With Syntropy, health systems and life science organizations gain unparalleled capabilities to optimize and answer healthcare's toughest problems through intelligent and data-driven solutions.
Unlock the potential of your data by integrating proprietary and third-party models, collaborating on a single platform to build new models, and training models with trustworthy data, complete with transparency from source to execution.
Healthcare organizations often struggle to extract meaningful insights from the vast and complex landscape of medical data stored in various formats and systems. Without an ontology serving as a common language and structural framework, ensuring consistent categorization, relationships, and definitions of data can be exceedingly challenging.
Syntropy provides a dynamic platform that leverages common ontologies that simplify data management. With semantic consistency of data objects and their relationships, researchers, analysts, and data scientists can seamlessly collaborate with the most accurate data, at the right time, with the right context, leading to more informed and strategic decisions/outcomes.
Medical data is often stored in various systems with differing semantics, making it difficult to aggregate data for analysis. An ontology in healthcare serves as a structured and standardized framework, acting as a shared source of truth to help understand vast and complex medical concepts and the relationships between them.
Providing a common language and structure, an ontology ensures that all medical data is categorized, related, and defined cohesively. This semantic consistency is vital for deep analysis in research. With a shared understanding of concepts and terminology, an ontology enables healthcare organizations to extract valuable insights.
In the Syntropy platform, the creation of an ontology is a multi-layered process that begins with integrating various data sources into a real-time, interactive view of the core entities and relationships relevant to an organization. This includes generating object and model-derived properties for richer semantic detail.
Next, dynamic behavior is represented, linking semantic objects and actions, enabling AI-guided workflows and real-time process monitoring. Then, models are bound to objects and actions, allowing them to reason across variables, facilitating exploration of various scenarios and creating a robust foundation for data-driven insights and research optimization.
Accommodate new data sources and use cases as your needs evolve, ensuring value over time
Adapt to different types of data and use cases to meet the specific needs of your organization
Simplify harmonization, aligning with industry standards tailored to your specific requirements
Deploy your data models to make system-wide decisions to enable insightful feedback loops
Harness the full potential of your machine learning models, optimizing performance and facilitating rapid adaptation to the evolving needs in healthcare research.
Increase long-term value and scalability of your AI/ML models by adapting models for versatile application across different use cases.
Collect feedback from researchers, analysts, data scientists, and performance data to iteratively improve models over time.
Enhance predictive capabilities and reduce the need for manual intervention, ensuring that AI/ML models remain accurate and relevant.