed does not include SNOMED/RxNorm/LOINC/UMLS.

Download the OHDSI vocabularies fr