What are the advantages of our approach compared to traditional guidelines? Traditional guidelines typically cover the diagnosis and management of a single disease. Knowledge graphs are more like a whole encyclopedia – a comprehensive domain model that is not single disease-specific or single drug-specific. Knowledge graphs allow us to model an entire domain. We can fuse knowledge graphs together from different domains and then they will be more comprehensive and more amenable to being continuously updated. They can allow us to see known and even discover unknown relationships between different combinations of concepts as diverse as diseases, clinical findings, and drugs. This comprehensive graph-based connected approach means that the reasoning capabilities of knowledge graphs will much more closely emulate the thinking of the physician. Today we can no longer see the patient before us as a single disease entity. We have to think about the patient in their full context; not only the multiple diseases and comorbidities which may be at play but the social factors and social determinants of health and support structures that can impact the patient and the care process as well. In fact, even payment reform may influence what we do and how we do it and ideally incentivize more of the right things and fewer of the wrong things and at the same provide continuous feedback to the clinician. The aim is that the clinician end-user has a delightful experience using a knowledge graph-based augmented clinical reasoning system and that they will be in a continuous learning mode as a result of receiving continuous feedback. If we do this for each and every doctor, we will be truly enabling a learning health system. The learning health system in this context is essentially the idea that we learn from real-world evidence as well as clinical guidelines. We model knowledge and make it available in computable form. We then deliver that to the point of care and observe, monitor, and update to improve the knowledge assets as they are used – enabling a virtuous learning cycle. User experience of a knowledge graph-based augmented clinical reasoning system is also important. Sometimes we aim too low in our goals for user experience in decision support – we plan for the software to be usable or acceptable. But with knowledge graph-based augmented clinical reasoning, we should be aiming to make the physician’s experience delightful – so that it is simple and fun for them to do the right thing and to support continuous learning in practice. In the same way that we can project patient data against the knowledge graph, it is also possible to project clinician experience and training data. The linked data and shared ontology approach allow us to improve user experience in a more powerful way.
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Why the BMJ Knowledge Graph
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