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What is OMOP?

OMOP (Observational Medical Outcomes Partnership) is a Common Data Model (CDM) designed to standardize the structure and content of clinical and administrative data across different healthcare systems. It was developed within the framework of the OHDSI (Observational Health Data Sciences and Informatics) initiative, a global consortium that promotes large-scale health data analysis.

Key Features of OMOP:

• Standardization: Converts health data from various sources (electronic health records, insurance claims, clinical trial registries, etc.) into a common format.
• Interoperability: Facilitates data analysis across multiple institutions without the need to share original data.
• Common Terminologies: Uses standardized vocabularies such as SNOMED CT, LOINC, RxNorm, and others.
• Analytical Capability: Enables the application of reproducible tools and analysis methods for epidemiological studies, pharmacovigilance, and comparative effectiveness research.

How to Transform Existing Data to OMOP?

To transform existing clinical data into the OMOP Common Data Model (CDM) structure, the typical process includes the following steps:

1. Evaluation of Current Data

• Identify existing data sources (electronic health records, claims, laboratory records, etc.).
• Review the data structure and compare it with OMOP tables and standards.
• Determine whether the data uses proprietary terminologies or standards like SNOMED CT, LOINC, RxNorm, etc.

2. Mapping to the OMOP Structure

• OMOP defines a series of tables with specific structures (e.g., PERSON, CONDITION_OCCURRENCE, DRUG_EXPOSURE, etc.).
• Each data element must be mapped to the correct OMOP table and field.
• This mapping can be done manually or with tools like WhiteRabbit and Rabbit-In-a-Hat from OHDSI.

3. Terminology Conversion

• If proprietary codes or different standards are used, they need to be mapped to OMOP-standardized vocabularies (SNOMED CT, LOINC, RxNorm, etc.).
• Tools like Usagi (from OHDSI) can facilitate this mapping.

4. Data Loading into the OMOP Database

• Once the data is transformed, it is loaded into an OMOP-compatible database (such as PostgreSQL, SQL Server, etc.).
• OHDSI provides tools to verify the quality of the conversion, such as Achilles (for validation and exploratory analysis).

5. Validation and Analysis

• It is recommended to test the data using OHDSI’s analysis tools, such as ATLAS, to ensure correct structuring.
• Queries can be executed for epidemiological analyses and cohort studies.

Differences Between SNOMED CT, LOINC, and RxNorm

SNOMED CT, LOINC, and RxNorm are standardized terminologies used in healthcare, but each serves a different purpose. Below are their key differences:

SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms)

• What is it? A clinical coding system that covers medical terms and health-related concepts.
• What is it used for?
  • Medical diagnoses.
  • Clinical procedures.
  • Signs and symptoms.
  • Anatomy and clinical findings.

• Example:
  • Acute myocardial infarction → 22298006
  • Type 2 diabetes → 44054006

LOINC (Logical Observation Identifiers Names and Codes)

• What is it? A coding system designed to identify laboratory tests, clinical observations, and results.
• What is it used for?
  • Laboratory tests (e.g., blood glucose, complete blood count).
  • Clinical observations (e.g., blood pressure, temperature).
  • Medical questionnaires and assessments.
• Example:
  • Fasting blood glucose → 1558-6
  • Systolic blood pressure → 8480-6

RxNorm

• What is it? A coding system for medications that provides unique names for clinical drugs and their relationships.
• What is it used for?
  • Identification of drugs and their active ingredients.
  • Equivalencies between different brands and formulations.
  • Interoperability in electronic prescriptions.
• Example:
  • Ibuprofen 200 mg (tablet) → 198211
  • Metformin 500 mg (tablet) → 861009

Summary of Differences

Terminology	Focus	Example Usage
SNOMED CT	Diagnoses, procedures, symptoms	Diagnosis of “Type 2 diabetes” → 44054006
LOINC	Laboratory tests and observations	“Fasting blood glucose” → 1558-6
RxNorm	Medications and prescriptions	“Ibuprofen 200 mg (tablet)” → 198211

Each of these terminologies plays a key role in clinical data standardization, and they are often used together in models like OMOP to ensure interoperability in healthcare systems.