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Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) – descriptive statistics about an OMOP CDM V5 database.

See Atlas Data Sources Interactive Demo


ACHILLES is a platform which enables the characterization, quality assessment and visualization of observational health databases. ACHILLES provides users with an interactive, exploratory framework to assess patient demographics, the prevalence of conditions, drugs and procedures, and to evaluate the distribution of values for clinical observations.

Scope and purpose

ACHILLES is intended to be implemented by organizations that have patient-level observational health databases available in their local environment. By itself, ACHILLES does not perform study-specific analysis, but can assist exploring the contents of a CDM database to determine whether data exists that can support a study.

ACHILLES consists of:

  1. Pre-computations (for database characterization)
  2. Achilles Heel for data quality
  3. Index generation for better performance with Atlas Data Sources


1. Atlas Data Sources, which provides a series of dashboards to help view the Achilles and Achilles Heel results.

2. Achilles Heel Results Viewer, an RShiny application which helps ETL users investigate Achilles Heel issues.

Git Repository

All source code and installation instructions available on Github:

Any bugs/issues/enhancements regarding Achilles Analysis should be posted to the Github repository:

Get Involved

If interested in helping develop the next generation of ACHILLES, join the Achilles 2.0 Working Group:


Please follow the instructions posted here:

A vignette about how to run ACHILLES is available here:

documentation/software/achilles.txt · Last modified: 2019/02/19 22:15 by ajit_londhe