Healthcare Data Dictionary ISO-11179 Standard Terms

Healthcare Data Dictionary — ISO-11179 Standard Terms A sample of 5,000 standardized healthcare data column names, abbreviations, and definitions for data engineers. Built for teams working with dbt, Snowflake, Databricks, and BigQuery on healthcare data pipelines. WHAT'S INCLUDED: Domain Coverage:- Claims (1,000 terms): ICD-10, CPT, EDI 837/835, adjudication- Clinical (1,000 terms): EHR, LOINC, SNOMED, patient care- Member (1,000 terms): Enrollment, eligibility, demographics- Pharmacy (1,000 terms): NDC, dispensing, PBM, RxNorm- Provider (1,000 terms): NPI, credentialing, taxonomy, networks Each row includes:- term: Full descriptive name (e.g. "claim paid amount")- abbr: ISO-11179 abbreviation (e.g. "clm_pd_amt")- category: Healthcare domain- definition: Definition (truncated — full at mdatool.com) WHAT IS ISO-11179? ISO-11179 is the international standard for naming data elements. It defines a predictable structure:[object class] + [property] + [representation] Examples:- mbr_birth_dt — member birth date- clm_pd_amt — claim paid amount- prvdr_npi — provider NPI number- diag_cd — diagnosis code- rx_fill_dt — prescription fill date Standard suffixes tell you the data type:_dt = date, _amt = money, _cd = code,_id = identifier, _nbr = number, _nm = name,_flg = boolean, _cnt = count USE CASES: For Data Engineers:- Standardize column names in Snowflake/BigQuery/Databricks- Build consistent data dictionaries across teams- Reference for healthcare schema design- dbt project naming standards For Data Scientists / NLP:- Train models on healthcare terminology- Build healthcare entity recognition- Map clinical concepts to standard abbreviations- Healthcare text normalization For Analytics Teams:- Understand healthcare data column names- Build self-documenting schemas- Onboard new team members faster SAMPLE QUERIES: Python:import pandas as pddf = pd.read_csv("healthcare-dictionary-sample.csv") # Find standard abbreviationresult = df[df['term'].str.contains('claim paid', case=False)] # Get all pharmacy termspharmacy = df[df['category'] == 'pharmacy'] # Find all date columnsdates = df[df['abbr'].str.endswith('_dt')] FULL DATASET: This is a 5,000 term sample. The complete dictionary includes:- 100,000+ terms across 13 healthcare domains- Full definitions (200-400 words each)- Free access at mdatool.com/glossary RELATED RESOURCES: Full Dictionary: mdatool.com/glossaryFree Tools: mdatool.com/toolsAI Data Modeling: mdatool.com/tools/modelingdbt Package: github.com/smudvar/dbt-healthcare-standardsNPI Lookup: mdatool.com/tools/npi-lookup Built by mdatool — The Healthcare Data Dictionary for dbt, Snowflake, Databricks, and BigQuery. AI Data Modeling