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

Area of Science:

• Health Informatics
• Observational Health Data Science
• Real-World Evidence Generation

Background:

• The Observational Health Data Sciences and Informatics (OHDSI) community developed the Health-Analytics Data to Evidence Suite (HADES).
• HADES is an open-source software collection designed for analyzing observational health data.
• It operates on data transformed into the Observational Medical Outcomes Partnership (OMOP) Common Data Model.

Purpose of the Study:

• To introduce and describe the capabilities of the HADES software suite.
• To highlight HADES's role in enabling advanced analytics on real-world health data.
• To showcase its utility in federated data networks and regulatory decision-making.

Main Methods:

• HADES executes advanced analytics directly on healthcare data (e.g., EHR, claims) converted to the OMOP CDM.
• It supports characterization, population-level causal effect estimation, and patient-level prediction.
• The software is designed for broad technical compatibility and utilizes continuous integration with extensive unit testing for reliability.

Main Results:

• HADES facilitates analysis across federated data networks, preserving data privacy by sharing only aggregated statistics.
• It is implemented across diverse technical environments, ensuring broad applicability.
• The suite adheres to OHDSI best practices and is integral to most published OHDSI studies.

Conclusions:

• HADES is a reliable, versatile, and open-source software suite crucial for real-world evidence generation in health informatics.
• Its application in federated networks enhances data privacy and analytical power.
• HADES plays a significant role in advancing observational health data science and has informed regulatory decisions.