Expected Output

condition_occurence.tsv

The condition_occurrence.tsv file is an OMOP CDM-formatted output file that contains diagnoses and conditions assigned to patients. These conditions are typically derived from clinical encounters, EHRs, claims data, or self-reported conditions.

Examples of conditions stored in this table:

• Acute conditions (e.g., COVID-19, Influenza).
• Chronic diseases (e.g., Diabetes, Hypertension).
• Injuries (e.g., Fractures, Burns).

Column description

Mapping

The rules file defined which input fields should be mapped to condition occurrences, for example:

{
  "condition_occurrence": {
    "diagnosis": {
      "source_field": "diagnosis_code",
      "source_table": "Encounters",
      "term_mapping": {
        "J12.82": 254761,
        "E11.9": 201826,
        "I10": 316866
      }
    }
  }
}

The source dataset contained condition data in ICD-10 format. The tool extracted these values and converted them into standardized OMOP concept IDs.

measurement.tsv

The measurement.tsv file is an OMOP CDM-formatted output file that contains clinical measurements and test results recorded for patients. Measurements include lab tests, vital signs, and other numeric assessments.

• Examples of measurements stored in this table:
  • Laboratory values (e.g., Hemoglobin A1c, Creatinine).
  • Vital signs (e.g., Blood pressure, Heart rate).
  • Other clinical assessments (e.g., BMI, Cholesterol levels)

Column description

Mapping

• The rules file (rules_14June2021.json) defined which input fields should be mapped to measurement values.
• Example mapping rule:

{
  "measurement": {
    "lab_test": {
      "source_field": "test_code",
      "source_table": "LabResults",
      "term_mapping": {
        "789-8": 37398191,
        "8480-6": 40762499,
        "17856-6": 3020891
      }
    }
  }
}

• This rule maps LOINC lab test codes (e.g., “789-8” for Hemoglobin A1c) to OMOP concept IDs.

observation.tsv

The observation.tsv file is an OMOP CDM-formatted output file that contains non-clinical or miscellaneous patient observations. Observations in OMOP CDM typically represent facts about a person that do not fall under measurements, conditions, or procedures.

• Examples of observations:
  • Social determinants of health (e.g., race, smoking status).
  • Family medical history.
  • Self-reported conditions.
  • Survey responses.

Columns description

Mapping

The rules file defined which input fields should be mapped to observations:

{
  "observation": {
    "race": {
      "source_field": "race",
      "source_table": "Demographics",
      "term_mapping": {
        "Asian": 35825508,
        "Black": 8515,
        "White": 8527
      }
    }
  }
}

This rule maps race categories (Asian, Black, White) to OMOP concept IDs. The Demographics.csv file contained race data and the tool extracted the race field and converted it into standard OMOP codes.

person_ids.tsv

The Person ID Mapping Table is a lookup table that maps original subject IDs from the source system to standardized OMOP person_ids. This transformation ensures that all records referring to the same individual use a consistent identifier, facilitating data integration across multiple tables.

Why is this necessary?

• Many datasets use hashed or encrypted subject IDs that are not sequential or standardized.
• The OMOP CDM requires numeric person_ids to maintain consistency across tables like condition_occurrence, measurement, and observation`.
• This mapping ensures that every source subject ID is assigned a unique person_id.

Column description

Mapping

The tool scanned input datasets (e.g. Demographics.csv) and collected unique subject identifiers. Each unique SOURCE_SUBJECT was assigned a numeric TARGET_SUBJECT (OMOP person_id), ensuring consistency across tables. The assigned person_ids were propagated across all transformed OMOP CDM tables (condition_occurrence.tsv, measurement.tsv, etc.).

person.tsv

The person.tsv file in OMOP CDM format contains demographic and personal information about each individual in the database.

Column description

Mapping

The rules file defines how the fields from the input person.tsv map to OMOP concepts for gender, race, and ethnicity. The key mappings are:

{
  "person": {
    "gender": {
      "source_field": "gender_source_value",
      "source_table": "person",
      "term_mapping": {
        "M": 8507,   // Male mapped to concept ID 8507
        "F": 8532    // Female mapped to concept ID 8532
      }
    },
    "race": {
      "source_field": "race_source_value",
      "source_table": "person",
      "term_mapping": {
        "Asian": 35825508,
        "Black": 8515,
        "White": 8527
      }
    },
    "ethnicity": {
      "source_field": "ethnicity_source_value",
      "source_table": "person",
      "term_mapping": {
        "Hispanic": 38003563,
        "Non-Hispanic": 38003564
      }
    }
  }
}

summary.tsv