#OHDSI

HADES (Health Analytics Data-to-Evidence Suite) is the OHDSI bundle of R packages for Patient-Level Estimation, Patient-Level Prediction, Characterization, and Self-Controlled Case Series. This article walks from install to publishing a network study.

Duy TranMay 7, 202616 min

ATLAS is the official OHDSI cohort builder; Data Quality Dashboard runs more than 3,000 quality checks; ACHILLES profiles each CDM. This article walks through installing Broadsea, defining cohorts, reading DQD, and acting on results.

Duy TranMay 7, 202614 min

ETL from a HIS/EHR/claims source to OMOP CDM takes 3-6 months from scratch. This article walks through the OHDSI standard pipeline: WhiteRabbit profiling, RabbitInAHat design, USAGI mapping, implementation with SQL/Perseus/dbt, and validation with DQD.

Duy TranMay 7, 202618 min

Vocabulary is the hardest but most important part of OMOP. This article explains Concepts, Standard vs. Source, Domain, Vocabulary, ConceptRelationship, and ConceptAncestor — plus the Athena download/lookup workflow for Vietnamese projects.

Duy TranMay 7, 202616 min

Real-World Evidence (RWE) is changing how the FDA, EMA, and other regulators make decisions. OMOP CDM is the data standard that lets you run a single study across hundreds of organizations at once. This article introduces OHDSI, CDM 5.4, and the Vietnam context.

Duy TranMay 7, 202614 min

The most comprehensive series for beginners to learn OMOP Common Data Model version 5.4. From the basic concept of medical data standardization, Person-centric architecture, 37 data tables (Clinical Data, Health System, Health Economics, Standardized Vocabularies, Derived Elements, Metadata), to Concept/Vocabulary systems, ETL processes, and OHDSI ecosystem tools. Each lesson has real-life examples from Vietnamese hospitals, intuitive diagrams, and hands-on SQL exercises.

Comprehensive series on the OHDSI (Observational Health Data Sciences and Informatics) ecosystem and OMOP Common Data Model — from platform overview, Standardized Vocabularies (Athena), medical data ETL (WhiteRabbit, Rabbit-in-a-Hat, Usagi), OMOP CDM implementation on PostgreSQL, WebAPI and ATLAS installation, to clinical data analysis (Cohort Definitions, Characterization, Incidence Rates, Population-Level Estimation, Patient-Level Prediction), data quality assessment (ACHILLES, Data Quality Dashboard), HADES R packages for observational studies, and OHDSI stack deployment on Docker/Kubernetes for multicenter Network Studies.