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

• Published in: Studies in health technology and informatics Vol. 310; p. 966
• Main Authors: Schuemie, Martijn, Reps, Jenna, Black, Adam, Defalco, Frank, Evans, Lee, Fridgeirsson, Egill, Gilbert, James P, Knoll, Chris, Lavallee, Martin, Rao, Gowtham A, Rijnbeek, Peter, Sadowski, Katy, Sena, Anthony, Swerdel, Joel, Williams, Ross D, Suchard, Marc
• Format: Journal Article
• Language: English
• Published: Netherlands 25.01.2024
• Subjects: Data Science; Databases, Factual; Electronic Health Records; Humans; Observational Studies as Topic; Reproducibility of Results; Software; epidemiology; Observational research; open-source; software; machine learning
• ISSN: 1879-8365
• DOI: 10.3233/SHTI231108

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.