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.
Generic data models fall short in addressing the distinct needs of individual organizations, especially in the healthcare industry. Syntropy solves this problem by empowering customers to bring their own, bespoke data models.
Through a flexible and open platform, organizations can seamlessly incorporate both proprietary models that currently inform business decision and third-party models to evaluate efficacy and performance. With multiple integration options such as APIs, third-party connectors, and diverse file formats, Syntropy supports all major open-source frameworks such as FHIR, OMOP, and more.
Data models built with a diverse range of perspectives from multiple contributors lead to more accurate outcomes through the incorporation of varied insights, mitigating biases, and capturing a broader understanding of the subject matter. However, the challenge lies in auditing model integrity.
With Syntropy, data scientists and researchers can collaborate seamlessly within a single platform to construct and deploy data models. Syntropy's comprehensive lineage records every change made to a model and links it to the corresponding user, providing complete transparency into the modification process. Additionally, Syntropy allows for easy rollback to previous versions with a click of a mouse if changes are inaccurate.
Adapting patient data to various industry standards without disrupting existing data pipelines and related analytics/reporting presents a significant challenge due to the complexity, potential compromises in data integrity, interoperability issues, compliance hurdles, risk of inaccuracy, and substantial time and effort required.
Syntropy offers a practical solution to this challenge by streamlining the process of evolving data models for specific use cases. The platform empowers health systems and life science organizations to efficiently adjust their data structure, saving valuable time and resources while ensuring that they can effectively meet the changing demands of research in their fields without disrupting existing data pipelines and analytics.
Ontology
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.
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