If you've ever inherited a healthcare database with columns named DOB, PatientID, or CLAIM_NUMBER — this guide is for you.

Healthcare data engineering has a naming problem. Every team, every vendor, every health plan names their columns differently. A "member ID" becomes MemberID in one system, mem_id in another, PATIENT_KEY in a third, and mbr_identifier in a fourth.

When you try to join these systems — and you always have to join them — you spend more time figuring out what columns mean than actually building the pipeline.

There's a standard that solves this. Most healthcare data engineers have never heard of it.

What is ISO-11179?

ISO-11179 is an international standard for naming data elements. It was developed specifically to make data interoperable across systems — which is exactly what healthcare data engineering needs.

The core idea is simple: every column name should follow a predictable structure:

So instead of DOB you get mbr_birth_dt.
Instead of CLAIM_NUMBER you get clm_nbr.
Instead of PatientID you get pt_id.

Every name tells you exactly what it is, what entity it belongs to, and what type of value to expect — without opening a data dictionary.

You can look up any of these standard abbreviations in the mdatool healthcare data glossary — it has 100,000+ standardized healthcare terms with their ISO-11179 abbreviations.

The Rules

1. Lowercase snake_case only

-- Wrong
MemberBirthDate
MEMBER_BIRTH_DATE
memberBirthDate

-- Right
mbr_birth_dt

Snowflake, BigQuery, and Databricks all handle lowercase snake_case consistently. Mixed case causes headaches with case-sensitive databases and makes joins fragile.

2. Standard abbreviations not made-up acronyms

This is where most teams go wrong. They abbreviate inconsistently:

-- Same concept, 4 different names on 4 tables
dob
birth_dt
patient_birth_date
DOB_DT

ISO-11179 gives you a fixed vocabulary. Learn it once, use it everywhere. The mdatool naming auditor checks your column names against this standard automatically.

3. Standard suffixes tell you the data type

This is the most powerful part of the standard. The suffix tells you exactly what kind of value to expect:

When you see clm_pd_amt you immediately know:

• clm = claim
• pd = paid
• amt = monetary amount (NUMBER type, probably NUMBER(12,2))

No documentation required.

Standard Prefixes by Healthcare Domain

Claims

clm_id          -- claim identifier
clm_nbr         -- claim number (business key)
clm_pd_amt      -- claim paid amount
clm_alwd_amt    -- claim allowed amount
clm_chrg_amt    -- claim charge amount
clm_ded_amt     -- claim deductible amount
clm_coins_amt   -- claim coinsurance amount
clm_pd_dt       -- claim paid date
clm_rcvd_dt     -- claim received date
clm_sts_cd      -- claim status code
clm_typ_cd      -- claim type code

Browse all claims data terms →

Member / Enrollment

mbr_id          -- member identifier
mbr_nbr         -- member number
mbr_first_nm    -- member first name
mbr_last_nm     -- member last name
mbr_birth_dt    -- member birth date
mbr_gndr_cd     -- member gender code
mbr_eff_dt      -- member effective date
mbr_term_dt     -- member termination date
subscr_id       -- subscriber identifier
subscr_grp_nbr  -- subscriber group number

Browse all member enrollment terms →

Provider

prvdr_id        -- provider identifier
prvdr_npi       -- provider NPI number
prvdr_nm        -- provider name
prvdr_first_nm  -- provider first name
prvdr_last_nm   -- provider last name
prvdr_tax_cd    -- provider taxonomy code
prvdr_eff_dt    -- provider effective date
prvdr_term_dt   -- provider termination date

Browse all provider data terms →

Clinical

pt_id           -- patient identifier
diag_cd         -- diagnosis code
proc_cd         -- procedure code
enc_id          -- encounter identifier
admn_dt         -- admission date
dsch_dt         -- discharge date
icd_ver_cd      -- ICD version code

Browse all clinical data terms →

Pharmacy

rx_id           -- prescription identifier
ndc_cd          -- NDC drug code
rx_fill_dt      -- prescription fill date
days_sup_cnt    -- days supply count
qty_disp_cnt    -- quantity dispensed

Browse all pharmacy data terms →

The Most Common Naming Mistakes

Here's what I see constantly in healthcare data warehouses — and the correct ISO-11179 equivalent:

Not sure what the right name is for a column? The mdatool name generator generates ISO-11179 standard names from a plain English description.

