Prometheus Federated RWE Analytics Platform Federated Data Network Platform
Prometheus Federated RWE Analytics Platform
Solution Overview
Prometheus Federated RWE Analytics Platform is a federated data analytics and data network platform developed by EPAM, enabling collaborative research powered by open-source analytics standards, designed to support the healthcare industry in generating real-world evidence.
Researchers can analyze data from multiple sources without transferring or pooling it, which makes analysis faster and more secure. Private or public data networks let teams securely share insights, code and results, and a scalable execution engine runs complex analyses on large datasets.
The platform is compatible with OHDSI ATLAS and supports both OHDSI and non-OHDSI. A modern user interface and a centralized environment orchestrate the study workflow from start to finish. A data catalog provides dataset characteristics, quality reports and usage metrics.
Enterprise-grade security and multi-level access controls protect data privacy.
Customer Challenge
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Data is limited within individual organizations with insufficient access to networks.
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Data is not aggregated/federated, leading to issues with compliance.
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Data is not downloadable and/or reproducible, making submission to regulatory bodies challenging.
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Limited access to productivity features and shortcuts slows time to solution.
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Siloed research often results from a lack of study collaboration, artifact sharing and reuse capabilities.
EPAM Solution
Prometheus Federated RWE Analytics Platform is a powerful and versatile platform that can enable collaborative research and data analytics in the healthcare industry. Its comprehensive features, compatibility with industry standards, and focus on security and collaboration make it a valuable tool for researchers and healthcare organizations seeking to leverage the power of data to improve patient outcomes.
1/5
Key Differentiators
Taxonomy-Driven Integrated Vocabularies
Support OMOP CDM standard and custom vocabularies
Comprehensive Data Catalog
Artifact library, metadata, data characterization and pluggable analytics
Benefits
Improved Productivity
Advanced UI/UX, artifact reuse and gen-AI for efficiency
High Research Quality
Open data analytics standards, results reproducibility
Improved Collaboration
Secure platform for federated data access and analysis
Enhanced Data Security
Enterprise-grade protection and tiered access controls
Features
- Federated Data Analytics: Enables researchers to analyze data from multiple sources without needing to transfer or pool the data.
- Data Network Support: Supports the creation of private or public data networks, enabling researchers to collaborate and share artifacts, code and results securely.
- OHDSI ATLAS Compatibility: Fully compatible with the OHDSI ATLAS tool, allowing researchers to collaborate and exchange designs with ATLAS users.
- OMOP and Non-OMOP Support: Supports both the OMOP common data model and non-OMOP data, providing flexibility for researchers working with different data formats.
- Scalable Analytics Execution: Provides a scalable execution engine that supports R and OHDSI HADES, allowing researchers to perform complex analyses on large datasets.
- Collaboration Tools: Offers a suite of collaboration features, including a modern user interface, and a centralized environment supporting an end-to-end study workflow orchestration.
- Data Catalog: Includes a data catalog, database characterization and quality reports and usage statistics.
- Security and Access Controls: Enterprise-grade security and multi-level access controls to ensure data privacy and security.
Use Cases
Healthcare Organization
Problem Statement
The analysis of health data is significantly hampered by data heterogeneity, vocabulary inconsistencies, a lack of standardized tools, visualization difficulties and collaboration barriers stemming from security and workflow issues
Solution Proposed
Implemented Prometheus Federated RWE Analytics Platform as the federated data analytics and data network platform, leveraging collaboration features to facilitate teamwork and data sharing
Achieved Results
Established the framework of a robust network that enabled standardized and collaborative research across the organization, improving data quality and consistency
Large Volume Data Standardization
Problem Statement
Large-volume data conversions of multiple databases require specialist knowledge and experience with lengthy processes
Solution Proposed
Implemented and maintained the open-source data analytics platform including designing the hosting infrastructure
Achieved Results
Standardized RWE data analytics environment at scale, providing lifecycle maintenance to multiple datasets
Questions & Answers
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