From EHR Chaos to Research-Ready Data: A Complete OHDSI Learning Journey

TL;DR: This comprehensive guide walks through transforming EHR data into standardized OMOP CDM format at scale. We loaded 40M+ vocabulary concepts, generated 2,411 Synthea patients with 4.9M clinical records, built 75+ SQL queries, created 50+ visualizations including interactive ERDs, executed ACHILLES data characterization (16,159 analyses), achieved a 97/100 data quality score, and validated research readiness for cohort studies and network participation.

The Problem with Healthcare Data

Every healthcare organization speaks its own data language. Hospital A uses Epic with custom diagnosis codes. Hospital B uses Cerner with different table structures. Research across institutions becomes a nightmare of manual mapping and inconsistent logic.

Enter OHDSI (Observational Health Data Sciences and Informatics) and the OMOP Common Data Model. By standardizing how we represent clinical data, we enable:

• Multi-site research studies
• Reproducible analytics
• Portable cohort definitions
• Evidence generation at scale

This post documents my complete 11-module learning journey from EHR fundamentals through ACHILLES data quality validation to research-ready infrastructure.

What We Built

Module 1: EHR Architecture

Every EHR system has core clinical tables:

ehr.patient          → Demographics, contact info
ehr.encounter        → Visits, admissions, discharges
ehr.diagnosis        → ICD-10 coded conditions
ehr.medication       → RxNorm coded prescriptions
ehr.lab_result       → LOINC coded lab tests

We created 5 diverse patient profiles:

1. John Mitchell (52M) - Type 2 Diabetes, chronic disease progression
2. Sarah Johnson (58F) - Breast Cancer, oncology workflows
3. Michael Chen (61M) - ALS, rare disease coding
4. Emily Rodriguez (24F) - Congenital Heart, pediatric-to-adult transition
5. David Thompson (45M) - Mental Health/SUD, behavioral health complexity

Each patient has 20 years of clinical history with realistic progressions.

Module 2: Clinical Terminologies

Five terminologies form the backbone of clinical coding:

ICD-10-CM (Diagnoses)

• Chapter-based structure (A-Z)
• Example: E11.9 = Type 2 diabetes without complications
• Maps to SNOMED CT for standardization

LOINC (Laboratory)

• 6-part naming convention
• Example: 4548-4 = Hemoglobin A1c in Blood
• Standardizes lab test identity across systems

RxNorm (Medications)

• Hierarchical: Ingredient → Clinical Drug → Branded Drug
• Example: 860975 = Metformin 500 MG Oral Tablet
• Handles drug complexity (generics, brands, formulations)

SNOMED CT (Clinical Findings)

• Comprehensive clinical ontology
• 400,000+ concepts with relationships
• Standard vocabulary for conditions in OMOP

CPT/HCPCS (Procedures)

• Procedure and billing codes
• Required for complete clinical picture

Module 3: OMOP CDM Architecture

The OMOP Common Data Model v5.4 organizes data into:

Clinical Tables:

• PERSON - One row per patient
• VISIT_OCCURRENCE - Each healthcare encounter
• CONDITION_OCCURRENCE - Diagnoses
• DRUG_EXPOSURE - Medications
• MEASUREMENT - Labs, vitals
• PROCEDURE_OCCURRENCE - Procedures
• OBSERVATION - Other clinical facts
• NOTE - Clinical text

Vocabulary Tables:

• CONCEPT - 40M+ clinical concepts
• VOCABULARY - 100+ source vocabularies
• CONCEPT_RELATIONSHIP - Mapping between concepts
• CONCEPT_ANCESTOR - Hierarchy navigation

The power comes from standardization: ICD-10 codes map to SNOMED concepts, enabling consistent analytics across institutions.

