Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research

Author:

Schuemie Martijn123,Reps Jenna124,Black Adam15,Defalco Frank12,Evans Lee16,Fridgeirsson Egill14,Gilbert James P.12,Knoll Chris12,Lavallee Martin17,Rao Gowtham A.12,Rijnbeek Peter14,Sadowski Katy18,Sena Anthony124,Swerdel Joel12,Williams Ross D.14,Suchard Marc139

Affiliation:

1. Observational Health Data Science and Informatics, New York, NY, USA

2. Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA

3. Department of Biostatistics, UCLA, Los Angeles, CA, USA

4. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands

5. Odysseus Data Services Inc., Cambridge, MA, USA

6. LTS Computing LLC, West Chester, PA, USA

7. Virginia Commonwealth University, Richmond, VA, USA

8. TrialSpark Inc., New York, NY, USA

9. VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City, UT, USA

Abstract

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.

Publisher

IOS Press

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