Any healthcare data needs to be converted to a common format before it can be used for analysis. The OMOP Common Data Model (CDM) helps to organise disparate data sources into the same relational database design. However, the extract, transform and load (ETL) process can be difficult and usually requires strong technical knowledge and domain expertise.

Perseus is a software solution that simplifies CDM conversion by providing a flexible and easy-to-use visual environment. You don't need to have strong technical knowledge to convert your data to the OMOP format. Perform required transformations with a comprehensible 3-step process: pre-processing, mapping and conversion.

How It Works




Visual interface guides users through the different actions
Conversion process can be viewed in real time, making it possible to react quickly if an error occurs
Summaries and statuses can be viewed for all conversion runs


Auto mapping for similar fields that prevents users from starting from scratch every time
Auto-generated field values for the CDM fields
Users can utilise built-in source-to-source and source-to-standard vocabulary lookups


Data quality checks test whether data conform to a given requirement
App is integrated with the tools used in the Observational Health Data Sciences and Informatics (OHDSI) community, which include White Rabbit, Rabbit-in-a-Hat, Usagi, Athena and Data Quality Dashboard

Time & Cost-Effectiveness

Reduce time spent on manual processes, such as coding and mapping source data to target systems
Fewer people need to be involved in the process compared to traditional conversion projects

General Workflow



Import data assets (e.g. White Rabbit scan report or Scan DB)
Combine source tables
Review and confirm the data



Save a mapping and open an existing mapping
Create custom lookups for the concept_id fields
Map sources to the target tables / fields
Search mapping between new codes and OMOP standard concepts
Setup transformations and vocabulary lookups
Define the logic for creating eras (DRUG_ERAs, CONDITION_ERAs, etc.)
Define the logic for grouping visit occurrence / observation_period records
Create an ETL specification document



Convert source to the CDM
View progress for a conversion
View conversion run results
Export the results for offline review and further analysis

Request a demo

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