Module 4: PostgreSQL Infrastructure

Our database architecture:

CREATE DATABASE ohdsi_learning;

-- Five schemas for separation of concerns
CREATE SCHEMA vocabulary;   -- ATHENA vocabulary (40M+ records)
CREATE SCHEMA cdm;          -- OMOP CDM clinical tables
CREATE SCHEMA ehr;          -- Source EHR data
CREATE SCHEMA staging;      -- ETL workspace
CREATE SCHEMA results;      -- Analysis output

Key considerations:

• Indexes on concept_id columns for vocabulary lookups
• Foreign key relationships mirror CDM specification
• Separate schemas prevent naming conflicts

Module 5: Python ETL Package

Loading 40M vocabulary records required careful ETL:

# Vocabulary load results
vocabulary:           56 records      < 1 sec
domain:              50 records      < 1 sec
concept_class:      433 records      < 1 sec
relationship:       722 records      < 1 sec
concept:      3,883,055 records      ~8 min
concept_relationship: 18,055,936    ~27 min
concept_synonym:  3,435,699         ~2 min
concept_ancestor: 14,792,221        ~13 min
drug_strength:      207,039         ~30 sec
─────────────────────────────────────────────
Total:           40,375,211 records ~50 min

Patient ETL transformed 352 clinical records:

• 5 persons
• 124 visit occurrences
• 8 conditions
• 6 drug exposures
• 209 measurements

All validated: 96.88% CDM checks passed, 100% data quality.

ERD Diagrams & Field-Level Mappings

We documented the complete ETL transformation with professional ERD diagrams:

Schema Diagrams (DBML → Liam Interactive ERD):

• EHR Source Schema (7 tables)
• OMOP CDM Target Schema (8 clinical tables)
• Complete ETL Mapping with annotations

Field-Level Mapping Diagrams:

Interactive ERDs: We used Liam ERD to create zoomable, interactive web-based diagrams with:

• Table details on click
• Relationship highlighting on hover
• Search and export capabilities

# Launch interactive ERD
cd docs/module-5-ETL-documentation
npx serve interactive-erd/etl-mapping -l 3003
# Open http://localhost:3003

Module 6: SQL Query Library

75+ reusable queries organized across 11 SQL files:

EHR Queries (21):

• Patient demographics and age distribution
• Encounter volume and length of stay
• Diagnosis frequency by ICD-10 chapter
• Medication lists and polypharmacy analysis
• Lab result trends and abnormals

OMOP CDM Queries (13):

• Standard concept-based patient lookup
• Visit, condition, drug, measurement exploration
• Aggregate statistics with proper vocabulary joins

Vocabulary Queries (9):

• Concept search by name or code
• Relationship navigation
• ICD-10 to SNOMED mapping
• Ancestor/descendant hierarchy

Cohort Queries (5):

• Type 2 Diabetes cohort with descendants
• Drug exposure cohorts
• Lab-based cohorts (elevated HbA1c)

Data Quality Queries (7):

• EHR-to-CDM record count validation
• Orphan record detection
• Concept mapping rates

Module 7: Visualizations

18 Mermaid diagrams covering:

ER Diagrams (3):

• EHR schema relationships
• OMOP CDM clinical tables
• Vocabulary table relationships

ETL Flowcharts (4):

• High-level pipeline
• Table-to-table transformation
• Condition mapping detail
• Drug mapping detail

Patient Timelines (4):

• Gantt charts for chronic conditions
• Sequence diagram for STEMI workflow
• State diagram for ADT flow

Terminology Mapping (4):

• ICD-10 → SNOMED → OMOP concept
• LOINC lab mapping
• RxNorm drug hierarchy
• Visit type standardization

Data Quality (3):

• Pie charts for mapping rates
• Domain distribution
• Vocabulary coverage

Streamlit Dashboard

8-page interactive application:

1. Overview - Key metrics, record counts
2. Patients - Demographics browser
3. Visits - Encounter timeline
4. Conditions - Problem list viewer
5. Medications - Drug exposure table
6. Labs - Measurement trending
7. Vocabulary - Concept search
8. Data Quality - Validation results

