OpenMed ETL to OMOP CDM

What it does

When your agent uses it

• Loading NLP-derived clinical facts into an OMOP database
• Building an OHDSI ETL from clinical notes
• Standardizing note-derived findings to OMOP standard concepts

Example prompts

• “Turn the coded OpenMed output for this note into condition_occurrence and drug_exposure rows.”
• “Map our RxNorm-linked medications to standard concepts and fill drug_exposure.”
• “Which required OMOP fields am I missing for measurement rows built from lab text?”

Requirements

• OpenMed `analyze_text` output already linked to SNOMED, RxNorm or LOINC
• An OHDSI vocabulary bundle (Athena) with CONCEPT and CONCEPT_RELATIONSHIP

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

1. 1*_source_concept_id — the OHDSI CONCEPT for your original code (e.g. the
2. 2*_concept_id — the standard concept, obtained by following

What it can do on your machine

Read from SKILL.md and the folder at commit 34d7b8c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • ohdsi.github.io
  • athena.ohdsi.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

OpenMed ETL to OMOP CDM loads about 1.9k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 187 tokens; SKILL.md has 709 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~187

When it runs · the whole SKILL.md, loaded when a task matches

~1.9k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 709 words, ~1,895 tokens.

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

metadata.category

analytics-reporting

metadata.pairs

after

metadata.version

1.0

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),
}

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/etl-to-omop-cdm of maziyarpanahi/openmed.

• SKILL.md
• references/omop_cdm_v5_4_fields.md

Open the folder on GitHubat commit 34d7b8c