Healthcare represent some of the most complex data sets for modeling and interpretation. At QED we have embraced the these complex data relationships by embracing the FHIR standard for interoperability and extending it into our Healthcare Knowledge Graph model. These purpose driven models focus on specific Healthcare use-cases for analyzing patient data, identifying trends, and predicting outcomes. In addition, these models are integrated with standard and customized Ontologies that provide broader analytical capabilities.
With the integration of Generative AI, healthcare applications can now seamlessly access and synthesize disparate datasets for specific use cases focused on Healthcare Administration. Our RAG-based solution approach enhances transparency by providing visibility into the reasoning and source information used to make decisions. Additionally, we incorporate Human-in-the-Loop features to ensure alignment with business goals, enabling more accurate, accountable, and strategic decision-making across administrative workflows.
Our technology consulting services harness Graph RAG (Retrieval-Augmented Generation) frameworks to implement flexible and scalable Generative AI solutions. By combining foundational and open-source Large Language Models (LLMs) with dynamic retrieval workflows, we develop intelligent knowledge systems that organize and generate domain-specific content. This approach reveals hidden data relationships through knowledge graphs, enriching LLM context and meaning. The outcome is a seamless, high-performance platform that delivers relevant, actionable insights while driving strategic decisions and discovering new opportunities across various industries.
We surface ontologies from purpose-built Knowledge Graphs to derive high-level concepts and provide deeper insights from an existing graph. This new abstraction layer reveals topic coverage as well as highlighting content omissions or underrepresented areas.
By modeling these broader conceptual frameworks, organizations gain richer context and relevance for their data assets, while also providing traversal paths across datasets once thought to be unrelated. Overall these Ontology-driven graphs enhance dataset interpretability, helping identify gaps, redundancies, and emerging trends.
Integrating ontologies ensures Knowledge Graphs evolve with business needs, capturing explicit and implicit knowledge to drive innovation and informed decision-making.
Graph RAG (Retrieval-Augmented Generation) revolutionizes healthcare data analytics by facilitating precise, domain-specific knowledge retrieval for Generative AI solutions. This approach seamlessly integrates clinical data, research findings, and operational metrics into intelligent, agentic systems, enabling highly tailored responses and real-time insights. As a result, healthcare organizations gain comprehensive visibility and robust control over critical data assets, ensuring compliance, reducing costs, and driving meaningful improvements in patient care.
By adopting these agentic systems, institutions can move beyond passive data storage and instead automate the execution of key insights across diverse functions—from risk stratification to care coordination. This operational integration offers continuous, data-driven decision-making aligned with strategic objectives. Our expertise ensures clients harness the full potential of AI-driven knowledge graphs, unlocking innovation, streamlining operations, and delivering enhanced health outcomes in a rapidly evolving industry.