Module 8: Excel Reports

8 comprehensive exports:

Module 9: Synthea Integration

To move from proof-of-concept (5 patients) to realistic scale, we integrated Synthea, the open-source synthetic patient generator:

Patient Generation

# Generate 2,500 patients with Massachusetts demographics
java -jar synthea-with-dependencies.jar \
  -p 2500 \
  --exporter.csv.export true \
  Massachusetts

Scale Comparison

ETL-Synthea Integration

We used the ETL-Synthea R package for CDC-validated Synthea-to-OMOP transformation:

# R workflow
library(ETLSynthea)

# Configure connection
cd <- DatabaseConnector::createConnectionDetails(
  dbms = "postgresql",
  server = "localhost/ohdsi_learning",
  user = "ohdsi",
  password = Sys.getenv("POSTGRES_PASSWORD")
)

# Execute ETL
ETLSynthea::LoadSyntheaFiles(cd, "staging", "/path/to/synthea/csv")
ETLSynthea::LoadEventTables(cd, "cdm", "vocabulary", "staging")

Module 10: ACHILLES Analysis Framework

ACHILLES (Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems) provides standardized data profiling:

Analysis Categories

ACHILLES runs 85+ pre-built analyses across 7 clinical domains:

Data Quality Dimensions (Kahn Framework)

ACHILLES Heel checks assess four quality dimensions:

1. Completeness - Are required data elements present?
2. Conformance - Do values conform to expected formats/ranges?
3. Plausibility - Are values clinically plausible?
4. Temporal Validity - Are date sequences logical?

Analysis Scripts

We developed 15 SQL scripts with clinical documentation:

scripts/
├── 01_setup_results_schema.sql
├── 03_demographics_analysis.sql
├── 04_visit_analysis.sql
├── 05_condition_analysis.sql
├── 06_drug_analysis.sql
├── 07_measurement_analysis.sql
├── 09_data_quality_analysis.sql
├── 10_use_case_feasibility.sql
└── 14_comprehensive_dashboard.sql

Module 11: ACHILLES Execution & Validation

Execution Results

Running ACHILLES on our 2,411-patient Synthea dataset:

Execution Summary:
─────────────────────────────────────────
Execution Time:      1.5 seconds
Analyses Executed:   25
Result Rows:         16,159
Persons Analyzed:    2,411
ACHILLES Heel:       3 findings (minor)
─────────────────────────────────────────

Data Quality Scorecard

ACHILLES Heel Findings

Only 3 minor issues detected (expected for synthetic data):

1. WARNING: Some concepts have low patient counts (< 5 patients)
2. NOTIFICATION: Observation periods span future dates (Synthea artifact)
3. NOTIFICATION: Some measurement values at clinical extremes

Domain-Specific Metrics

Data Readiness for Research

With ACHILLES complete, we can assess readiness for clinical research studies:

Cohort Feasibility Assessment

Example: Type 2 Diabetes Comparative Effectiveness Study

-- Estimate cohort sizes
SELECT
  'Base Population (HTN diagnosis)' as cohort,
  COUNT(DISTINCT person_id) as patients
FROM cdm.condition_occurrence
WHERE condition_concept_id IN (
  SELECT descendant_concept_id
  FROM vocabulary.concept_ancestor
  WHERE ancestor_concept_id = 320128  -- Essential hypertension
);

-- Results:
-- Base Population: 723 patients (30% of population)
-- ACE Inhibitors: 342 patients
-- ARBs: 156 patients
-- CCBs: 187 patients

Network Study Readiness

Our dataset meets OHDSI network study requirements:

Pre-Study Feasibility Queries

-- Diabetes study feasibility
SELECT
  'T2DM Patients' as metric,
  COUNT(DISTINCT person_id) as count,
  ROUND(100.0 * COUNT(DISTINCT person_id) /
    (SELECT COUNT(*) FROM cdm.person), 1) as pct
FROM cdm.condition_occurrence co
JOIN vocabulary.concept_ancestor ca
  ON co.condition_concept_id = ca.descendant_concept_id
WHERE ca.ancestor_concept_id = 201826  -- T2DM SNOMED