A Real Example — Before and After

Here's a typical claims table I've encountered in the wild:

-- Before (nightmare to work with)
CREATE TABLE Claims (
  ID              INT,
  ClaimNum        VARCHAR(20),
  MemberID        VARCHAR(20),
  DOB             DATE,
  ProviderNPI     VARCHAR(10),
  DOS             DATE,
  BilledAmt       DECIMAL(12,2),
  PaidAmt         DECIMAL(12,2),
  AllowedAmt      DECIMAL(12,2),
  StatusCode      VARCHAR(3),
  PaidDate        DATE,
  ReceivedDate    DATE
);

And the same table following ISO-11179:

-- After (self-documenting)
CREATE TABLE fact_claim (
  clm_id          VARCHAR(20)      NOT NULL,
  clm_nbr         VARCHAR(20)      NOT NULL,
  mbr_id          VARCHAR(20)      NOT NULL,
  mbr_birth_dt    DATE,
  prvdr_npi       VARCHAR(10)      NOT NULL,
  svc_dt          DATE             NOT NULL,
  clm_chrg_amt    NUMBER(12,2),
  clm_pd_amt      NUMBER(12,2),
  clm_alwd_amt    NUMBER(12,2),
  clm_sts_cd      VARCHAR(3),
  clm_pd_dt       DATE,
  clm_rcvd_dt     DATE
);

The second version tells you everything you need to know without a single comment or data dictionary lookup. A new engineer joining your team can read this schema and immediately understand what each column represents.

Want to generate a schema like this automatically for any healthcare domain? Try the mdatool AI data modeling tool — it generates production-ready DDL with ISO-11179 column names for Snowflake, BigQuery, Databricks, and more.

Why This Matters Beyond Aesthetics

Consistent naming isn't just clean code — it has real business impact in healthcare:

1. Faster onboarding
New data engineers can understand your schema in hours instead of weeks. In healthcare IT where turnover is high this matters enormously.

2. HEDIS and CMS reporting accuracy
When your column names clearly indicate what a field contains you're less likely to join on the wrong field or use the wrong date in a quality measure denominator. Wrong HEDIS rates have real financial consequences.

3. Easier compliance audits
HIPAA auditors reviewing your data lineage want to understand what data flows where. A self-documenting schema makes this dramatically easier.

4. Multi-system integration
Healthcare data comes from Epic, Cerner, Availity, Change Healthcare, and dozens of other systems. When every team follows the same naming standard integration becomes mapping instead of archaeology.

How to Audit Your Existing Schema

If you have an existing schema that doesn't follow these conventions here's a SQL query to find the worst offenders:

-- Find columns that likely violate naming standards
SELECT 
  table_name,
  column_name,
  data_type,
  CASE
    WHEN column_name != LOWER(column_name) 
      THEN 'Has uppercase letters'
    WHEN column_name NOT LIKE '%\_%' 
      THEN 'No underscore separator'
    WHEN column_name IN (
      'id','date','name','status',
      'code','flag','amount','number'
    )
      THEN 'Too generic — no entity prefix'
    WHEN LENGTH(column_name) < 4 
      THEN 'Too short — likely an abbreviation'
    ELSE 'Check manually'
  END as issue
FROM information_schema.columns
WHERE table_schema = 'your_schema'
  AND (
    column_name != LOWER(column_name)
    OR column_name NOT LIKE '%\_%'
    OR LENGTH(column_name) < 4
  )
ORDER BY table_name, column_name;

Or skip the manual work and paste your DDL into the mdatool naming auditor — it checks against ISO-11179 healthcare standards automatically and flags every violation with a suggested fix.

Start Small

You don't have to rename everything at once. Start with:

1. All new tables follow ISO-11179 from day one
2. Any table you're modifying — rename columns in that PR
3. Create views with standard names over legacy tables

Six months from now your new engineers will thank you. Your HEDIS submissions will be cleaner. Your CMS audits will be smoother.

And you'll never spend 20 minutes figuring out whether DOS means "Date of Service" or "Denial of Service" again.

Resources mentioned in this article:

• mdatool Healthcare Data Glossary — 100,000+ terms with ISO-11179 abbreviations
• Naming Auditor — audit your schema against healthcare standards
• Name Generator — generate standard column names from plain English
• AI Data Modeling — generate production-ready DDL with standard naming
• DDL Converter — convert schemas between Snowflake, BigQuery, Databricks

mdatool is a free platform for healthcare data engineers. No account required for most tools.