The chronic pain research community is no stranger the challenges posed by poor-quality data, despite the substantial disease and societal burden it represents. Issues such as lack of provider knowledge, resistance to exploring beyond the biomedical model, inconsistent and ambiguous terminology, fragmented and siloed data sources, systemic underreporting, and a lack of standardized assessment and documentation practices (to name a few) create a ‘bad data tax.’ This hidden cost hinders progress, limits innovation, and exacerbates inequities.
Knowledge graphs (KGs) offer a scalable solution to these challenges. By organizing, integrating, and standardizing fragmented data into an interconnected framework of entities, relationships, and attributes, KGs create a cohesive structure. Applied to chronic pain, KGs provide several advantages such as:
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Semantic Interoperability: They enable systems to exchange data with unambiguous, shared meanings, preserving context.
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Linguistic and Cultural Flexibility: KGs accommodate nuanced expressions of concepts, allowing for better global applicability.
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Scalability and Adaptability: The technology grows and evolves, repurposing knowledge for new use cases and new knowledge as they emerge.
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Enhanced Insight Generation: By linking diverse data sources, KGs uncover patterns and insights previously unattainable, paving the way for advanced, context-driven analytics.
As a community, let’s rethink our data management practices and consider establishing a Graph Center of Excellence to address the ‘bad data tax’ and drive innovation in pain research.
I’d love to hear your thoughts—what do you think of graph technology, what potential use cases can you think of where knowledge graphs could be beneficial, what challenges or opportunities do you see?
White Paper: Modernizing Your Data Strategy with a Graph Center of Excellence