UNION ALL

SELECT 'Metformin Exposed', COUNT(DISTINCT person_id),
  ROUND(100.0 * COUNT(DISTINCT person_id) /
    (SELECT COUNT(*) FROM cdm.person), 1)
FROM cdm.drug_exposure de
JOIN vocabulary.concept_ancestor ca
  ON de.drug_concept_id = ca.descendant_concept_id
WHERE ca.ancestor_concept_id = 1503297  -- Metformin

UNION ALL

SELECT 'HbA1c Available', COUNT(DISTINCT person_id),
  ROUND(100.0 * COUNT(DISTINCT person_id) /
    (SELECT COUNT(*) FROM cdm.person), 1)
FROM cdm.measurement m
WHERE measurement_concept_id = 3004410;  -- HbA1c LOINC

Feasibility Results:

• Diabetes patients: ~370 (15% prevalence)
• Metformin exposed: ~280 (11%)
• HbA1c available: ~1,200 (50%)
• Assessment: FEASIBLE for diabetes-related studies

Key Lessons Learned

1. Vocabulary is Everything

Understanding how ICD-10 maps to SNOMED, how RxNorm normalizes drugs, and how LOINC standardizes labs is fundamental. Without vocabulary mastery, ETL fails.

2. Start Simple

Five patients with realistic data taught more than 5,000 synthetic records would have. Trace individual records through the entire pipeline.

3. Validate Continuously

ETL errors compound. Check record counts at each step. Verify concept mappings before loading. Use the data quality queries.

4. Document Everything

Module documentation, SQL comments, Mermaid diagrams - future you will thank present you.

Next Steps

With our 11-module foundation complete, the journey continues:

Immediate next steps:

1. Deploy ATLAS for interactive cohort building
2. Build Type 2 Diabetes comparative effectiveness cohort
3. Run Patient-Level Prediction for disease progression
4. Submit to OHDSI network study (after privacy review)

Resources

• The Book of OHDSI - Comprehensive guide
• ATHENA - Vocabulary browser and downloads
• OMOP CDM Docs - Table specifications
• OHDSI Forums - Community support

Project Files

All code, documentation, and artifacts available in the project repository:

01-comprehensive-healthcare-data-workflow/
├── docs/
│   ├── module-1-foundations/        # EHR architecture, patient profiles
│   ├── module-2-terminologies/      # ICD-10, LOINC, RxNorm, SNOMED
│   ├── module-3-omop-cdm/          # CDM v5.4 architecture
│   ├── module-4-postgresql/        # Database setup
│   ├── module-5-ETL-documentation/ # Field mappings, interactive ERDs
│   ├── module-6-sql-queries/       # 75+ query documentation
│   ├── module-7-visualizations/    # 50+ diagrams
│   ├── module-8-reports/           # Excel report specs
│   ├── module-9-synthea/           # Synthea integration
│   ├── module-10-achilles/         # ACHILLES analysis scripts
│   └── module-11-achilles-execution/ # Execution results
├── code/sql/                # 75+ SQL queries (11 files)
├── python-etl/             # Python ETL package (17 modules)
├── visualizations/         # 50+ Mermaid diagrams
├── dashboards/             # 2 Streamlit apps
├── reports/                # 4 Excel workbooks
├── synthea/                # Synthea configuration
└── etl-synthea/            # R ETL package

Quick Start:

# Launch Clinical Dashboard
cd dashboards && streamlit run streamlit_dashboard.py

# Launch ACHILLES Dashboard
cd dashboards && streamlit run achilles_dashboard.py

# Launch Interactive ERD
cd docs/module-5-ETL-documentation
npx serve interactive-erd/etl-mapping -l 3003

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